{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:49:16Z","timestamp":1782280156008,"version":"3.54.5"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Neuroradiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Assessing generalizability and performance of machine learning models in clinical settings is crucial. In this study, we aimed to test our models\u2019 performance on an external validation real-world clinical dataset and to evaluate the impact of magnetic field strength and brain volume normalization on the classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We validated two previously published models trained on public datasets (Alzheimer\u2019s disease [AD], mild cognitive impairment [MCI] and cognitively normal [CN] subjects) on a\u00a0real-world clinical dataset from UK memory clinics (SLaM-BRC: 255 non-AD [subjects without cognitive complaints], 281 MCI and 711 AD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our \u2018CN vs. AD\u2019 model showed similar performance when tested at different magnetic fields (1.5T vs. 3.0T: 87.1% of 93\u00a0subjects had the same class assignment). The volume-normalized \u2018CN vs. AD\u2019 model (87.7% balanced accuracy [BAC]) led to decreased performance in the SLaM-BRC (81.5% BAC) due to misclassifications of non-AD subjects with evidence of hippocampal atrophy. The non-normalized \u2018CN vs. MCI vs. AD\u2019 model\u2019s performance (initially 55.3% BAC) remained similar in the SLaM-BRC cohort (BAC: SLaM-BRC\u202f=\u200954.6%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Volumes normalized to the estimated total intracranial volume led to a\u00a0smaller difference in performance between internal and external datasets than non-normalized volumes. Our \u2018CN vs. MCI vs. AD\u2019 model performance remained the same, denoting robustness. These findings suggest that dataset and disease\/diagnostic heterogeneities, magnetic field, and brain volume normalization may affect models\u2019 performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00062-026-01688-8","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:57:44Z","timestamp":1782277064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-World Generalizability of Alzheimer\u2019s Volumetric MRI Machine-Learning Models: External Validation with British Data"],"prefix":"10.1007","author":[{"given":"Helena Rico","family":"Pereira","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasco S\u00e1","family":"Diogo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Manuel","family":"Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo Alexandre","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana Pinto","family":"Prata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"1688_CR1","doi-asserted-by":"crossref","unstructured":"Grueso S, Viejo-Sobera R (2021) Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: a\u00a0systematic review. 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However, this had no influence on any aspect of the present work. The other authors have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}