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Saigal, K.; Patel, A.B.; Lucke-Wold, B. Use of AI to Monitor Antiplatelet Therapy. Encyclopedia. Available online: https://encyclopedia.pub/entry/50166 (accessed on 04 July 2024).
Saigal K, Patel AB, Lucke-Wold B. Use of AI to Monitor Antiplatelet Therapy. Encyclopedia. Available at: https://encyclopedia.pub/entry/50166. Accessed July 04, 2024.
Saigal, Khushi, Anmol Bharat Patel, Brandon Lucke-Wold. "Use of AI to Monitor Antiplatelet Therapy" Encyclopedia, https://encyclopedia.pub/entry/50166 (accessed July 04, 2024).
Saigal, K., Patel, A.B., & Lucke-Wold, B. (2023, October 12). Use of AI to Monitor Antiplatelet Therapy. In Encyclopedia. https://encyclopedia.pub/entry/50166
Saigal, Khushi, et al. "Use of AI to Monitor Antiplatelet Therapy." Encyclopedia. Web. 12 October, 2023.
Use of AI to Monitor Antiplatelet Therapy
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This entry is adapted from the peer-reviewed paper 10.3390/medicina59101714

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. 

artificial intelligence antiplatelet therapy endovascular intervention

1. Introduction

Platelets play a crucial role in the maintenance of normal blood clotting and the formation of blood clots. The process of platelet aggregation is a significant factor in the development of various arterial blockages such as coronary artery disease, strokes, and peripheral arterial disease. Consequently, the use of medications that inhibit platelet function, known as antiplatelet therapy, is essential in preventing recurring events in individuals affected by these conditions [1][2][3][4].
There is a lack of comprehensive guidelines regarding the use of antiplatelet agents in elective neurosurgery. The decision to continue antiplatelet therapy during surgery presents a risk due to increased bleeding, while discontinuing the medication prior to surgery carries the risk of thrombosis. In general, the discontinuation of antiplatelet therapy is typically the recommended course of action in neurosurgical settings [5], but this carries an elevated risk of ischemic events, including potentially life-threatening stent thrombosis, myocardial infarction, and stroke. Conversely, maintaining antithrombotic therapy may heighten the chances of bleeding and the need for blood transfusions, both of which are recognized factors that contribute to a poor prognosis [6]. It is possible that these difficult decisions regarding when to use antiplatelet therapy could be determined by artificial intelligence (AI) through utilizing technology to monitor individual antiplatelet compliance and platelet function.
Artificial intelligence (AI) is increasingly shaping medical decision-making, with notable applications in neurosurgery and antiplatelet therapy. In neurosurgery, AI aids in assessing the delicate balance between ischemia and bleeding risks post-stent placement or -WEB embolization. Although most AI research has historically focused on cardiovascular medicine, recent studies have developed AI models for predicting ischemic and bleeding risks in patients undergoing drug-eluting stent implantation. Additionally, AI can enhance medication adherence through smartphone applications, ensuring that patients follow their prescribed regimens, including antiplatelet therapies. The integration of AI in medical decision-making holds promise for optimizing individualized treatment plans in the complex realm of neuroendovascular procedures.

2. Use of AI to Monitor Antiplatelet Therapy

It is crucial to assess the balance between the risks of ischemia and bleeding after stent placement or WEB embolization in neurosurgery and to determine the optimal duration of DAPT accordingly. With varying degrees of adherence to their medication regimen and different levels of platelet inhibition within individuals, it may be possible to use artificial intelligence (AI) to make important decisions regarding when to use antiplatelet therapy. Currently, most research that exists that has studied the use of AI in the prediction of ischemic/bleeding risk exists in the field of cardiovascular medicine, but many of these AI models can potentially be applied to the field of neurosurgery. AI’s predictive capabilities can significantly impact the management of antiplatelet therapy in neurosurgery. The following is a summary of the various ways in which AI can impact the management of antiplatelet therapy in neurosurgery.
Predicting Ischemic and Bleeding Risks in Neurosurgery: AI models have been successfully used in cardiovascular medicine to predict ischemic and bleeding risks following procedures like drug-eluting stent (DES) implantation [7][8][9][10][11][12][13]. These models take into account various patient factors, including age, diagnoses, medications, procedures, and DAPT continuation status. AI can adapt and extend these models to neurosurgery settings, helping to predict the risks of ischemic events and bleeding after stent placement or WEB embolization in neuroendovascular procedures. These predictions can guide decisions regarding the timing and duration of antiplatelet therapy.
Superior Performance of AI Models: Studies have shown that AI models can outperform traditional clinical tools like the DAPT score in predicting outcomes. For example, AI models demonstrated a higher area under the receiver operating characteristic (AUC) values, a performance metric that is used to evaluate classification models, for predicting ischemia and bleeding compared to the DAPT score [7]. In addition, these models accounted for scenarios that were not addressed by the DAPT score. This improved accuracy can aid neurosurgeons in making more informed decisions about antiplatelet therapy, especially in the 12–30-month window following stent placement.
Personalized Treatment Plans: AI models can help create personalized treatment plans based on individual patient profiles and risks. For example, in one study, a machine learning tool known as the PRAISE (Prediction of Adverse Events Following an Acute Coronary Syndrome) predicted death, myocardial infarctions, and major bleeding following an acute coronary syndrome using 25 clinical features that are incorporated during discharge. Based on this information, patients can be categorized into different risk groups (low, intermediate, high), and those at a higher risk can receive closer monitoring and potentially shorter durations of DAPT. This personalized approach enhances patient care and minimizes the risk of complications [14][15].
AI for Subarachnoid Hemorrhage (SAH), Unruptured Intracranial Aneurysms (UIA), and Chronic Subdural Hematoma Outcomes: AI has also already been applied to the field of neurosurgery to predict outcomes in conditions like SAH. These predictive models can assess factors such as neurological severity, age, aneurysm location, and size to forecast outcomes [16][17][18][19][20][21][22][23][24][25][26][27][28] with AUC values ranging from 0.70 to 0.90. More recently, deep learning has been employed, which has resulted in an improved prediction accuracy of 0.90 with limited datasets for SAH outcomes [24][27][28]. Two widely recognized radiological scales, namely the Fisher computed tomography (CT) scale [29] and the modified Fisher scale [30], assess the extent of bleeding to forecast delayed cerebral ischemia (DCI) incidence. Additionally, several statistically derived scores [31][32][33] incorporating supplementary factors have been investigated, yielding an average AUC of approximately 0.70 for predicting DCI occurrence [34]. As for AI-based prediction models, they have demonstrated AUC values of around 0.80 [27][35][36][37][38][39] in forecasting DCI occurrence. If we were able to anticipate DCI, we could administer proactive and immediate treatment. Other studies have investigated the potential use of machine learning to predict clinical outcomes in the microsurgical treatment of unruptured intracranial aneurysms (UIA) [40][41][42]. One study offered personalized predictions at the patient level, estimating outcomes such as neurological recovery upon discharge, as well as the likelihood of complications and new neurological issues upon discharge. These predictions are based on readily available preoperative data. The study employed various scoring systems to estimate the risk of aneurysm rupture (PHASES) or growth (ELAPSS), or to directly assess the balance between potential risks and benefits (UIATS) [43]. Methodologies from these studies could be used to anticipate potential complications following UIAs to adjust antiplatelet therapies to respond to them. Additionally, ML models have been used to predict the recurrence risk of chronic subdural hematoma (cSDH) while withholding antiplatelet and anticoagulant agents. This study can also inform the adjustment of antiplatelet regimens according to ischemia/bleeding complication [44].
Medication Adherence Monitoring: AI can be used to monitor patient adherence to antiplatelet therapy. For instance, smartphone applications can use AI to confirm medication ingestion through the phone’s camera. Past studies using this technology demonstrated a 67% increase in absolute drug adherence compared to the control [45]. This technology can ensure that patients are adhering to their medication regimen, leading to better outcomes. Similar systems can be adapted for patients undergoing stent or WEB embolization placement, helping to ensure adherence to antiplatelet medications and facilitating communication among healthcare providers.
Individualized Treatment Plans with PRU and TEG Mapping: AI platforms can incorporate PRU and TEG mapping to provide individualized treatment plans [45]. This can help to promptly identify non-responders to antiplatelet therapy and adjust their medication regimens accordingly. Through the monitoring of labs, AI has the potential to identify those with suboptimal antiplatelet treatment.
Patient Engagement and Self-Efficacy: AI can be used to engage patients in their treatment plans through reminder systems, text messages, and other technologies, as other studies have done in the past [46]. This not only promotes medication adherence but also increases patient self-efficacy in managing their health.
Of course, the commonly discussed challenges associated with integrating AI into clinical practice remain and require attention before widespread clinical adoption [47]. One of these critical challenges revolves around patient privacy concerns, as AI relies on extensive data for algorithm training and sequencing [48]. Striking a balance between privacy protection and data accessibility is essential for sustaining long-term progress in neurosurgical AI [49]. Furthermore, ensuring the quality of data is paramount for meaningful results, emphasizing the importance of effective data implementation in machine learning training [49]. Another issue to consider is the risk of neurosurgeons becoming overly reliant on AI, potentially hindering their skill development, while hardware and software glitches pose the threat of incorrectly directing antiplatelet regimens if not addressed promptly [47][50]. Although artificial intelligence in healthcare has made significant strides, its potential for future advancements remains untapped. The existing innovations have already yielded benefits for patients, but it is crucial for regulatory frameworks to adapt to the rapidly evolving healthcare landscape to proactively address and mitigate potential risks.
In summary, AI’s predictive capabilities can revolutionize the management of antiplatelet therapy in neurosurgery by providing more accurate risk assessments and personalized treatment plans, as well as improved medication adherence. By adapting AI models from cardiovascular medicine and leveraging advanced technologies, neurosurgeons can optimize patient care and outcomes in neuroendovascular procedures (Table 1).
Table 1. Important articles that address the use of AI to predict clinical outcomes relating to ischemia/hemorrhages.
Author Year of Publication Predictive Model Used Condition Studied Results
Li et al.; Fan et al. [7][8] 2022, 2019 light gradient boosting machine Risks of ischemic/bleeding events for patients who had undergone DES implantation AUC of 0.82 for ischemia and 0.77 for bleeding
Li et al.; Tolles et al. [7][9] 2022, 2016 Logistic regression Risks of ischemic/bleeding events for patients who had undergone DES implantation AUC of 0.82 for ischemia and 0.77 for bleeding
Li et al.; Svetnik et al. [7][10] 2022, 2003 Random forest Risks of ischemic/bleeding events for patients who had undergone DES implantation AUC of 0.82 for ischemia and 0.77 for bleeding
Li et al.; Cho et al. [7][11] 2022, 2004 Random forest Risks of ischemic/bleeding events for patients who had undergone DES implantation AUC of 0.82 for ischemia and 0.77 for bleeding
Li et al.; Choi et al. [7][12] 2022, 2016 RETAIN Risks of ischemic/bleeding events for patients who had undergone DES implantation AUC of 0.82 for ischemia and 0.77 for bleeding
D’Ascenzo [15] 2021 PRAISE ACS syndrome outcomes AUC for the 1-year risk of MI of 0.74 in the internal validation cohort and 0.81 in the external validation cohort and an AUC for the 1-year risk of major bleeding of 0.70 in the internal validation cohort and 0.86 in external validation cohort
Jaja et al.; Risselada et al.; Abulhasan et al.; Zeiler et al.; Hostettler et al.; Jaja et al.; Witsch et al.; van Donkelaar et al., 2019 [16][17][18][19][20][21][22][23] 2013, 2010, 2018; 2017, 2020, 2018; 2016, 2019 Statistical methods SAH outcomes 0.70–0.90
Rubbert et al. [25] 2018 Random forests SAH outcomes 71% accuracy
De Toledo et al. [26] 2009 Random forests SAH outcomes 0.84 AUC
Katsuki et al.; de Jong et al.; Wang et al. [24][27][28] 2020, 2021, 2022 Deep learning SAH outcomes Approximately 0.90 AUC
Ramos et al. [36] 2019 Machine learning SAH outcomes AUC 0.74
Meghjani et al. [37] 2021 L2-regularized logistic regression, random forest, and support vector machines models SAH outcomes AUC 0.83
De Jong et al. [27] 2021 feedforward artificial neural networks (ffANNs) SAH outcomes AUC 0.72
Park et al. [38] 2019 Random kernel SAH outcomes AUC 0.77
Savarraj et al. [35] 2021 Machine learning SAH outcomes AUC 0.75
Taghavi et al. [39] 2023 Random Forest SAH outcomes AUC 0.780
Staartjes et al. [43] 2020 Machine learning models UIA outcomes AUC 0.63–0.77
Zanaty et al. [44] 2020 Machine learning models Recurrence of cSDH AUC of 0.91

References

  1. Thachil, J. Antiplatelet therapy—A summary for the general physicians. Clin. Med. 2016, 16, 152–160.
  2. Passacquale, G.; Sharma, P.; Perera, D.; Ferro, A. Antiplatelet therapy in cardiovascular disease: Current status and future directions. Br. J. Clin. Pharmacol. 2022, 88, 2686–2699.
  3. Patrono, C.; Morais, J.; Baigent, C.; Collet, J.P.; Fitzgerald, D.; Halvorsen, S.; Rocca, B.; Siegbehan, A.; Storey, R.F.; Vilahur, G. Antiplatelet agents for the treatment and prevention of coronary atherothrombosis. J. Am. Coll. Cardiol. 2017, 70, 1760–1776.
  4. Montinari, M.R.; Minelli, S.; De Caterina, R. The first 3500 years of aspirin history from its roots–A concise summary. Vasc. Pharmacol. 2019, 113, 1–8.
  5. Wang, X.; Wang, X.; Yu, Y.; Han, R. Continuation versus discontinuation of aspirin-based antiplatelet therapy for perioperative bleeding and ischaemic events in adults undergoing neurosurgery: Protocol for a systematic review and meta-analysis. BMJ Open 2021, 11, e046741.
  6. Garg, P.; Galper, B.Z.; Cohen, D.J.; Yeh, R.W.; Mauri, L. Balancing the risks of bleeding and stent thrombosis: A decision analytic model to compare durations of dual antiplatelet therapy after drug-eluting stents. Am. Heart J. 2015, 169, 222–233.e5.
  7. Li, F.; Rasmy, L.; Xiang, Y.; Feng, J.; Du, J.; Aguilar, D.; Dhoble, A.; Wang, Q.; Niu, S.; Hu, X.; et al. AI-aided dynamic prediction of bleeding and ischemic risk after coronary stenting and subsequent DAPT. bioRxiv 2022.
  8. Fan, J.; Ma, X.; Wu, L.; Zhang, F.; Yu, X.; Zeng, W. Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agric. Water Manag. 2019, 225, 105758.
  9. Tolles, J.; Meurer, W.J. Logistic regression: Relating patient characteristics to outcomes. JAMA 2016, 316, 533–534.
  10. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J.C.; Sheridan, R.P.; Feuston, B.P. Random forest: A classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947–1958.
  11. Cho, K.; Van Merriënboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014, arXiv:1406.1078.
  12. Choi, E.; Bahadori, M.T.; Kulas, J.A.; Schuetz, A.; Stewart, W.F.; Sun, J. RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. Adv. Neural Inf. Process. Syst. 2016, 29, 3512–3520.
  13. Rasmy, L.; Wu, Y.; Wang, N.; Geng, X.; Zheng, W.J.; Wang, F.; Wu, H.; Xu, H.; Zhi, D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J. Biomed. Inform. 2018, 84, 11–16.
  14. Fernández-Ruiz, I. Machine learning predicts risk in ACS. Nat. Rev. Cardiol. 2021, 18, 230.
  15. D’Ascenzo, F.; De Filippo, O.; Gallone, G.; Mittone, G.; Deriu, M.A.; Iannaccone, M.; Ariza-Solé, A.; Liebetrau, C.; Manzano-Fernández, S.; Quadri, G.; et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): A modelling study of pooled datasets. Lancet 2021, 397, 199–207.
  16. Jaja, B.N.; Cusimano, M.D.; Etminan, N.; Hanggi, D.; Hasan, D.; Ilodigwe, D.; Lantigua, H.; Le Roux, P.L.; Lo, B.; Macdonald, R.L.; et al. Clinical prediction models for aneurysmal subarachnoid hemorrhage: A systematic review. Neurocritical Care 2013, 18, 143–153.
  17. Risselada, R.; Lingsma, H.F.; Bauer-Mehren, A.; Friedrich, C.M.; Molyneux, A.J.; Kerr, R.S.C.; Yarnold, K.J.; Sneade, M.; Steyerberg, E.W.; Sturkenboom, M.C.J.M. Prediction of 60 day case-fatality after aneurysmal subarachnoid haemorrhage: Results from the International Subarachnoid Aneurysm Trial (ISAT). Eur. J. Epidemiol. 2010, 25, 261–266.
  18. Abulhasan, Y.B.; Alabdulraheem, N.; Simoneau, G.; Angle, M.R.; Teitelbaum, J. Mortality after spontaneous subarachnoid hemorrhage: Causality and validation of a prediction model. World Neurosurg. 2018, 112, e799–e811.
  19. Zeiler, F.A.; Lo, B.W.Y.; Akoth, E.; Silvaggio, J.; Kaufmann, A.M.; Teitelbaum, J.; West, M. Predicting outcome in subarachnoid hemorrhage (SAH) utilizing the Full Outline of UnResponsiveness (FOUR) score. Neurocritical Care 2017, 27, 381–391.
  20. Hostettler, I.C.; Sebök, M.; Ambler, G.; Muroi, C.; Prömmel, P.; Neidert, M.C.; Cristoph, M.; Johannes, K.; Pangalu, A.; Germans, M.R. Validation and Optimization of barrow neurological institute score in prediction of adverse events and functional outcome after subarachnoid hemorrhage—Creation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score. Neurosurgery 2020, 88, 96–105.
  21. Jaja, B.N.R.; Saposnik, G.; Lingsma, H.F.; Macdonald, E.; Thorpe, K.E.; Mamdani, M.; Steyerberg, E.W.; Molyneux, A.; Manoel, A.L.O.; Schatlo, B.; et al. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: The SAHIT multinational cohort study. BMJ 2018, 360, j5745.
  22. Witsch, J.; Frey, H.P.; Patel, S.; Park, S.; Lahiri, S.; Schmidt, J.M.; Agarwal, S.; Falo, M.C.; Velazquez, A.; Claassen, J.; et al. Prognostication of long-term outcomes after subarachnoid hemorrhage: The FRESH score. Ann. Neurol. 2016, 80, 46–58.
  23. Van Donkelaar, C.E.; Bakker, N.A.; Birks, J.; Veeger, N.J.; Metzemaekers, J.D.; Molyneux, A.J.; Groen, R.J.M.; van Dijk, J.M.C. Prediction of outcome after aneurysmal subarachnoid hemorrhage: Development and validation of the SAFIRE grading scale. Stroke 2019, 50, 837–844.
  24. Katsuki, M.; Kawamura, S.; Koh, A. Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia. Cureus 2021, 13, e15695.
  25. Rubbert, C.; Patil, K.R.; Beseoglu, K.; Mathys, C.; May, R.; Kaschner, M.G.; Sigl, B.; Teichert, N.A.; Boos, J.; Caspers, J.; et al. Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission. Eur. Radiol. 2018, 28, 4949–4958.
  26. De Toledo, P.; Rios, P.M.; Ledezma, A.; Sanchis, A.; Alen, J.F.; Lagares, A. Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques. IEEE Trans. Inf. Technol. Biomed. 2009, 13, 794–801.
  27. De Jong, G.; Aquarius, R.; Sanaan, B.; Bartels, R.H.; Grotenhuis, J.A.; Henssen, D.J.; Boogaarts, H.D. Prediction models in aneurysmal subarachnoid hemorrhage: Forecasting clinical outcome with artificial intelligence. Neurosurgery 2021, 88, E427–E434.
  28. Wang, R.; Zhang, J.; Shan, B.; He, M.; Xu, J. XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage. Neuropsychiatr. Dis. Treat. 2022, 18, 659–667.
  29. Fisher, C.M.; Kistler, J.P.; Davis, J.M. Relation of cerebral vasospasm to subarachnoid hemorrhage visualized by computerized tomographic scanning. Neurosurgery 1980, 6, 1–9.
  30. Frontera, J.A.; Claassen, J.; Schmidt, J.M.; Wartenberg, K.E.; Temes, R.; Connolly, E.S.; Macdonald, R.; Loch, R.; Mayer, S.A. Prediction of symptomatic vasospasm after subarachnoid hemorrhage: The modified fisher scale. Neurosurgery 2006, 59, 21–27.
  31. Ahn, S.H.; Savarraj, J.P.; Pervez, M.; Jones, W.; Park, J.; Jeon, S.B.; Choi, H.A. The subarachnoid hemorrhage early brain edema score predicts delayed cerebral ischemia and clinical outcomes. Neurosurgery 2018, 83, 137–145.
  32. Claassen, J.; Carhuapoma, J.R.; Kreiter, K.T.; Du, E.Y.; Connolly, E.S.; Mayer, S.A. Global cerebral edema after subarachnoid hemorrhage: Frequency, predictors, and impact on outcome. Stroke 2002, 33, 1225–1232.
  33. De Oliveira Manoel, A.L.; Jaja, B.N.; Germans, M.R.; Yan, H.; Qian, W.; Kouzmina, E.; Marotta, T.R.; Turkel-Parrella, D.; Schweizer, T.A.; Macdonald, R.L.; et al. The VASOGRADE: A simple grading scale for prediction of delayed cerebral ischemia after subarachnoid hemorrhage. Stroke 2015, 46, 1826–1831.
  34. Fang, Y.; Lu, J.; Zheng, J.; Wu, H.; Araujo, C.; Reis, C.; Lenahan, C.; Zhu, S.; Chen, S.; Zhang, J. Comparison of aneurysmal subarachnoid hemorrhage grading scores in patients with aneurysm clipping and coiling. Sci. Rep. 2020, 10, 1–9.
  35. Savarraj, J.P.; Hergenroeder, G.W.; Zhu, L.; Chang, T.; Park, S.; Megjhani, M.; Vahidy, F.S.; Zhao, Z.; Kitagawa, R.S.; Choi, H.A. Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage. Neurology 2021, 96, e553–e562.
  36. Ramos, L.A.; van der Steen, W.E.; Barros, R.S.; Majoie, C.B.; van den Berg, R.; Verbaan, D.; Marquering, H.A. Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage. J. Neurointerv. Surg. 2019, 11, 497–502.
  37. Megjhani, M.; Terilli, K.; Weiss, M.; Savarraj, J.; Chen, L.H.; Alkhachroum, A.; Roh, D.J.; Agarwai, S.; Connolly Jr, E.S.; Park, S.; et al. Dynamic detection of delayed cerebral ischemia: A study in 3 centers. Stroke 2021, 52, 1370–1379.
  38. Park, S.; Megjhani, M.; Frey, H.P.; Grave, E.; Wiggins, C.; Terilli, K.L.; Roh, D.J.; Velazquez, A.; Agarwal, S.; Elhadad, N.; et al. Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data. J. Clin. Monit. Comput. 2019, 33, 95–105.
  39. Taghavi, R.M.; Zhu, G.; Wintermark, M.; Kuraitis, G.M.; Sussman, E.S.; Pulli, B.; Biniam, B.; Ostmeier, S.; Steinberg, G.K.; Heit, J.J. Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach. Interv. Neuroradiol. J. Peritherapeutic Neuroradiol. Surg. Proced. Relat. Neurosci. 2023.
  40. Greving, J.P.; Wermer, M.J.; Brown, R.D., Jr.; Morita, A.; Juvela, S.; Yonekura, M.; Ishibashi, T.; Torner, J.C.; Nakayama, T.; Algra, A.; et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: A pooled analysis of six prospective cohort studies. Lancet Neurol. 2014, 13, 59–66.
  41. Greving, J.P.; Wermer, M.J.H.; Brown, R.D.; Morita, A.; Juvela, S.; Yonekura, M.; Ishibashi, T.; Torner, J.C.; Nakayama, T.; Rinkel, G.J.E.; et al. ELAPSS score for prediction of risk of growth of unruptured intracranial aneurysms. Neurology 2017, 88, 1600–1606.
  42. Backes, D.; Rinkel, G.J.; Greving, J.P.; Velthuis, B.K.; Murayama, Y.; Takao, H.; Ishibashi, T.; Igase, M.; Terbrugge, K.G.; Agid, R.; et al. The unruptured intracranial aneurysm treatment score: A multidisciplinary consensus. Neurology 2015, 85, 881–889.
  43. Staartjes, V.E.; Sebök, M.; Blum, P.G.; Serra, C.; Germans, M.R.; Krayenbühl, N.; Regli, L.; Esposito, G. Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: A pilot study. Acta Neurochir. 2020, 162, 2759–2765.
  44. Zanaty, M.; Park, B.J.; Seaman, S.C.; Cliffton, W.E.; Woodiwiss, T.; Piscopo, A.; Howard, M.A.; Abode-Iyamah, K. Predicting Chronic Subdural Hematoma Recurrence and Stroke Outcomes While withholding Antiplatelet and Anticoagulant Agents. Front. Neurol. 2020, 10, 1401.
  45. Labovitz, D.L.; Shafner, L.; Reyes Gil, M.; Virmani, D.; Hanina, A. Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy. Stroke 2017, 48, 1416–1419.
  46. Babel, A.; Taneja, R.; Mondello Malvestiti, F.; Monaco, A.; Donde, S. Artificial intelligence solutions to increase medication adherence in patients with non-communicable diseases. Front. Digit. Health 2021, 3, 669869.
  47. Mofatteh, M. Neurosurgery and artificial intelligence. AIMS Neurosci. 2021, 8, 477–495.
  48. Bellini, V.; Valente, M.; Del Rio, P.; Bignami, E. Artificial intelligence in thoracic surgery: A narrative review. J. Thorac. Dis. 2021, 13, 6963–6975.
  49. Deo, R.C. Machine Learning in Medicine. Circulation 2015, 132, 1920–1930.
  50. Iqbal, J.; Jahangir, K.; Mashkoor, Y.; Sultana, N.; Mehmood, D.; Ashraf, M.; Hafeez, M.H. The future of artificial intelligence in neurosurgery: A narrative review. Surg. Neurol. Int. 2022, 13, 536.
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