Anatomically Localized Detection of Six Acute Abdominal Emergencies on CT Using Multi-window Deep Learning: Development and Validation
An AI system accurately identifies and pinpoints six life-threatening abdominal emergencies on CT scans, potentially speeding emergency diagnosis.
This study developed a YOLOv11-based detection system using three-window HU encoding to simultaneously classify and anatomically localize six acute abdominal emergencies on CT, achieving 94% macro AUROC internally and preserving 88% on external Stanford validation with frozen thresholds. Region-level localization accuracy of 99.5% provides clinically interpretable output, though variable class-level F1 and retrospective design require prospective multicenter validation before clinical deployment.
What the study was
- Study design
- retrospective_development_external_validation
- Category
- ai_ml_clinical_diagnostics
- Maturity
- Validated
- Journal
- J Imaging Inform Med
Why it surfaced
Multi-class abdominal emergency detection with external validation at Stanford and clinically interpretable nine-region localization; addresses high-acuity diagnostic bottleneck; demonstrates model generalizability with frozen thresholds; variable F1 and retrospective design are appropriate caveats.
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