Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Deep Learning Approaches for Early Prediction of Conversion From Mci to Ad Using Mri and Clinical Data: A Systematic Review Publisher



Valizadeh G1 ; Elahi R1, 2 ; Hasankhani Z3 ; Rad HS1 ; Shalbaf A4
Authors
Show Affiliations
Authors Affiliations
  1. 1. Quantitative MR Imaging and Spectroscopy Group, Advanced Medical Technologies and Equipment Institute, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
  3. 3. Department of Para-Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran
  4. 4. Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Source: Archives of Computational Methods in Engineering Published:2025


Abstract

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. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.
Related Docs
View other Related Docs
1. Artificial Intelligence in Science and Society: The Vision of Usern, IEEE Access (2025)
2. Diagnosis of Early Mild Cognitive Impairment Using a Multiobjective Optimization Algorithm Based on T1-Mri Data, Scientific Reports (2022)
3. Machine Learning Based Algorithms for Virtual Early Detection and Screening of Neurodegenerative and Neurocognitive Disorders: A Systematic-Review, Frontiers in Neurology (2024)
4. Proposing a Novel Deep Network for Detecting Covid-19 Based on Chest Images, Scientific Reports (2022)
5. Artificial Intelligence Based Vision Transformer Application for Grading Histopathological Images of Oral Epithelial Dysplasia: A Step Towards Ai-Driven Diagnosis, BMC Cancer (2025)
6. Classification of Early-Mci Patients From Healthy Controls Using Evolutionary Optimization of Graph Measures of Resting-State Fmri, for the Alzheimer’S Disease Neuroimaging Initiative, PLoS ONE (2022)
7. Exploring a Decade of Deep Learning in Dentistry: A Comprehensive Mapping Review, Clinical Oral Investigations (2025)
8. Nf-Rcnn: Heart Localization and Right Ventricle Wall Motion Abnormality Detection in Cardiac Mri, Physica Medica (2020)
Experts (# of related papers)
View all Related Experts
Maryam Noroozian (4)
Massoud Amanlou (2)
Other Related Docs
9. Binarized Multi-Gate Mixture of Bayesian Experts for Cardiac Syndrome X Diagnosis: A Clinician-In-The-Loop Scenario With a Belief-Uncertainty Fusion Paradigm, Information Fusion (2023)
10. Salivary Levels of Disease-Related Biomarkers in the Early Stages of Parkinson's and Alzheimer's Disease: A Cross-Sectional Study, IBRO Neuroscience Reports (2023)
11. Artificial Intelligence and Covid-19: Deep Learning Approaches for Diagnosis and Treatment, IEEE Access (2020)
12. Differential Diagnosis Among Alzheimer's Disease, Mild Cognitive Impairment, and Normal Subjects Using Resting-State Fmri Data Extracted From Multi-Subject Dictionary Learning Atlas: A Deep Learning-Based Study, Frontiers in Biomedical Technologies (2022)
13. An Efficient Computer-Aided Diagnosis Model for Classifying Melanoma Cancer Using Fuzzy-Id3-Pvalue Decision Tree Algorithm, Multimedia Tools and Applications (2024)
14. Localized-Atlas-Based Segmentation of Breast Mri in a Decision-Making Framework, Australasian Physical and Engineering Sciences in Medicine (2017)
15. Deepcda: Deep Cross-Domain Compound-Protein Affinity Prediction Through Lstm and Convolutional Neural Networks, Bioinformatics (2020)
16. A Modified Deep Learning Model in the Classification of Post-Covid-19 Lung Disease and a Comparative Study on Iranian and International Databases, International Journal of Radiation Research (2024)
17. Classification of Alzheimer's Disease Using Ricci Flow-Based Spherical Parameterization and Machine Learning Techniques, Signal, Image and Video Processing (2024)
18. Repeatability of Radiomic Features in Magnetic Resonance Imaging of Glioblastoma: Test–Retest and Image Registration Analyses, Medical Physics (2020)
19. Validity and Cultural Generalisability of a 5-Minute Ai-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia, Frontiers in Psychiatry (2021)
20. Mri-Based Brain Tumor Detection Using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques, BMC Medical Informatics and Decision Making (2023)
21. A Mask-Guided Attention Deep Learning Model for Covid-19 Diagnosis Based on an Integrated Ct Scan Images Database, IISE Transactions on Healthcare Systems Engineering (2023)
22. Utilizing Pix2pix Conditional Generative Adversarial Networks to Recover Missing Data in Preclinical Pet Scanner Sinogram Gaps, Physica Medica (2025)
23. Two Year Outcomes, Cognitive and Behavioral Markers of Decline in Healthy, Cognitively Normal Older Persons With Global Deterioration Scale Stage 2 (Subjective Cognitive Decline With Impairment), Journal of Alzheimer's Disease (2019)
24. Convergent Regional Brain Abnormalities in Behavioral Variant Frontotemporal Dementia: A Neuroimaging Meta-Analysis of 73 Studies, Alzheimer's and Dementia: Diagnosis# Assessment and Disease Monitoring (2022)
25. Generalizable End-To-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6Dof Inertial Measurement Units, Measurement: Journal of the International Measurement Confederation (2023)
26. Development and Validation of Alzheimer’S Disease Animal Model for the Purpose of Regenerative Medicine, Cell and Tissue Banking (2019)
27. Subcortical Signal Alteration of Corticospinal Tracts. a Radiologic Manifestation of Aria: A Case Report, Radiology Case Reports (2023)
28. A Novel Deep Learning Model for Breast Lesion Classification Using Ultrasound Images: A Multicenter Data Evaluation, Physica Medica (2023)
29. Deep Learning Framework for Prediction of Infection Severity of Covid-19, Frontiers in Medicine (2022)
30. Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets, IEEE Access (2023)
31. Translation and Psychometric Evaluation of a Persian Version of the Functional Assessment Staging Scale (I-Fast) in Older Patients With Mild Cognitive Impairment and Alzheimer’S Disease in Iran, Acta Neurologica Belgica (2022)
32. Alzheimer's Disease: Prototype of Cognitive Deterioration, Valuable Lessons to Understand Human Cognition, Neurologic Clinics (2016)
33. Deep Learning for Diagnosis of Head and Neck Cancers Through Radiographic Data: A Systematic Review and Meta-Analysis, Oral Radiology (2024)
34. Deep Transferable Compound Representation Across Domains and Tasks for Low Data Drug Discovery, Journal of Chemical Information and Modeling (2019)
35. Deep Vision Transformers for Prognostic Modeling in Covid-19 Patients Using Large Multi-Institutional Chest Ct Dataset, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)
36. Automatic Cardiac Evaluations Using a Deep Video Object Segmentation Network, Insights into Imaging (2022)
37. Medical Imaging and Computational Image Analysis in Covid-19 Diagnosis: A Review, Computers in Biology and Medicine (2021)