Patient and procedure selection for mechanical thrombectomy: Toward personalized medicine and the role of artificial intelligence

Abstract

Mechanical thrombectomy (MT) for ischemic stroke due to large vessel occlusion is standard of care. Evidence-based guidelines on eligibility for MT have been outlined and evidence to extend the treatment benefit to more patients, particularly those at the extreme ends of a stroke clinical severity spectrum, is currently awaited. As patient selection continues to be explored, there is growing focus on procedure selection including the tools and techniques of thrombectomy and associated outcomes. Artificial intelligence (AI) has been instrumental in the area of patient selection for MT with a role in diagnosis and delivery of acute stroke care. Machine learning algorithms have been developed to detect cerebral ischemia and early infarct core, presence of large vessel occlusion, and perfusion deficit in acute ischemic stroke. Several available deep learning AI applications provide ready visualization and interpretation of cervical and cerebral arteries. Further enhancement of AI techniques to potentially include automated vessel probe tools in suspected large vessel occlusions is proposed. Value of AI may be extended to assist in procedure selection including both the tools and technique of thrombectomy. Delivering personalized medicine is the wave of the future and tailoring the MT treatment to a stroke patient is in line with this trend. K E Y W O R D Sartificial intelligence, mechanical thrombectomy, stroke significantly improved patient selection and delivery of acute stroke care. 12,13 In recent years, there has been a marked improvement in thrombectomy device technology and refinements in procedure techniques. In times of growing scientific interest and shifting focus on 798

iSPAN: Improved prediction of outcomes post thrombectomy with Machine Learning

“…Models such as XGB show state of the art performance on tabular data 27 and have been successful applied to smaller stoke databases 28 . There is also hope that machine learning can add a level of personalisation to decision making around thrombectomy 29 . However there is a large degree in heterogeneity in these studies 12,29 .…”

“…There is also hope that machine learning can add a level of personalisation to decision making around thrombectomy 29 . However there is a large degree in heterogeneity in these studies 12,29 . Many are also hampered by low sample size 26,28,[30][31][32] .…”

Artificial Intelligence in Public Health: Current Trends and Future Possibilities

“…Think, for example, of the opportunities presented by wearable monitoring, big data, robotic assistance, rehabilitation, and surgery. The applications of artificial intelligence have received growing interest in many sectors, such as in those related to organ, functional tissue, and cell diagnostics [3][4][5][6]; care robotics, which assist in interventions, rehabilitation, and supporting the communication and assistance of disabled people [11][12][13][14]; the biomedicine sector, where it is implemented in applications from genetics to modelling [15][16][17][18]; and precision and personalized biomedicine [19][20][21][22][23][24]. The consolidation of technologies based on artificial intelligence in the health domain is intended to bring benefits to everyone, from the stakeholder to the patient, in the form of equity of care.…”