Neurodegenerative Diseases | Eloi Navet

AI-Based Differential Diagnosis of Neurodegenerative Diseases Using Structural MRI: A Systematic Review

Thu, 01 Jan 2026 00:00:00 +0000

Abstract

Accurate differential diagnosis among neurodegenerative diseases is a major clinical challenge due to overlapping symptomatology and pathologies. Artificial intelligence (AI) applied to structural MRI (sMRI) offers a promising, noninvasive approach for identifying subtle, disease-specific patterns of brain atrophy. This systematic review evaluates the landscape of AI methods for this purpose, focusing on the differential diagnosis between neurodegenerative diseases while excluding isolated disease-versus-control comparisons. Following the PRISMA guidelines, we analyzed 82 studies published between January 2008 and December 2025. Our analysis reveals a distinct methodological evolution from classical machine learning (ML), which relies on handcrafted features, to end-to-end deep learning (DL) architectures that automatically learn representations directly from image data. While models often report high classification accuracy (typically exceeding 90% for binary tasks), accuracy ranges from 70% to 92% for standard 3-class tasks, and drops to 65% to 88% for broader multi-disease frameworks (≥ 4 classes) or out-of-domain evaluations. Furthermore, we identify systemic barriers that delay clinical translation: a widespread reliance on private datasets (77% of studies), a severe lack of rigorous out-of-domain validation (only 28% of studies), a low rate of public code availability (20% of studies), and limited use of pathologically or biologically confirmed diagnostic labels (only 18% of studies). These challenges in data infrastructure and validation standards undermine model generalizability and cast doubt on the clinical relevance of …

Citation

Éloi Navet, Rémi Giraud, Boris Mansencal, Andrew Zamai, Vincent Planche, & Pierrick Coupé (2026). AI-Based Differential Diagnosis of Neurodegenerative Diseases Using Structural MRI: A Systematic Review. d1gkVwhDpl0C. https://hal.science/hal-05640352/

On the Stability and Robustness of Vision Transformers for Neurodegenerative Disease Classification

Thu, 01 Jan 2026 00:00:00 +0000

Abstract

Vision Transformers (ViTs) have recently been explored for structural MRI classification, motivated by their ability to capture non-local image structure. However, in limited and heterogeneous clinical cohorts, their weak inductive biases and sensitivity to training conditions often lead to high-variance behaviour. While binary settings such as cognitively normal vs. dementia are widely reported and typically exhibit moderate variability, we show that this stability does not extend to differential diagnosis. When increasing task complexity (e.g., controls vs. Alzheimer’s Disease vs. Frontotemporal Dementia), performance becomes sensitive to class imbalance and phenotype overlap, with greater variability driven by fewer samples per class, noisier labels, and increased inter-site heterogeneity. In this study, we investigate a stabilization protocol combining data augmentation, architectural constraints, and optimization strategies on multi-site MRI datasets. We assess how model variance evolves with task complexity using patient-level paired bootstrapping, calibration analysis, paired significance tests, and estimates of the probability of false outperformance to obtain uncertainty-aware comparisons across models. Our results highlight conditions under which Transformer-based classifiers can be consistently trained with limited neuroimaging data and illustrate that several performance gains disappear once stochastic variability is reported. These results emphasize that reliable differential diagnosis with ViTs requires both robust stabilization protocols to mitigate optimization noise and standardized uncertainty quantification beyond simple point-estimates.

Citation

Eloi Navet, Rémi Giraud, Boris Mansencal, & Pierrick Coupe (2026). On the Stability and Robustness of Vision Transformers for Neurodegenerative Disease Classification. Medical Imaging with Deep Learning-Validation Papers. https://openreview.net/forum?id=MiS54B5arR

Neurodegenerative Diseases | Eloi Navet

AI-Based Differential Diagnosis of Neurodegenerative Diseases Using Structural MRI: A Systematic Review

Abstract

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Citation

On the Stability and Robustness of Vision Transformers for Neurodegenerative Disease Classification

Abstract

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Citation