{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:06:01Z","timestamp":1791461161116,"version":"4.3.4"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":350,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union's Horizon","doi-asserted-by":"publisher","award":["826421"],"award-info":[{"award-number":["826421"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Dementia probably due to Alzheimer\u2019s disease is a progressive condition that manifests in cognitive decline and impairs patients\u2019 daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments. In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into \u2018slow\u2019 and \u2018fast\u2019 progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 \u00b1 0.01 area under the receiver operating characteristic curve in external validation. By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared with conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.<\/jats:p>","DOI":"10.1093\/braincomms\/fcae445","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T15:30:10Z","timestamp":1733412610000},"source":"Crossref","is-referenced-by-count":24,"title":["Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7212-7700","authenticated-orcid":false,"given":"Colin","family":"Birkenbihl","sequence":"first","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) , Sankt Augustin 53757 ,","place":["Germany"]},{"name":"Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn , Bonn 53115 ,","place":["Germany"]},{"name":"Department of Neurology, Massachusetts General Hospital, Harvard Medical School , Boston 02114 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johann","family":"de Jong","sequence":"additional","affiliation":[{"name":"Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG , Ingelheim 55216 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilya","family":"Yalchyk","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) , Sankt Augustin 53757 ,","place":["Germany"]},{"name":"Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn , Bonn 53115 ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holger","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) , Sankt Augustin 53757 ,","place":["Germany"]},{"name":"Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn , Bonn 53115 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