SAHVAI-3D and 4D: A New, Automated Subarachnoid Hemorrhage Volumetric Artificial Intelligence (SAHVAI) Measurement Approach Using Non-Contrast Head CT Scans.

ObjectivesTo automate subarachnoid hemorrhage volume (SAHV) calculation (SAHVAI-SAHV Artificial Intelligence) and create 3D volumetric images (SAHVAI-3D) using non-contrast head CT (NCCT) imaging data in aneurysmal subarachnoid hemorrhage (SAH) patients. We also defined SAHVAI-4D, representing SAHV over time. The aim was to compare automated SAHVAI volumes to manual SAHV methods and computation times, explore these imaging biomarkers potential in identifying at-risk brain regions for delayed cerebral ischemia (DCI), and explore potential insights in future neurotherapeutic interventions for SAH patient recovery. MethodsA training set of 10 consecutive aneurysmal SAH cases was used to manually compute SAHV, SAHVAI-3D, and SAHVAI-4D, involving 92 non-contrast CT scans (182 slices each). The SAHVAI deep learning (DL) algorithm generated automated SAHV values in cubic centimeters (cc). For both SAHVAI and manual evaluations, a 3D SAH brain map was created for each patient. Blood volumetric outputs were analyzed and compared to neurological outcomes at discharge, including DCI events, symptomatic vasospasm (sVSP), and areas with the thickest SAH blood concentration. ResultsSAHVAI quantified SAH blood volume (SAHV) in average of 6.7 seconds per scan, significantly faster than the manual method, which took over 60 minutes per scan (Fishers exact test, P value <0.001). SAHVAI demonstrated an accuracy of 99.8%, a Dice score of 0.701, a false positive rate of 0.0005, and a negative predictive value of 0.999. The mean absolute error between SAHVAI and manual methods was 5.67 ml. The SAHVAI-3D brain map and total SAHV at admission were strongly associated with neurological outcomes, inversely with Glasgow coma scale (R2=0.23, p=0.017) and directly with length of hospital stay (R2=0.175, p=0.004), especially in regions with dense blood concentration. ConclusionSAHVAI-3D and SAHVAI-4D brain mapping techniques represent innovative imaging biomarkers for SAH. These advancements enable rapid evaluation and targeted interventions, potentially improving patient care in SAH management.