{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:19:10Z","timestamp":1788740350369,"version":"build-2803163510"},"reference-count":19,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006483","name":"AbbVie","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006483","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["A&amp;D Transl Res &amp; Clin Interv"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) clinical trials with therapeutic interventions require hundreds of subjects to be studied over many months\/years due to variable and slow disease progression. This article presents a novel screening paradigm integrating disease progression models to improve trial efficiency by identifying appropriate candidates for early phase clinical studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>A traditional screening funnel is enhanced using machine learning models, including 3D convolutional neural networks and ensemble models, which integrate neuroimaging, demographic, genetic, and clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>This approach predicts clinical progression (2\u2010year Clinical Dementia Rating Sum of Boxes change &gt; 1) with an area under the curve of 0.836. Incorporating it into trials (with maximized sensitivity\/specificity optimization) could reduce the number of subjects required by 55%, shorten recruitment by 13 months, and reduce screening amyloid positron emission tomography scans by 72%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>By reducing patient burden and shortening timelines in clinical trials, this enhanced screening funnel could accelerate the development of AD therapies.<\/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>An innovative screening funnel was developed to improve Alzheimer's disease clinical trial efficiency.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The funnel incorporates machine learning (ML)\u2013based disease progression models.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The ML model identifies patients with progression rate optimal for clinical trials.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Unsuitable patients would fail early in the funnel before burdensome imaging procedures.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>This screening funnel is customizable to specific study needs.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/trc2.70084","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T06:08:58Z","timestamp":1745474938000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning\u2013enhanced screening funnel for clinical trials in Alzheimer's disease"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-7512","authenticated-orcid":false,"given":"Scott","family":"Gladstein","sequence":"first","affiliation":[{"name":"AbbVie Inc.  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