Background. Subdural hematoma (SDH) is a common neurosurgical emergency, with increasing incidence in aging populations. Rapid and accurate identification is essential to guide timely intervention, yet existing automated tools focus primarily on detection and provide limited interpretability or spatial localization. There remains a need for transparent, high-performing systems that integrate multimodal clinical and imaging information to support real-time decision-making. Methods. We developed a multimodal deep-learning framework that integrates structured clinical variables, a 3D convolutional neural network trained on CT volumes, and a transformer-enhanced 2D segmentation model for SDH detection and localization. Using 25,315 head CT studies from Hartford HealthCare (2015--2024), of which 3,774 (14.9\%) contained clinician-confirmed SDH, tabular models were trained on demographics, comorbidities, medications, and laboratory results. Imaging models were trained to detect SDH and generate voxel-level probability maps. A greedy ensemble strategy combined complementary predictors. Findings. Clinical variables alone provided modest discriminatory power (AUC 0.75). Convolutional models trained on CT volumes and segmentation-derived maps achieved substantially higher accuracy (AUCs 0.922 and 0.926). The multimodal ensemble integrating all components achieved the best overall performance (AUC 0.9407; 95\% CI, 0.930--0.951) and produced anatomically meaningful localization maps consistent with known SDH patterns. Interpretation. This multimodal, interpretable framework provides rapid and accurate SDH detection and localization, achieving high detection performance and offering transparent, anatomically grounded outputs. Integration into radiology workflows could streamline triage, reduce time to intervention, and improve consistency in SDH management.

Viz Subdural Hematoma (SDH), a new artificial intelligence (AI)-powered algorithm for diagnosing subdural hemorrhages, has garnered 510(k) clearance from the Food and Drug Administration (FDA), according to Viz.ai, the manufacturer of the module. Noting a rising incidence of subdural hematomas, Viz.ai said the SDH algorithm demonstrated sensitivity and specificity rates of 94 and 92 percent respectively in a multicenter trial of over 500 patients.1,2 "The algorithm is very sensitive and specific, significantly increasing the number of subdural hemorrhages detected and ensuring patients receive the necessary follow-up from this potentially life-threatening disease," maintained Jayme Strauss, the chief clinical officer at Viz.ai. The company emphasizes that Viz SDH is currently the only AI-powered platform specifically geared to identifying and differentiating between acute and chronic subdural hemorrhages. Jason Davies, M.D., Ph.D., said this is a key benefit given the different treatment pathways for these conditions.