Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease - Gilotra, Kevin; Swarna, Sujith; Mani, Racheed; Basem, Jade; Dashti, Reza (Frontiers in Human Neuroscience, 2023)
Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing management including neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease. We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic and hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, a detailed analysis of their accuracy and effectiveness was provided. The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of cerebrovascular pathologies. For ischemic and hemorrhagic strokes, few commercial AI software platforms have been implemented into clinical practice. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.