{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T10:41:03Z","timestamp":1783507263334,"version":"3.55.0"},"reference-count":116,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in elective neurosurgery. Continuing therapy during surgery poses a bleeding risk, while discontinuing it before surgery increases the risk of thrombosis. Discontinuation is recommended in neurosurgical settings but carries an elevated risk of ischemic events. Conversely, maintaining antithrombotic therapy may increase bleeding and the need for transfusions, leading to a poor prognosis. Artificial intelligence (AI) holds promise in making difficult decisions regarding antiplatelet therapy. This paper discusses current clinical guidelines and supported regimens for antiplatelet therapy in neurosurgery. It also explores methodologies like P2Y12 reaction units (PRU) monitoring and thromboelastography (TEG) mapping for monitoring the use of antiplatelet regimens as well as their limitations. The paper explores the potential of AI to overcome such limitations associated with PRU monitoring and TEG mapping. It highlights various studies in the field of cardiovascular and neuroendovascular surgery which use AI prediction models to forecast adverse outcomes such as ischemia and bleeding, offering assistance in decision-making for antiplatelet therapy. In addition, the use of AI to improve patient adherence to antiplatelet regimens is also considered. Overall, this research aims to provide insights into the use of antiplatelet therapy and the role of AI in optimizing treatment plans in neurosurgical settings.<\/jats:p>","DOI":"10.3390\/medicina59101714","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T03:53:00Z","timestamp":1695700380000},"page":"1714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention"],"prefix":"10.3390","volume":"59","author":[{"given":"Khushi","family":"Saigal","sequence":"first","affiliation":[{"name":"College of Medicine, University of Florida, Gainesville, FL 32610, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anmol Bharat","family":"Patel","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Miami\u2014Miller School of Medicine, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6577-4080","authenticated-orcid":false,"given":"Brandon","family":"Lucke-Wold","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Florida, Gainesville, FL 32608, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"152","DOI":"10.7861\/clinmedicine.16-2-152","article-title":"Antiplatelet therapy\u2014A summary for the general physicians","volume":"16","author":"Thachil","year":"2016","journal-title":"Clin. 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