{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:15:06Z","timestamp":1791951306258,"version":"4.3.4"},"reference-count":42,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2023,4,18]]},"abstract":"<jats:p>Background: Driving behavior as a digital marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer\u2019s disease (AD). Objective: This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD. Methods: We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma A\u03b242\/A\u03b240, where A\u03b242\/A\u03b240\u200a&lt;\u200a0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE \u025b4 status, and driving variables. Results: All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma A\u03b242\/A\u03b240) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma A\u03b242\/A\u03b240. Incorporating age and APOE \u025b4 carrier status improved the diagnostic performance of the model to 0.80 [&gt;0.051]. Conclusion: Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research.<\/jats:p>","DOI":"10.3233\/jad-221268","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T12:11:40Z","timestamp":1678795900000},"page":"1487-1497","source":"Crossref","is-referenced-by-count":8,"title":["Everyday Driving and Plasma Biomarkers in Alzheimer\u2019s Disease: Leveraging Artificial Intelligence to Expand Our Diagnostic Toolkit"],"prefix":"10.1177","volume":"92","author":[{"given":"Sayeh","family":"Bayat","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada"},{"name":"Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada"},{"name":"Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine M.","family":"Roe","sequence":"additional","affiliation":[{"name":"Roe Consulting LLC, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne","family":"Schindler","sequence":"additional","affiliation":[{"name":"Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samantha A.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason M.","family":"Doherty","sequence":"additional","affiliation":[{"name":"Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann 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