Specifies the number of studies evaluated orselected
Steps, and targets of constructing a good review article are listed in Table 3 . To write a good review article the items in Table 3 should be implemented step by step. [ 11 – 13 ]
Steps of a systematic review
Formulation of researchable questions | Select answerable questions |
Disclosure of studies | Databases, and key words |
Evaluation of its quality | Quality criteria during selection of studies |
Synthesis | Methods interpretation, and synthesis of outcomes |
It might be helpful to divide the research question into components. The most prevalently used format for questions related to the treatment is PICO (P - Patient, Problem or Population; I-Intervention; C-appropriate Comparisons, and O-Outcome measures) procedure. For example In female patients (P) with stress urinary incontinence, comparisons (C) between transobturator, and retropubic midurethral tension-free band surgery (I) as for patients’ satisfaction (O).
In a systematic review on a focused question, methods of investigation used should be clearly specified.
Ideally, research methods, investigated databases, and key words should be described in the final report. Different databases are used dependent on the topic analyzed. In most of the clinical topics, Medline should be surveyed. However searching through Embase and CINAHL can be also appropriate.
While determining appropriate terms for surveying, PICO elements of the issue to be sought may guide the process. Since in general we are interested in more than one outcome, P, and I can be key elements. In this case we should think about synonyms of P, and I elements, and combine them with a conjunction AND.
One method which might alleviate the workload of surveying process is “methodological filter” which aims to find the best investigation method for each research question. A good example of this method can be found in PubMed interface of Medline. The Clinical Queries tool offers empirically developed filters for five different inquiries as guidelines for etiology, diagnosis, treatment, prognosis or clinical prediction.
As an indispensable component of the review process is to discriminate good, and bad quality researches from each other, and the outcomes should be based on better qualified researches, as far as possible. To achieve this goal you should know the best possible evidence for each type of question The first component of the quality is its general planning/design of the study. General planning/design of a cohort study, a case series or normal study demonstrates variations.
A hierarchy of evidence for different research questions is presented in Table 4 . However this hierarchy is only a first step. After you find good quality research articles, you won’t need to read all the rest of other articles which saves you tons of time. [ 14 ]
Determination of levels of evidence based on the type of the research question
I | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies | Systematic review of Level II studies |
II | Randomized controlled study | Crross-sectional study in consecutive patients | Initial cohort study | Prospective cohort study |
III | One of the following: Non-randomized experimental study (ie. controlled pre-, and post-test intervention study) Comparative studies with concurrent control groups (observational study) (ie. cohort study, case-control study) | One of the following: Cross-sectional study in non-consecutive case series; diagnostic case-control study | One of the following: Untreated control group patients in a randomized controlled study, integrated cohort study | One of the following: Retrospective cohort study, case-control study (Note: these are most prevalently used types of etiological studies; for other alternatives, and interventional studies see Level III |
IV | Case series | Case series | Case series or cohort studies with patients at different stages of their disease states |
Rarely all researches arrive at the same conclusion. In this case a solution should be found. However it is risky to make a decision based on the votes of absolute majority. Indeed, a well-performed large scale study, and a weakly designed one are weighed on the same scale. Therefore, ideally a meta-analysis should be performed to solve apparent differences. Ideally, first of all, one should be focused on the largest, and higher quality study, then other studies should be compared with this basic study.
In conclusion, during writing process of a review article, the procedures to be achieved can be indicated as follows: 1) Get rid of fixed ideas, and obsessions from your head, and view the subject from a large perspective. 2) Research articles in the literature should be approached with a methodological, and critical attitude and 3) finally data should be explained in an attractive way.
Discover the world's research
To read the full-text of this research, you can request a copy directly from the authors.
Advertisement
1 Altmetric
Due to the absence of definitive treatment for Alzheimer’s disease (AD), slowing its development is essential. Accurately predicting the conversion of mild cognitive impairment (MCI) -a potential early stage of AD- to AD is challenging due to the subtle distinctions between individuals who will develop AD and those who will not. As an increasing body of evidence in the literature suggests, advanced magnetic resonance imaging (MRI) scans, coupled with high-performance computing techniques and novel deep learning techniques, have revolutionized the ability to predict MCI to AD conversion. This study systematically reviewed the publications from 2013 to 2023 (July) to investigate the contribution of deep learning in predicting the MCI conversion to AD, concentrating on the MRI data (structural or functional) and clinical information. The search conducted across seven different databases yielded a total of 2273 studies. Out of these, 78 relevant studies were included, which were thoroughly reviewed, and their essential details and findings were extracted. Furthermore, this study comprehensively explores the challenges associated with predicting the conversion from MCI to AD using deep learning methods with MRI data. Also, it identifies potential solutions to address these challenges. The research field of predicting MCI to AD conversion from MRI data using deep learning techniques is constantly evolving. There is an increasing focus on employing explainable approaches to improve transparency in the analysis process. The paper concludes with an overview of future perspectives and recommends conducting further studies in MCI to AD conversion prediction using deep learning methods.
This is a preview of subscription content, log in via an institution to check access.
Subscribe and save.
Price includes VAT (Russian Federation)
Instant access to the full article PDF.
Rent this article via DeepDyve
Institutional subscriptions
Yiannopoulou KG, Papageorgiou SG (2020) Current and future treatments in Alzheimer disease: an update. Journal of central nervous system disease 12:1179573520907397
Article Google Scholar
Mohammadian F, Zare Sadeghi A, Noroozian M, Malekian V, Abbasi Sisara M, Hashemi H, Mobarak Salari H, Valizadeh G, Samadi F, Sodaei F (2023) Quantitative assessment of resting-state functional connectivity MRI to differentiate amnestic mild cognitive impairment, late-onset Alzheimer’s disease from normal subjects. J Magn Reson Imaging 57(6):1702–1712
Mohammadian F, Noroozian M, Sadeghi AZ, Malekian V, Saffar A, Talebi M, Hashemi H, Mobarak Salari H, Samadi F, Sodaei F (2023) Effective connectivity evaluation of resting-state brain networks in Alzheimer’s disease, amnestic mild cognitive impairment, and normal aging: an exploratory study. Brain Sci 13(2):265
Illakiya T, Karthik R, Siddharth M, Mishra R, Udainiya A (2023) AHANet: adaptive hybrid attention network for Alzheimer’s disease classification using brain magnetic resonance imaging. Bioengineering 10(6):714
Hu C, Ju R, Shen Y, Zhou P, Li Q (2016) Clinical decision support for Alzheimer’s disease based on deep learning and brain network. In: 2016 IEEE international conference on communications (ICC). IEEE, pp 1–6
de la Torre JC (2010) Alzheimer’s disease is incurable but preventable. J Alzheimer’s Dis 20(3):861–870
De Strooper B, Karran E (2016) The cellular phase of Alzheimer’s disease. Cell 164(4):603–615
Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, Mehmood Z (2020) A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. J Med Syst 44:1–16
World Health Organization (2021) Global status report on the public health response to dementia
Zhao Z, Chuah JH, Lai KW, Chow C-O, Gochoo M, Dhanalakshmi S, Wang N, Bao W, Wu X (2023) Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: a review. Front Comput Neurosci 17:10
Patterson C (2018) World Alzheimer Report 2018. The State of the Art of Dementia Research. New Frontiers.
Christina P (2018) The state of the art of dementia research: new frontiers (ADI). Alzheimer’s Disease International, London
Google Scholar
Bahar-Fuchs A, Clare L, Woods B (2013) Cognitive training and cognitive rehabilitation for persons with mild to moderate dementia of the Alzheimer’s or vascular type: a review. Alzheimer’s Res Ther 5:1–14
Lee G, Nho K, Kang B, Sohn KA, Kim D (2019) Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Sci Rep 9(1):1952
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E (1999) Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 56(3):303–308
Reisberg B, Ferris SH, Kluger A, Franssen E, Wegiel J, De Leon MJ (2008) Mild cognitive impairment (MCI): a historical perspective. Int Psychogeriatr 20(1):18–31
Bolourchi P, Gholami M, Moradi M, Beheshti I, Demirel H (2023) MCI Conversion prediction using 3D zernike moments and the improved dynamic particle swarm optimization algorithm. Appl Sci 13(7):4489
Tábuas-Pereira M, Baldeiras I, Duro D, Santiago B, Ribeiro MH, Leitão MJ, Oliveira C, Santana I (2016) Prognosis of early-onset vs. late-onset mild cognitive impairment: comparison of conversion rates and its predictors. Geriatrics 1(2):11
Er F, Goularas D (2020) Predicting the prognosis of MCI patients using longitudinal MRI data. IEEE/ACM Trans Comput Biol Bioinf 18(3):1164–1173
As A (2019) 2019 Alzheimer’s disease facts and figures. Alzheimer’s Dement 15(3):321–387
Lowndes G, Savage G (2007) Early detection of memory impairment in Alzheimer’s disease: a neurocognitive perspective on assessment. Neuropsychol Rev 17:193–202
Beach TG, Monsell SE, Phillips LE, Kukull W (2012) Accuracy of the clinical diagnosis of Alzheimer disease at national institute on aging Alzheimer disease centers, 2005–2010. J Neuropathol Exp Neurol 71(4):266–273
Huang Y, Xu J, Zhou Y, Tong T, Zhuang X, AsDN I (2019) Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Front Neurosci 13:509
Acharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MKM, Tanik UJ, Rajinikanth V, Yeong CH (2019) Automated detection of Alzheimer’s disease using brain MRI images–a study with various feature extraction techniques. J Med Syst 43:1–14
Zhao YX, Zhang YM, Song M, Liu CL (2021) Region ensemble network for MCI conversion prediction with a relation regularized loss. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th international conference, Strasbourg, September 27–October 1, 2021, Proceedings, Part V 24, 2021. Springer, pp 185-194
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 7(3):263–269
Moscoso A, Silva-Rodríguez J, Aldrey JM, Cortés J, Fernández-Ferreiro A, Gómez-Lado N, Ruibal Á, Aguiar P, AsDN I (2019) Prediction of Alzheimer’s disease dementia with MRI beyond the short-term: implications for the design of predictive models. NeuroImag Clin 23:101837
Zhao Y, Ma B, Jiang P, Zeng D, Wang X, Li S (2020) Prediction of Alzheimer’s disease progression with multi-information generative adversarial network. IEEE J Biomed Health Inform 25(3):711–719
Marcus C, Mena E, Subramaniam RM (2014) Brain PET in the diagnosis of Alzheimer’s disease. Clin Nucl Med 39(10):e413
Odusami M, Maskeliūnas R, Damaševičius R, Misra S (2023) Explainable deep-learning-based diagnosis of Alzheimer’s disease using multimodal input fusion of PET and MRI images. J Med Biol Eng 79:1–12
Ren F, Yang C, Nanehkaran Y (2023) MRI-based model for MCI conversion using deep zero-shot transfer learning. J Supercomput 79(2):1182–1200
Hill DL, Schwarz AJ, Isaac M, Pani L, Vamvakas S, Hemmings R, Carrillo MC, Yu P, Sun J, Beckett L (2014) Coalition against major diseases/European medicines agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer’s disease. Alzheimer’s Dement 10(4):421–429
Killiany R, Hyman B, Gomez-Isla T, Moss M, Kikinis R, Jolesz F, Tanzi R, Jones K, Albert M (2002) MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology 58(8):1188–1196
Du A, Schuff N, Amend D, Laakso M, Hsu Y, Jagust W, Yaffe K, Kramer J, Reed B, Norman D (2001) Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry 71(4):441–447
Devanand D, Pradhaban G, Liu X, Khandji A, De Santi S, Segal S, Rusinek H, Pelton G, Honig L, Mayeux R (2007) Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology 68(11):828–836
Luo M, He Z, Cui H, Chen Y-PP, Ward P, AsDN I (2023) Class activation attention transfer neural networks for MCI conversion prediction. Comput Biol Med 156:106700
Liu M, Zhang J, Adeli E, Shen D (2018) Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng 66(5):1195–1206
Hett K, Ta V-T, Oguz I, Manjón JV, Coupé P, AsDN I (2021) Multi-scale graph-based grading for Alzheimer’s disease prediction. Med Image Anal 67:101850
Park S, Hong CH, Lee D-g, Park K, Shin H, AsDN I (2023) Prospective classification of Alzheimer’s disease conversion from mild cognitive impairment. Neural Netw 164:335–344
Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248
Basaia S, Agosta F, Wagner L, Canu E, Magnani G, Santangelo R, Filippi M, Initiative AsDN (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImag Clin 21:101645
Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 155:530–548
Khojaste-Sarakhsi M, Haghighi SS, Ghomi SF, Marchiori E (2022) Deep learning for Alzheimer’s disease diagnosis: a survey. Artif Intell Med 130:102332
Zhou Q, Wang J, Yu X, Wang S, Zhang Y (2023) A survey of deep learning for Alzheimer’s disease. Mach Learn Knowl Extr 5(2):611–668
Fathi S, Ahmadi M, Dehnad A (2022) Early diagnosis of Alzheimer’s disease based on deep learning: a systematic review. Comput Biol Med 146:105634
Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220
Niyas KM, Thiyagarajan P (2023) A systematic review on early prediction of mild cognitive impairment to alzheimers using machine learning algorithms. Int J Intell Netw 4:74–88
Grueso S, Viejo-Sobera R (2021) Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review. Alzheimer’s Res Ther 13:1–29
Arya AD, Verma SS, Chakarabarti P, Chakrabarti T, Elngar AA, Kamali A-M, Nami M (2023) A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease. Brain Inf 10(1):1–15
Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D’Arcy RC, Song X (2022) Artificial intelligence in brain MRI analysis of Alzheimer’s disease over the past 12 years: a systematic review. Ageing Res Rev 77:101614
Zhao X, Ang CKE, Acharya UR, Cheong KH (2021) Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 41(2):456–473
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906
Suk HI, Shen D (2013) Deep learning-based feature representation for AD/MCI classification. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, 22-26 September 2013, Proceedings, Part II 16, 2013. Springer, pp 583-590
Suk HI, Lee SW, Shen D, AsDN I (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:569–582
Suk HI, Lee S-W, Shen D, AsDN I (2015) Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 220:841–859
Suk HI, Lee SW, Shen D, AsDN I (2016) Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis. Brain Struct Funct 221:2569–2587
Suk H-I, Lee S-W, Shen D, AsDN I (2017) Deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal 37:101–113
Çitak-ER F, Goularas D, Ormeci B (2017) A novel convolutional neural network model based on voxel-based morphometry of imaging data in predicting the prognosis of patients with mild cognitive impairment. J Neurol Sci 34(1):52–69
Shi J, Zheng X, Li Y, Zhang Q, Ying S (2017) Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J Biomed Health Inform 22(1):173–183
Lu D, Popuri K, Ding GW, Balachandar R, Beg MF (2018) Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci Rep 8(1):5697
Wang X, Cai W, Shen D, Huang H (2018) Temporal correlation structure learning for MCI conversion prediction. In: Medical image computing and computer assisted intervention–MICCAI 2018: 21st international conference, Granada, 16-20 September 2018, Proceedings, Part III, 2018. Springer, pp 446-454
Liu M, Zhang J, Adeli E, Shen D (2018) Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43:157–168
Wu C, Guo S, Hong Y, Xiao B, Wu Y, Zhang Q (2018) Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant Imaging Med Surg 8(10):992–1003. https://doi.org/10.21037/qims.2018.10.17
Lin W, Tong T, Gao Q, Guo D, Du X, Yang Y, Guo G, Xiao M, Du M, Qu X (2018) Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front Neurosci 12:777
Shmulev Y, Belyaev M, Initiative AsDN ( 2018) Predicting conversion of mild cognitive impairments to Alzheimer’s disease and exploring impact of neuroimaging. In: Graphs in biomedical image analysis and integrating medical imaging and non-imaging modalities: second international workshop, GRAIL 2018 and first international workshop, beyond MIC 2018, held in conjunction with MICCAI 2018, Granada, 20 September 2018, Proceedings 2, 2018. Springer, pp 83-91
Lian C, Liu M, Zhang J, Shen D (2018) Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell 42(4):880–893
Lee E, Choi J-S, Kim M, Suk H-I, AsDN I (2019) Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning. Neuroimage 202:116113
Cui R, Liu M (2018) Hippocampus analysis by combination of 3-d densenet and shapes for alzheimer’s disease diagnosis. IEEE J Biomed Health Inform 23(5):2099–2107
Li F, Liu M, AsDN I (2019) A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer’s disease. J Neurosci Methods 323:108–118
Oh K, Chung YC, Kim KW, Kim WS, Oh IS (2019) Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci Rep 9(1):1–16
Martinez-Murcia FJ, Ortiz A, Gorriz JM, Ramirez J, Castillo-Barnes D (2019) Studying the manifold structure of Alzheimer’s disease: a deep learning approach using convolutional autoencoders. IEEE J Biomed Health Inform 24(1):17–26
Li H, Habes M, Wolk DA, Fan Y, AsDN I (2019) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer’s Dement 15(8):1059–1070
Spasov S, Passamonti L, Duggento A, Liò P, Toschi N (2019) A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. Neuroimage 189:276–287. https://doi.org/10.1016/j.neuroimage.2019.01.031
Cui R, Liu M, AsDN I (2019) RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imaging Graph 73:1–10
Rana SS, Ma X, Pang W, Wolverson E (2020) A multi-modal deep learning approach to the early prediction of mild cognitive impairment conversion to Alzheimer’s disease. 2020 IEEE/ACM international conference on big data computing, applications and technologies (BDCAT). IEEE, New York, pp 9–18
Chapter Google Scholar
Mukhtar G, Farhan S (2020) Convolutional neural network based prediction of conversion from mild cognitive impairment to Alzheimer’s disease: a technique using hippocampus extracted from MRI. Adv Electr Comput Eng 20(2):113–122
Ramon-Julvez U, Hernandez M, Mayordomo E (2020) Adni analysis of the influence of diffeomorphic normalization in the prediction of stable vs progressive MCI conversion with convolutional neural networks. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI), 3–7 April 2020. pp 1120–1124. https://doi.org/10.1109/ISBI45749.2020.9098445
Abrol A, Bhattarai M, Fedorov A, Du Y, Plis S, Calhoun V, AsDN I (2020) Deep residual learning for neuroimaging: an application to predict progression to Alzheimer’s disease. J Neurosci Methods 339:108701
Pan Y, Liu M, Lian C, Xia Y, Shen D (2020) Spatially-constrained fisher representation for brain disease identification with incomplete multi-modal neuroimages. IEEE Trans Med Imaging 39(9):2965–2975
Gao F, Yoon H, Xu Y, Goradia D, Luo J, Wu T, Su Y, Initiative AsDN (2020) AD-NET: age-adjust neural network for improved MCI to AD conversion prediction. NeuroImag Clin 27:102290
Nanni L, Interlenghi M, Brahnam S, Salvatore C, Papa S, Nemni R, Castiglioni I, AsDN I (2020) Comparison of transfer learning and conventional machine learning applied to structural brain MRI for the early diagnosis and prognosis of Alzheimer’s disease. Front Neurol 11:576194
Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X (2020) Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci 14:259
Lian C, Liu M, Pan Y, Shen D (2020) Attention-guided hybrid network for dementia diagnosis with structural MR images. IEEE transactions on cybernetics 52(4):1992–2003
Li A, Li F, Elahifasaee F, Liu M, Zhang L, Initiative AsDN (2021) Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Brain Imag Behav 15:1–10
Bae J, Stocks J, Heywood A, Jung Y, Jenkins L, Hill V, Katsaggelos A, Popuri K, Rosen H, Beg MF (2021) Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer’s type based on a three-dimensional convolutional neural network. Neurobiol Aging 99:53–64
Zhang X, Han L, Zhu W, Sun L, Zhang D (2021) An explainable 3D residual self-attention deep neural network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE J Biomed Health Inform 26(11):5289–5297
Ocasio E, Duong TQ (2021) Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI. PeerJ Comput Sci 7:e560. https://doi.org/10.7717/peerj-cs.560
Chen Y, Xia Y (2021) Iterative sparse and deep learning for accurate diagnosis of Alzheimer’s disease. Pattern Recogn 116:107944. https://doi.org/10.1016/j.patcog.2021.107944
Zhang J, Zheng B, Gao A, Feng X, Liang D, Long X (2021) A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer’s disease classification. Magn Reson Imaging 78:119–126
Guan H, Wang C, Cheng J, Jing J, Liu T (2022) A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer’s disease. Hum Brain Mapp 43(2):760–772
Er F, Goularas D (2021) Predicting the prognosis of MCI patients using longitudinal MRI data. IEEE/ACM Trans Comput Biol Bioinf 18(3):1164–1173. https://doi.org/10.1109/TCBB.2020.3017872
Guan H, Wang C, Tao D (2021) MRI-based Alzheimer’s disease prediction via distilling the knowledge in multi-modal data. Neuroimage 244:118586. https://doi.org/10.1016/j.neuroimage.2021.118586
Kang W, Lin L, Zhang B, Shen X, Wu S (2021) Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer’s disease diagnosis. Comput Biol Med 136:104678. https://doi.org/10.1016/j.compbiomed.2021.104678
Alinsaif S, Lang J, AsDN I (2021) 3D shearlet-based descriptors combined with deep features for the classification of Alzheimer’s disease based on MRI data. Comput Biol Med 138:104879
Zhu W, Sun L, Huang J, Han L, Zhang D (2021) Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI. IEEE Trans Med Imaging 40(9):2354–2366
Bron EE, Klein S, Papma JM, Jiskoot LC, Venkatraghavan V, Linders J, Aalten P, De Deyn PP, Biessels GJ, Claassen JA (2021) Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease. NeuroImag Clin 31:102712
Guan H, Liu Y, Yang E, Yap PT, Shen D, Liu M (2021) Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification. Med Image Anal 71:102076
Gao X, Shi F, Shen D, Liu M (2021) Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in alzheimer’s disease. IEEE J Biomed Health Inform 26(1):36–43
Zhang P, Lin S, Qiao J, Tu Y (2021) Diagnosis of Alzheimer’s disease with ensemble learning classifier and 3D convolutional neural network. Sensors 21(22):7634
Yang L, Wang X, Guo Q, Gladstein S, Wooten D, Li T, Robieson WZ, Sun Y, Huang X, AsDN I (2021) Deep learning based multimodal progression modeling for Alzheimer’s disease. Stat Biopharm Res 13(3):337–343
Zheng G, Zhang Y, Zhao Z, Wang Y, Liu X, Shang Y, Cong Z, Dimitriadis SI, Yao Z, Hu B (2022) A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment. Methods 204:241–248
Ghafoori S, Shalbaf A (2022) Predicting conversion from MCI to AD by integration of rs-fMRI and clinical information using 3D-convolutional neural network. Int J Comput Assist Radiol Surg 17(7):1245–1255
Ashtari-Majlan M, Seifi A, Dehshibi MM (2022) A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer’s disease using structural MRI images. IEEE J Biomed Health Inform 26(8):3918–3926
Lu P, Hu L, Zhang N, Liang H, Tian T, Lu L (2022) A two-stage model for predicting mild cognitive impairment to Alzheimer’s disease conversion. Front Aging Neurosci 14:826622
Zhang F, Pan B, Shao P, Liu P, Shen S, Yao P, Xu RX, AsDN I (2022) A single model deep learning approach for Alzheimer’s disease diagnosis. Neuroscience 491:200–214
Kwak K, Niethammer M, Giovanello KS, Styner M, Dayan E, AsDN I (2022) Differential role for hippocampal subfields in Alzheimer’s disease progression revealed with deep learning. Cereb Cortex 32(3):467–478
Zhang S, Chen X, Ren B, Yang H, Yu Z, Zhang XY, Zhou Y (2022) 3D Global Fourier Network for Alzheimer’s Disease Diagnosis Using Structural MRI. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, 18–22 September 2022, Proceedings, Part I. Springer, pp 34-43
Chen L, Qiao H, Zhu F (2022) Alzheimer’s disease diagnosis with brain structural MRI using multiview-slice attention and 3D convolution neural network. Front Aging Neurosci 14:871706
Oh K, Yoon JS, Suk H-I (2022) Learn-explain-reinforce: counterfactual reasoning and its guidance to reinforce an Alzheimer’s Disease diagnosis model. IEEE Trans Pattern Anal Mach Intell 45(4):4843–4857
Fan CC, Peng L, Wang T, Yang H, Zhou XH, Ni ZL, Chen S, Zhou YJ, Hou ZG (2022) TR-Gan: multi-session future MRI prediction with temporal recurrent generative adversarial Network. IEEE Trans Med Imaging 41(8):1925–1937
Sun H, Wang A, He S (2022) Temporal and spatial analysis of alzheimer’s disease based on an improved convolutional neural network and a resting-state FMRI brain functional network. Int J Environ Res Public Health 19(8):4508
Lao H, Zhang X (2022) Diagnose Alzheimer’s disease by combining 3D discrete wavelet transform and 3D moment invariants. IET Image Proc 16(14):3948–3964
Han K, He M, Yang F, Zhang Y (2022) Multi-task multi-level feature adversarial network for joint Alzheimer’s disease diagnosis and atrophy localization using sMRI. Phys Med Biol 67(8):085002
Li M, Jiang Y, Li X, Yin S, Luo H (2023) Ensemble of convolutional neural networks and multilayer perceptron for the diagnosis of mild cognitive impairment and Alzheimer’s disease. Med Phys 50(1):209–225
Guan H, Yue L, Yap P-T, Xiao S, Bozoki A, Liu M (2023) Attention-guided autoencoder for automated progression prediction of subjective cognitive decline with structural MRI. IEEE J Biomed Health Inf. https://doi.org/10.1109/JBHI.2023.3257081
Zhao Q, Huang G, Xu P, Chen Z, Li W, Yuan X, Zhong G, Pun C-M, Huang Z (2023) IDA-Net: inheritable deformable attention network of structural MRI for Alzheimer’s disease diagnosis. Biomed Signal Process Control 84:104787
Guan X, Ma L, Huang Y, Tang S, Li T (2023) An interpretable brain network atlas-based hybrid model for mild cognitive impairment progression prediction. In: Proceedings of the 2023 2nd Asia conference on algorithms, computing and machine learning. pp 424–428
Hu Z, Wang Z, Jin Y, Hou W (2023) VGG-TSwinformer: transformer-based deep learning model for early Alzheimer’s disease prediction. Comput Methods Programs Biomed 229:107291
Francis A, Pandian IA (2023) Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging. TELKOMNIKA (Telecommun Comput Electr Control) 21(2):374–381
Cao G, Zhang M, Wang Y, Zhang J, Han Y, Xu X, Huang J, Kang G (2023) End-to-end automatic pathology localization for Alzheimer’s disease diagnosis using structural MRI. Comput Biol Med 163:107110
Liu F, Wang H, Liang SN, Jin Z, Wei S, Li X, AsDN I (2023) MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer’s disease prediction with structural MRI. Comput Biol Med 157:106790
Gao X, Cai H, Liu M (2023) A Hybrid multi-scale attention convolution and aging transformer network for Alzheimer’s disease diagnosis. IEEE J Biomed Health Inf 27(7):3292–3301
Zheng B, Gao A, Huang X, Li Y, Liang D, Long X (2023) A modified 3D EfficientNet for the classification of Alzheimer’s disease using structural magnetic resonance images. IET Image Proc 17(1):77–87
Hoang GM, Kim UH, Kim JG (2023) Vision transformers for the prediction of mild cognitive impairment to Alzheimer’s disease progression using mid-sagittal sMRI. Front Aging Neurosci 15:1102869
Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L (2005) The Alzheimer’s disease neuroimaging initiative. Neuroimag Clin 15(4):869–877
Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, Thomas RG, Walter S, Trojanowski JQ, Shaw LM, Beckett LA (2010) Clinical core of the Alzheimer’s disease neuroimaging initiative: progress and plans. Alzheimer’s Dement 6(3):239–246
Jack CR Jr, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C (2015) Magnetic resonance imaging in Alzheimer’s disease neuroimaging initiative 2. Alzheimer’s Dement 11(7):740–756
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR Jr, Jagust W, Morris JC (2017) The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimer’s Dement 13(5):561–571
Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, Lautenschlager NT, Lenzo N, Martins RN, Maruff P (2009) The Australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr 21(4):672–687
Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17(1):87–97
Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320
Kalavathi P, Prasath VS (2016) Methods on skull stripping of MRI head scan images—a review. J Digit Imaging 29:365–379
Liu Y, Dawant BM (2015) Automatic localization of the anterior commissure, posterior commissure, and midsagittal plane in MRI scans using regression forests. IEEE J Biomed Health Inform 19(4):1362–1374
Dora L, Agrawal S, Panda R, Abraham A (2017) State-of-the-art methods for brain tissue segmentation: a review. IEEE Rev Biomed Eng 10:235–249
Rana S, Ma X, Pang W, Wolverson E (2020) A multi-modal deep learning approach to the early prediction of mild cognitive impairment conversion to Alzheimer’s disease. 2020 IEEE/ACM international conference on big data computing, applications and technologies (BDCAT). IEEE, New York
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge
Bengio Y, Goodfellow I, Courville A (2017) Deep learning. MIT press, Cambridge
Larochelle H, Bengio Y, Louradour J, Lamblin P (2009) Exploring strategies for training deep neural networks. J Mach Learn Res 10(1):1–40
Livni R, Shalev-Shwartz S, Shamir O (2013) An algorithm for training polynomial networks. arXiv preprint arXiv:13047045
Medsker LR, Jain L (2001) Recurrent neural networks. Design Appl 5(64–67):2
Salakhutdinov R, Hinton G Deep boltzmann machines. In: Artificial intelligence and statistics, 2009. PMLR, pp 448–455
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144
Article MathSciNet Google Scholar
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv1706.03762
Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53:5455–5516
Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B (2020) 3D deep learning on medical images: a review. Sensors 20(18):5097
Mofrad FB, Valizadeh G (2023) DenseNet-based transfer learning for LV shape classification: introducing a novel information fusion and data augmentation using statistical shape/color modeling. Expert Syst Appl 213:119261
Valizadeh G, Mofrad FB (2023) Parametrized pre-trained network (PPNet): a novel shape classification method using SPHARMs for MI detection. Expert Syst Appl 228:120368
Lin CJ, Jeng SY, Chen MK (2020) Using 2D CNN with Taguchi parametric optimization for lung cancer recognition from CT images. Appl Sci 10(7):2591
Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR (2019) Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Comput Biol Med 113:103387
Sultana A, Nahiduzzaman M, Bakchy SC, Shahriar SM, Peyal HI, Chowdhury ME, Khandakar A, Arselene Ayari M, Ahsan M, Haider J (2023) A real time method for distinguishing COVID-19 utilizing 2D-CNN and transfer learning. Sensors 23(9):4458
Pereira M, Fantini I, Lotufo R, Rittner L (2020) An extended-2D CNN for multiclass Alzheimer’s disease diagnosis through structural MRI. Medical imaging 2020: computer-aided diagnosis. SPIE, Bellingham, pp 438–444
Müller MJ, Greverus D, Dellani PR, Weibrich C, Wille PR, Scheurich A, Stoeter P, Fellgiebel A (2005) Functional implications of hippocampal volume and diffusivity in mild cognitive impairment. Neuroimage 28(4):1033–1042
Thompson PM, Hayashi KM, De Zubicaray GI, Janke AL, Rose SE, Semple J, Hong MS, Herman DH, Gravano D, Doddrell DM (2004) Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 22(4):1754–1766
Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Toga AW, Jack CR Jr, Schuff N (2009) Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Neuroimage 45(1):S3–S15
Wang S-H, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42:1–11
Chen T, Kornblith S, Norouzi M, Hinton GA (2020) simple framework for contrastive learning of visual representations. International conference on machine learning. PMLR, Seattle, pp 1597–1607
He K, Fan H, Wu Y, Xie S, Girshick R 2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, IEEE, New York, pp 9729–9738
Li K, O’Brien R, Lutz M, Luo S, AsDN I (2018) A prognostic model of Alzheimer’s disease relying on multiple longitudinal measures and time-to-event data. Alzheimer’s Dement 14(5):644–651
Barnes DE, Cenzer IS, Yaffe K, Ritchie CS, Lee SJ, AsDN I (2014) A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer’s disease. Alzheimer’s Dement 10(6):646–655
Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan Y, Murali Doraiswamy P, Zhu H, AsDN I (2015) Predicting Alzheimer’s disease using combined imaging-whole genome SNP data. J Alzheimer’s Dis 46(3):695–702
Li S, Okonkwo O, Albert M, Wang M-C (2013) Variation in variables that predict progression from MCI to AD dementia over duration of follow-up. Am J Alzheimer’s Dis 2(1):12
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536
Ranzato MA, Poultney C, Chopra S, Cun Y (2006) Efficient learning of sparse representations with an energy-based model. Adv Neural Inf Process Syst 19:1137
Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning (ICML 2008), ACM (Association for Computing Machinery), New York, pp 1096–1103
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:13126114
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of Alzheimer’s disease with deep learning. 2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, New York, pp 1015–1018
Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ (2014) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng 62(4):1132–1140
Hosseini-Asl E, Gimel’farb G, El-Baz A (2016) Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv preprint arXiv:160700556
Kruthika K, Maheshappa H, AsDN I (2019) CBIR system using capsule networks and 3D CNN for Alzheimer’s disease diagnosis. Inf Med Unlocked 14:59–68
Vu TD, Ho NH, Yang HJ, Kim J, Song HC (2018) Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection. Soft Comput 22:6825–6833
Zheng X, Shi J, Li Y, Liu X, Zhang Q (2016) Multi-modality stacked deep polynomial network based feature learning for Alzheimer’s disease diagnosis. 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, New York, pp 851–854
Shi J, Zhou S, Liu X, Zhang Q, Lu M, Wang T (2016) Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing 194:87–94
Shen L, Shi J, Dong Y, Ying S, Peng Y, Chen L, Zhang Q, An H, Zhang Y (2020) An improved deep polynomial network algorithm for transcranial sonography–based diagnosis of parkinson’s disease. Cogn Comput 12:553–562
Lei B, Yang M, Yang P, Zhou F, Hou W, Zou W, Li X, Wang T, Xiao X, Wang S (2020) Deep and joint learning of longitudinal data for Alzheimer’s disease prediction. Pattern Recogn 102:107247
Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12:229–244
Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. Classification in BioApps: automation of decision making. Springer, Berlin, pp 323–350
Book Google Scholar
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078
Cheng D, Liu M (2017) Combining convolutional and recurrent neural networks for Alzheimer’s disease diagnosis using PET images. 2017 IEEE international conference on imaging systems and techniques (IST). IEEE, New York, pp 1–5
Cui R, Liu M, Li G (2018) Longitudinal analysis for Alzheimer’s disease diagnosis using RNN. 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, New York, pp 1398–1401
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Hinton GE (2012) A practical guide to training restricted Boltzmann machines. Neural networks: tricks of the trade, 2nd edn. Springer, Berlin, pp 599–619
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127
Mohamed AR, Dahl GE, Hinton G (2011) Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process 20(1):14–22
Zhou T, Li Q, Lu H, Cheng Q, Zhang X (2023) GAN review: models and medical image fusion applications. Information Fusion 91:134–148
Han C, Hayashi H, Rundo L, Araki R, Shimoda W, Muramatsu S, Furukawa Y, Mauri G, Nakayama H (2018) GAN-based synthetic brain MR image generation. 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, New York, pp 734–738
Tanner C, Ozdemir F, Profanter R, Vishnevsky V, Konukoglu E, Goksel O (2018) Generative adversarial networks for MR-CT deformable image registration. arXiv preprint arXiv:180707349
Tavse S, Varadarajan V, Bachute M, Gite S, Kotecha K (2022) A Systematic literature review on applications of GAN-synthesized images for brain MRI. Future Internet 14(12):351
Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM computing surveys (CSUR) 54(10s):1–41
Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min knowl Manag Process 5(2):1
Powers DM (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:201016061
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45(4):427–437
Valizadeh G, Babapour Mofrad F (2022) A comprehensive survey on two and three-dimensional fourier shape descriptors: biomedical applications. Arch Comput Methods Eng 29(7):4643–4681
Suk HI, Shen D (2013) Deep learning-based feature representation for AD/MCI classification. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention MICCAI 2013. Springer, Berlin, pp 583–590
Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. 2010 20th international conference on pattern recognition. IEEE, New York, pp 3121–3124
Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) Protein Struct 405(2):442–451
Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16(5):412–424
Schuckers ME, Schuckers ME (2010) Receiver operating characteristic curve and equal error rate. Computational methods in biometric authentication: statistical methods for performance evaluation. Springer, Berlin, pp 155–204
Collobert R, Van Der Maaten L, Joulin A (2016) Torchnet: an open-source platform for (deep) learning research. In: Proceedings of the 33rd International Conference on Machine Learning (ICML-2016). ACM, New York, NY, pp 19–24
Chollet F (2018) Keras: the python deep learning library. Astrophys Source Code Library:ascl 1806:1022
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M {TensorFlow}: a system for {Large-Scale} machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), 2016. pp 265–283
MathWorks MATLAB. https://www.mathworks.com . Accessed on 2024
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T Caffe (2014) Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACSM international conference on Multimedia. pp 675–678
Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, Bastien F, Bayer J, Belikov A, Belopolsky A (2016) Theano: a python framework for fast computation of mathematical expressions. arXiv e-prints:arXiv: 1605.02688
Cheng B, Zhu B, Pu S (2022) Multi-auxiliary domain transfer learning for diagnosis of MCI conversion. Neurol Sci 1:1–19
Cheng B, Liu M, Zhang D, Munsell BC, Shen D (2015) Domain transfer learning for MCI conversion prediction. IEEE Trans Biomed Eng 62(7):1805–1817
Singh A, Sengupta S, Lakshminarayanan V (2020) Explainable deep learning models in medical image analysis. J Imaging 6(6):52
Lombardi A, Diacono D, Amoroso N, Monaco A, Tavares JMR, Bellotti R, Tangaro S (2021) Explainable deep learning for personalized age prediction with brain morphology. Front Neurosci 15:578
de Vries BM, Zwezerijnen GJ, Burchell GL, van Velden FH, Boellaard R (2023) Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Front Med 10:1180773
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Piscataway, NJ, pp 2921–2929
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D Grad-cam (2017) Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. IEEE, Piscataway, NJ, pp 618–626
Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, New York, pp 839–847
Wang H, Wang Z, Du M, Yang F, Zhang Z, Ding S, Mardziel P, Hu X (2020) Score-CAM: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp 24–25
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Computer vision–ECCV 2014: 13th European conference, Zurich, 6-12 September 2014, Proceedings, Part I 13, 2014. Springer, pp 818-833
Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:13126034
Wachter S, Mittelstadt B, Russell C (2017) Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv JL Tech 31:841
Oh K, Chung Y-C, Kim KW, Kim W-S, Oh I-S (2019) Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci Rep 9(1):18150
Ocasio E, Duong TQ (2021) Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI. PeerJ Comput Sci 7:e560
Yagis E, De Herrera AGS, Citi L (2019) Generalization performance of deep learning models in neurodegenerative disease classification. 2019 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, New York, pp 1692–1698
Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O (2020) Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med Image Anal 63:101694
Kepuska V, Bohouta G (2018) Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home). 2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE, New York, pp 99–103
Carneiro T, Da Nóbrega RVM, Nepomuceno T, Bian G-B, De Albuquerque VHC, Reboucas Filho PP (2018) Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access 6:61677–61685
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:201011929
Li J, Chen J, Tang Y, Wang C, Landman BA, Zhou SK (2023) Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives. Med Image Anal 85:102762
He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, Gao Y, Wang Q, Zhang J, Shen D (2023) Transformers in medical image analysis. Intell Med 3(1):59–78
Tjoa E, Guan C (2020) A survey on explainable artificial intelligence (xai): toward medical xai. IEEE Trans Neural Netw Learn Syst 32(11):4793–4813
Van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA (2022) Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal 79:102470
Begoli E, Bhattacharya T, Kusnezov D (2019) The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell 1(1):20–23
Psaros AF, Meng X, Zou Z, Guo L, Karniadakis GE (2023) Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons. J Comput Phys 477:111902
Zhang X, Chan FT, Mahadevan S (2022) Explainable machine learning in image classification models: an uncertainty quantification perspective. Knowl-Based Syst 243:108418
Gawlikowski J, Tassi CRN, Ali M, Lee J, Humt M, Feng J, Kruspe A, Triebel R, Jung P, Roscher R (2023) A survey of uncertainty in deep neural networks. Artif Intell Rev 56:1–77
Download references
We would like to express our appreciation to Dr. Soheil Zarie and Dr. Jafar Zamani for their insightful suggestions throughout the course of this research.
Authors and affiliations.
Quantitative MR Imaging and Spectroscopy Group, Advanced Medical Technologies and Equipment Institute, Tehran University of Medical Sciences, Tehran, Iran
Gelareh Valizadeh, Reza Elahi & Hamidreza Saligheh Rad
School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
Department of Para-Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran
Zahra Hasankhani
Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Ahmad Shalbaf
You can also search for this author in PubMed Google Scholar
Correspondence to Hamidreza Saligheh Rad or Ahmad Shalbaf .
Publisher's note.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abbreviation/acronym | Definition |
---|---|
AAL | Automated anatomical labeling |
AC | Alternating current |
ACC | Accuracy |
AC-PC | Anterior commissure and posterior commissure |
AD | Alzheimer’s disease |
AD-NET | Age-adjusted neural network |
ADNI | Alzheimer’s disease neuroimaging initiative dataset |
AD A | Attention-guided deep domain adaptation |
AE | Auto-encoder |
AI | Artificial intelligence |
AIBL | Australian imaging, biomarker & lifestyle flagship study of ageing |
APOE | Apolipoprotein E |
AUC | Area under the receiver operating characteristic (ROC) curve |
BA | Balanced accuracy |
BGRU | Bidirectional gated recurrent unit |
BIDS | Brain imaging data structure |
BiLSTM | Bidirectional long short-term memory |
BRANT | BRainNetome analysis toolkit |
CAE | Convolutional auto-encoder |
CADs | Computer-aided diagnosis systems |
CAM | Class activation mapping |
CAM-CNN | Connection-wise attention mechanism |
CAT | Computational anatomy toolbox |
cMCI | Converter mild cognitive impairment |
CN | Cognitively normal |
CNN | Convolutional neural network |
CSF | Cerebrospinal fluid |
CV | Cross validation |
DAM | Disease-related activation map |
DARTEL | Diffeomorphic anatomical registration exponentiated lie algebra |
DBM | Deep Boltzmann machine |
DC | Direct current |
DCGAN | Deep convolutional generative adversarial networks |
DICOM | Digital imaging and communications in medicine |
DL | Deep learning |
DNN | Deep neural network |
DPN | Deep polynomial network |
DsAN | Deep subdomain adaptation network |
DWT | Discrete wavelet transform |
DZTLM | Deep zero-shot transfer learning model |
EEG | Electroencephalogram |
EER | Equal error rate |
F1 | F1-score |
FDG-PET | Fluorodeoxyglucose positron emission tomography |
FLAIR | Fluid-attenuated inversion recovery |
fMRI | Functional magnetic resonance imaging |
FSL | FMRIB software library |
GAN | Generative adversarial network |
GAP | Global average pooling |
GF-Net | Global fourier network |
GM | Gray matter |
GO | Grand opportunities |
Grad-CAM | Gradient-weighted class activation mapping |
GRU | Gated recurrent unit |
H-FCN | Hierarchical fully convolutional network |
J-ADNI | Japanese ADNI |
LASSO | Least absolute shrinkage and selection operator |
LOO | Leave-one-out |
LR | Logistic regression |
LSTM | Long short-term memory |
M FAN | Multi-task multi-level feature adversarial network |
MALPEM | Multi-Atlas label propagation with EM refinement |
MCC | Matthew’s correlation coefficient |
MCI | Mild cognitive impairment |
MEG | Magnetoencephalography |
mi-GAN | Multi-information generative adversarial network |
MIPAV | Medical image processing, analysis, and visualization |
MIs | Moment invariants |
ML | Machine learning |
MLP | Multilayer perceptron |
MNI | Montreal neurological institute |
MPRAGE | Magnetization prepared-rapid gradient echo |
MPS-FFA | A multiplane and multiscale feature fusion attention network |
MRI | Magnetic resonance imaging |
NA-ADNI | North American ADNI |
NIFTI | Neuroimaging informatics technology initiative |
NPV | Negative predictive value |
PET | Positron emission tomography |
PCA | Principal component analysis |
PDW | Proton density-weighted |
PiB | Pittsburgh compound B |
pMCI | Progressive mild cognitive impairment |
PND | Parelsnoer neurodegenerative diseases biobank |
PPV | Positive predictive value |
PREC | Precision |
PT-DCN | Path-wise transfer dense convolution network |
RF | Radiofrequency |
RNN | Recurrent neural network |
ROI | Region of interest |
SAE | Stacked auto encoder |
SCD | Subjective cognitive decline |
sMCI | Stable mild cognitive impairment |
sMRI | Structural magnetic resonance imaging |
SPE | Specificity |
SPHARM-PDM | Spherical harmonics-based parametric deformable models |
SPM | Statistical parametric mapping |
sSCD | Stable subjective cognitive decline |
SSIM | Structural similarity index |
SVM | Support vector machine |
T | Tesla |
TPA-GAN | Pyramid and attention generative adversarial network |
TR-GAN | Temporal recurrent generative adversarial network |
TRRA | Two-stage random RandAugmen |
UQ | Uncertainty quantification |
VAF | Voxels-as-features |
VBM | Voxel-based morphometry |
ViT | Vision transformer |
WHO | World health organization |
WM | White matter |
WW-ADNI | Worldwide ADNI |
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
Valizadeh, G., Elahi, R., Hasankhani, Z. et al. Deep Learning Approaches for Early Prediction of Conversion from MCI to AD using MRI and Clinical Data: A Systematic Review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10176-6
Download citation
Received : 28 October 2023
Accepted : 12 July 2024
Published : 27 September 2024
DOI : https://doi.org/10.1007/s11831-024-10176-6
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
IMAGES
VIDEO
COMMENTS
This paper discusses literature review as a methodology for conducting research and offers an overview of different types of reviews, as well as some guidelines to how to both conduct and evaluate a literature review paper. It also discusses common pitfalls and how to get literature reviews published. 1. Introduction.
What is the purpose of a literature review? When you write a thesis, dissertation, or research paper, you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to: Demonstrate your familiarity with the topic and its scholarly context Develop a theoretical framework and methodology for your research Position ...
This paper draws input from a study that employed a systematic literature review as its main source of data. A systematic review can be explained as a research method and process for identifying ...
A conceptual diagram of the need for different types of literature reviews depending on the amount of published research papers and literature reviews. The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature ...
A systematic review, as its name suggests, is a systematic way of collecting, evaluating, integrating, and presenting findings from several studies on a specific question or topic. [1] A systematic review is a research that, by identifying and combining evidence, is tailored to and answers the research question, based on an assessment of all ...
A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research ...
One of my favourite review-style articles 3 presents a plot bringing together data from multiple research papers (many of which directly contradict each other).
Writing a literature review requires a range of skills to gather, sort, evaluate and summarise peer-reviewed published data into a relevant and informative unbiased narrative. Digital access to research papers, academic texts, review articles, reference databases and public data sets are all sources of information that are available to enrich ...
Research methodology is the specific strategies, processes, or techniques utilised in the collection of information that is created and analysed. The methodology section of a research paper, or thesis, enables the reader to critically evaluate the study's validity and reliability by addressing how the data was collected or generated, and how ...
Writing a literature review in the pre or post-qualification, will be required to undertake a literature review, either as part of a course of study, as a key step in the research process.
Literature Review is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, in the form of an in-depth, critical bibliographic essay or annotated list in which attention is drawn to the most significant works. Also, we can define a literature review as the ...
In this article, through a systematic search on the methodology of literature review, we categorize a typology of literature reviews, discuss steps in conducting a systematic literature review, and provide suggestions on how to enhance rigor in literature reviews in planning education and research.
Scientific review articles provide a focused and comprehensive review of the available evidence about a subject, explain the current state of knowledge, and identify gaps that could be topics for potential future research. Detailed tables reviewing the relevant scientific literature are important components of high-quality scientific review articles. Tips for success include selecting a ...
Types of Literature Review are as follows: Narrative literature review: This type of review involves a comprehensive summary and critical analysis of the available literature on a particular topic or research question. It is often used as an introductory section of a research paper. Systematic literature review: This is a rigorous and ...
Literature Review A literature review is a discussion of the literature (aka. the "research" or "scholarship") surrounding a certain topic. A good literature review doesn't simply summarize the existing material, but provides thoughtful synthesis and analysis. The purpose of a literature review is to orient your own work within an existing body of knowledge. A literature review may be written ...
A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question. That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question. A literature review may be a stand alone work or the ...
Writing a review article is equivalent to conducting a research study, with the information gathered by the author (reviewer) representing the data. Like all major studies, it involves conceptualisation, planning, implementation, and dissemination [], all of which may be detailed in a methodology section, if necessary.
A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer. Example: Systematic review. In 2008, Dr. Robert Boyle and his colleagues published a systematic review in ...
This guide will provide research and writing tips to help students complete a literature review assignment.
ABSTRACT Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. In it we study the various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them. It is necessary for the researcher to know not only the research methods/techniques ...
A 'methodological filter' is the best method for identifying the best working style for a research question, and this method reduces the workload when surveying the literature. An essential part of the review process is differentiating good research from bad and leaning on the results of the better studies.
Based on our findings, research recommendations include conducting national studies to evaluate the current use of the BLW method, creating and validating instruments to assess health professionals' knowledge of this topic, creating structured programs for health professionals working in the field of child nutrition to learn about the BLW ...
Download Citation | On Aug 18, 2024, Thomas Huecker and others published An experimental review of different methods for measuring the grounding resistance of OHTL towers | Find, read and cite all ...
Unlike us, this review paper has evaluated studies that adopted both single-modality and multimodality of neuroimaging data as input. Moreover, the considered research questions in the mentioned review paper differ from ours. Other researchers reviewed AD early diagnosis using both machine learning and deep learning methods [48,49,50,51].