Development of a deep-learning model to detect free air on abdominal computed tomography for surgical decision support.

Background: Free air (FA) in the abdominal cavity is a critical finding requiring prompt surgical intervention. We developed an AI-based segmentation model, Free Air-Net (FA-NET), to detect FA in abdominal computed tomography (CT) scans and further refined it with negative training to create FA-NET-NT, aiming to reduce false positives. Materials and methods: FA-NET-NT was developed using a retrospective dataset from a single institution ( Results: Both models achieved high Dice scores (0.87). FA-NET-NT improved specificity (96%) while maintaining high sensitivity (85%) in image-wise analysis. In patient-wise analysis, FA-NET-NT achieved 95-96% sensitivity for ulcer perforation and 82-92% specificity for non-FA conditions (cholecystitis, pancreatitis, and appendicitis). Specificity for ileus remained moderate (62%). In the external validation, the model demonstrated a patient-wise sensitivity of 95% for ulcer perforation. High specificity was maintained against differential diagnoses, including appendicitis (88%), pancreatitis (88%), cholecystitis (82%), and ileus (80%). Most false-positive findings were attributable to physiological bowel gas mimicking FA. Conclusion: FA-NET-NT is a robust decision-support tool for detecting FA, with its generalizability confirmed through multi-institutional validation. To provide definitive evidence of its clinical superiority, further prospective multi-center trials are necessary in emergency settings.