Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention
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 elec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Medicina |
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| Online Access: | https://www.mdpi.com/1648-9144/59/10/1714 |
| _version_ | 1797573074318524416 |
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| author | Khushi Saigal Anmol Bharat Patel Brandon Lucke-Wold |
| author_facet | Khushi Saigal Anmol Bharat Patel Brandon Lucke-Wold |
| author_sort | Khushi Saigal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:04:30Z |
| format | Article |
| id | doaj.art-2df33ee483f24af99b60dc4783931a4e |
| institution | Directory Open Access Journal |
| issn | 1010-660X 1648-9144 |
| language | English |
| last_indexed | 2024-03-10T21:04:30Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medicina |
| spelling | doaj.art-2df33ee483f24af99b60dc4783931a4e2023-11-19T17:16:05ZengMDPI AGMedicina1010-660X1648-91442023-09-015910171410.3390/medicina59101714Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular InterventionKhushi Saigal0Anmol Bharat Patel1Brandon Lucke-Wold2College of Medicine, University of Florida, Gainesville, FL 32610, USACollege of Medicine, University of Miami—Miller School of Medicine, Miami, FL 33136, USADepartment of Neurosurgery, University of Florida, Gainesville, FL 32608, USAPlatelets 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.https://www.mdpi.com/1648-9144/59/10/1714artificial intelligenceantiplatelet therapyendovascular intervention |
| spellingShingle | Khushi Saigal Anmol Bharat Patel Brandon Lucke-Wold Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention Medicina artificial intelligence antiplatelet therapy endovascular intervention |
| title | Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention |
| title_full | Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention |
| title_fullStr | Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention |
| title_full_unstemmed | Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention |
| title_short | Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention |
| title_sort | artificial intelligence and neurosurgery tracking antiplatelet response patterns for endovascular intervention |
| topic | artificial intelligence antiplatelet therapy endovascular intervention |
| url | https://www.mdpi.com/1648-9144/59/10/1714 |
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