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Diagnostics
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Alzheimer's Disease Diagnosis Based on Deep Learning
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Alzheimer's Disease Diagnosis Based on Deep Learning

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 612

Special Issue Editor

Dr. Anees Abrol

E-Mail Website
Guest Editor
Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, USA
Interests: neuroimaging; neurological diseases; clinical neurology; brain imaging

Special Issue Information

Dear Colleagues,

This Special Issue focuses on Alzheimer's disease (AD) diagnosis based on deep learning techniques. Various studies are presented, highlighting the use of deep learning models for improving the accuracy and efficiency of AD diagnosis. One study reports a framework that integrates multi-modal inputs, including MRI scans, age, gender, and MMSE scores, to generate high-resolution disease probability maps for AD. Another study proposes a multi-task multi-channel convolutional neural network (CNN) for joint classification and regression tasks, utilizing MRI images and demographic information. These approaches demonstrate promising results in identifying AD and its progression, potentially paving the way for earlier interventions and the better management of this disease.

Dr. Anees Abrol
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Alzheimer's disease (AD)
  • deep learning
  • MRI
  • demographic information
  • disease probability map
  • convolutional neural network (CNN)

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Published Papers (1 paper)

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Review

18 pages, 1009 KB  
Review
Data Leakage in Deep Learning for Alzheimer’s Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation
by Vanessa M. Young, Samantha Gates, Layla Y. Garcia and Arash Salardini
Diagnostics 2025, 15(18), 2348; https://doi.org/10.3390/diagnostics15182348 - 16 Sep 2025
Viewed by 316
Abstract
Background: Deep-learning models for Alzheimer’s disease (AD) diagnosis frequently report revolutionary accuracies exceeding 95% yet consistently fail in clinical translation. This scoping review investigates whether methodological flaws, particularly data leakage, systematically inflates performance metrics, and examines the broader landscape of validation practices that [...] Read more.
Background: Deep-learning models for Alzheimer’s disease (AD) diagnosis frequently report revolutionary accuracies exceeding 95% yet consistently fail in clinical translation. This scoping review investigates whether methodological flaws, particularly data leakage, systematically inflates performance metrics, and examines the broader landscape of validation practices that impact clinical readiness. Methods: We conducted a scoping review following PRISMA-ScR guidelines, with protocol pre-registered in the Open Science Framework (OSF osf.io/2s6e9). We searched PubMed, Scopus, and CINAHL databases through May 2025 for studies employing deep learning for AD diagnosis. We developed a novel three-tier risk stratification framework to assess data leakage potential and systematically extracted data on validation practices, interpretability methods, and performance metrics. Results: From 2368 identified records, 44 studies met inclusion criteria, with 90.9% published between 2020–2023. We identified a striking inverse relationship between methodological rigor and reported accuracy. Studies with confirmed subject-wise data splitting reported accuracies of 66–90%, while those with high data leakage risk claimed 95–99% accuracy. Direct comparison within a single study demonstrated a 28-percentage point accuracy drop (from 94% to 66%) when proper validation was implemented. Only 15.9% of studies performed external validation, and 79.5% failed to control for confounders. While interpretability methods like Gradient-weighted Class Activation Mapping (Grad-CAM) were used in 18.2% of studies, clinical validation of these explanations remained largely absent. Encouragingly, high-risk methodologies decreased from 66.7% (2016–2019) to 9.5% (2022–2023). Conclusions: Data leakage and associated methodological flaws create a pervasive illusion of near-perfect performance in AD deep-learning research. True accuracy ranges from 66–90% when properly validated—comparable to existing clinical methods but far from revolutionary. The disconnect between technical implementation of interpretability methods and their clinical validation represents an additional barrier. These findings reveal fundamental challenges that must be addressed through adoption of a “methodological triad”: proper data splitting, external validation, and confounder control. Full article
(This article belongs to the Special Issue Alzheimer's Disease Diagnosis Based on Deep Learning)
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