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We aimed to develop a deep\u2010learning algorithm using OCT to detect AD and MCI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>We performed a cross\u2010sectional study involving 228 Asian participants (173 cases\/55 controls) for model development and testing on 68 Asian (52 cases\/16 controls) and 85 White (39 cases\/46 controls) participants. Features from OCT were used to develop an ensemble trilateral deep\u2010learning model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      The trilateral model significantly outperformed single non\u2010deep learning models in Asian (area under the curve [AUC]\u00a0=\u00a00.91\u00a0vs. 0.71\u20130.72,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.022\u20100.032) and White (AUC\u00a0=\u00a00.84\u00a0vs. 0.58\u20130.75,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.056\u2010\u00a0&lt;\u00a00.001) populations. However, its performance was comparable to that of the trilateral statistical model (AUCs similar,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&gt;\u00a00.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Both multimodal approaches, using deep learning or traditional statistical models, show promise for AD and MCI detection. The choice between these models may depend on computational resources, interpretability preferences, and clinical needs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>A deep\u2010learning algorithm was developed to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) using OCT images.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The combined model outperformed single OCT parameters in both Asian and White cohorts.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The study demonstrates the potential of OCT\u2010based deep\u2010learning algorithms for AD and MCI detection.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70041","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T09:51:22Z","timestamp":1736848282000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography"],"prefix":"10.1002","volume":"17","author":[{"given":"Jacqueline","family":"Chua","sequence":"first","affiliation":[{"name":"Singapore Eye Research Institute Singapore National Eye Centre  Singapore Singapore"},{"name":"Ophthalmology and Visual Sciences Academic Clinical Program Duke\u2010NUS Medical School National University of Singapore  Singapore 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