SAHVAI-3D and 4D: Automated AI Volumetric Measurement of Subarachnoid Hemorrhage on Noncontrast Head CT

We automated subarachnoid hemorrhage volume (SAHV) calculation with artificial intelligence (SAHVAI) and created 3-dimensional volumetric images (SAHVAI-3D) using noncontrast head computed tomography data for patients with aneurysmal SAH. We also defined 4-dimensional SAHVAI (SAHVAI-4D), representing SAHV over time. We aimed to compare automated SAHVAI values and computational times to manual SAHV measurement methods, explore the potential of imaging biomarkers to identify at-risk brain regions for delayed cerebral ischemia and explore potential insights in future neurotherapeutic interventions for patient recovery after SAH.

A training set of 10 consecutive patients with aneurysmal SAH was used to manually compute SAHV, SAHVAI-3D, and SAHVAI-4D, including 92 noncontrast computed tomography scans (182 slices each). The SAHVAI deep learning algorithm generated automated SAHV values in cubic centimeters. A 3-dimensional SAH brain map was created for each patient for the SAHVAI and manual evaluations. Blood volumetric outputs were analyzed and compared to neurologic outcomes at discharge, including delayed cerebral ischemia events, symptomatic vasospasm, and areas with the thickest SAH blood concentration.

SAHVAI quantified SAHV in a mean of 6.7 seconds per scan, significantly faster than the manual method, which took >60 minutes per scan (Fisher exact test, P<0.001). SAHVAI demonstrated an accuracy of 99.8%, Dice score of 0.701, false-positive rate of 0.0005, and negative predictive value of 0.999. The mean absolute error between SAHVAI and manual methods was 5.67 mL. SAHVAI-3D brain map and total SAHV at admission were inversely associated with Glasgow Coma Scale (R² = 0.23, P = 0.017) and directly associated with length of hospital stay (R² = 0.175, P = 0.004), especially in regions with dense blood concentration.

SAHVAI-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.