{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:09:56Z","timestamp":1790759396366,"version":"4.1.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:p>Structural magnetic resonance imaging (sMRI) depicts Alzheimer\u2019s disease (AD)-related atrophy noninvasively and has become a common input for deep-learning studies of diagnosis and progression. For this structured narrative Mini Review, we searched PubMed\/MEDLINE, the Web of Science Core Collection, and IEEE Xplore through 31 August 2026. We then purposively selected recent studies that were informative about model design or evaluation. The review covers two-dimensional convolutional neural networks, three-dimensional and hybrid volumetric architectures, transformers, and foundation models, together with longitudinal, multimodal, and graph-based extensions. Across these approaches, validity depends on clinically meaningful target labels, preprocessing that excludes test-set information, participant-level data separation, and evaluation beyond discrimination. Two-dimensional methods remain useful when data or computing resources are limited, although they do not preserve continuous whole-volume context. Three-dimensional and hybrid models incorporate more volumetric information, but anatomical fidelity still depends on representation and validation. Current AD-specific studies do not show that transformers or foundation models consistently outperform convolutional or hybrid alternatives. Stronger clinical evidence will require external testing, calibrated and uncertainty-aware predictions, transparent reporting, and prospective assessment within the intended workflow.<\/jats:p>","DOI":"10.3389\/fnhum.2026.1958462","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T08:50:36Z","timestamp":1790758236000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evolution of deep learning for structural magnetic resonance imaging analysis in Alzheimer\u2019s disease: from slice-level classification to brain structure modeling"],"prefix":"10.3389","volume":"20","author":[{"given":"Yunchen","family":"Chen","sequence":"first","affiliation":[{"name":"Shenzhen University Medical School, Shenzhen University","place":["Shenzhen, China"]},{"name":"Department of Medical Imaging, Peking University Shenzhen Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingshan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Peking University Shenzhen Hospital","place":["Shenzhen, China"]},{"name":"Key Laboratory of Biomedical Imaging Science and System, State Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanxun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Peking University Shenzhen Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ref1","first-page":"9525","article-title":"Sanity checks for saliency maps","volume":"31","author":"Adebayo","year":"2018","journal-title":"Adv. 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