**4.4 Stroke**

In the past decade, deep learning applications in stroke imaging have dramatically risen, likely as a byproduct of higher stroke imaging volume with the arrival of endovascular thrombectomy in addition to the increasing acknowledgement of the emergent nature of the disease process [58]. DL applications to stroke imaging can be divided into three areas: (1) Alberta Stroke Program Early CT Score (ASPECTS) measurement, (2) large vessel occlusion (LVO) detection, and (3) infarct prognostication.

ASPECTS is a 10-point topographical quantitative grading scale widely used to guide acute stroke treatment by measuring 10 regions within the middle cerebral artery (MCA) territory for early signs of ischemia [70, 71]. Many commercial DL tools designed to perform automated ASPECTS evaluation have been tested in clinical settings, demonstrating variable results. One study found that three neuroradiologists showed a higher correlation with infarct core than e-ASPECTS (Brainomix) (r = 0.71, 0.76, 0.80, compared to 0.59) while another study found that RAPID ASPECTS (iSchemaView) displayed higher correlation than two neuroradiologists from between symptom onset and imaging until 4 hours post-symptom [72, 73]. These results suggest that automated ASPECTS evaluation may continue to be implemented as an adjunct to current neuroradiological diagnostics. The efficacy of ASPECTS analysis depends on the software utilized and established ground truth.

Early identification of large vessel occlusion (LVO) in the early stages of admission can mitigate the probability of the patient suffering from the long-term implications of stroke and rescue life. A 2019 study developed a U-Net architecture DL tool designed to detect the hyperdense MCA sign in noncontrast head CT scans from a local Hong Kong population and achieved a high sensitivity (.930), though relatively lower specificity accuracy and AUC [74]. Automated LVO detection on CT angiograms (CTA) has become integral to many stroke centers. Viz-AI, a commercial CNN-based solution, has demonstrated 82% sensitivity and 94% specificity for LVO detection [71].

The ability to accurately and reliably predict posttreatment stroke outcomes can aid the neurosurgeon in selecting patients for thrombectomy or other

neuroendovascular procedures and developing a plan of care precisely tailored to the individual patient. Recent stroke thrombectomy trials utilizing automated perfusion CT and MR imaging have revolutionized the modern care of stroke patients. The now commercially available Rapid.AI perfusion product, which employs a threshold-based segmentation method, resulted in a 3-fold reduction in severe disability and death when used to select patients for thrombectomy [75]. However, CT perfusion (CTP) maps have historically been unreliable and threshold-based approaches may fail to fully capture the complexity of infarct evolution. Processing this data under a DL system, one can take into account other biomarkers and patient-specific factors for better prognostication. One study validated a CNN designed to identify and predict post-treatment MRI final lesion volume, achieving a modified ROC-AUC of 0.88 [76]. Nishi et al. used a U-Net DL tool to assess clinical post-treatment outcomes of LVO patients using pretreatment diffusion-weighted image data of patients who underwent mechanical thrombectomy, finding an ROC-AUC of 0.81 [77].
