https://eloinavet.github.io/en/tags/structural-mri/Structural MRIHugo Blox Builder (https://hugoblox.com)en-usThu, 01 Jan 2026 00:00:00 +0000https://eloinavet.github.io/media/icon_hu_8f6568e474c94b92.pnghttps://eloinavet.github.io/en/tags/structural-mri/https://eloinavet.github.io/en/publication/2026-ai-based-differential-diagnosi-d1gkvwhdpl0c/Thu, 01 Jan 2026 00:00:00 +0000https://eloinavet.github.io/en/publication/2026-ai-based-differential-diagnosi-d1gkvwhdpl0c/<h2 id="abstract">Abstract</h2> <p>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 …</p> <h2 id="links">Links</h2> <ul> <li> </li> <li> </li> </ul> <h2 id="citation">Citation</h2> <div class="hb-citation-display">Éloi Navet, Rémi Giraud, Boris Mansencal, Andrew Zamai, Vincent Planche, &amp; Pierrick Coupé (2026). <em>AI-Based Differential Diagnosis of Neurodegenerative Diseases Using Structural MRI: A Systematic Review</em>. d1gkVwhDpl0C. <a href="https://hal.science/hal-05640352/" target="_blank" rel="noopener">https://hal.science/hal-05640352/</a></div> <div class="js-cite-clipboard hb-citation-actions" data-filename="/citations/2026-ai-based-differential-diagnosi-d1gkVwhDpl0C.bib"> <button class="button">Copy BibTeX</button> </div>