Anatomically Localized Detection of Six Acute Abdominal Emergencies on CT Using Multi-window Deep Learning:...

The purpose of this study is to develop and retrospectively validate a deep learning system for the classification and anatomically interpretable localization of six acute abdominal emergencies on CT using multi-window Hounsfield Unit (HU) encoding. A publicly available national teleradiology dataset of 1274 patients (42,922 bounding box annotations) was used for training and internal validation (896/189/189 patient-level split). Each CT slice was encoded into three diagnostic HU windows (soft tissue, bone/stone, angio/liver). A YOLOv11-Large model with a stride-4 (P2) head was trained at $1280\times 1280$ . Localization was evaluated using the clinical nine-region abdominal grid, and specificity was assessed in 80 target-negative patients. External validation used a radiologist-adjudicated 280-patient Stanford Merlin cohort (US), with model weights and thresholds applied without modification. Internal macro AUROC was 0.941, and macro F1 was 76.1%. Nine-region localization accuracy was 99.5% among detected cases and 90.9% including missed detections. Specificity in the target-negative cohort was 86.2%. On the external Stanford Merlin cohort, macro AUROC was 0.879 with all six classes $\ge 0.80$ ; AAA reached F1 0.889 at frozen thresholds; macro F1 was 0.545, increasing to 0.648 after recalibration. A multi-window CT detection model classified six acute abdominal emergencies with high discrimination on internal testing and preserved moderate-to-high discrimination on an external Stanford Merlin cohort. Region-level evaluation using the nine-region abdominal grid provided a clinically interpretable localization endpoint complementary to conventional detection metrics. However, variable class-level F1, reduced external operating-point performance, and retrospective design indicate that multisite prospective validation and site-specific threshold calibration are required before clinical deployment.