ArticleSurgical endoscopy2026
Deep learning-based automatic identification model of main pancreatic duct in intraoperative ultrasonography.
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPancreatic enucleation (PE) preserves parenchyma in benign/low-grade malignant pancreatic tumors but risks postoperative pancreatic fistula (POPF) if the main pancreatic duct (MPD) is injured. Intraoperative ultrasound (IOUS) guides MPD localization, yet its efficacy is limited by operator dependency and expertise gaps. This study developed a deep learning (DL) model for automated MPD identification during IOUS to enhance surgical precision and reduce POPF risk.
methodsA retrospective dataset included IOUS images/videos from 166 patients undergoing minimally invasive PE (2018-2023). Images were manually annotated with MPD bounding boxes by experienced surgeons, categorized as positive (MPD present) or negative. Data augmentation balanced positive-to-negative ratios (1:1), yielding training (44,702 images), internal validation (447 images), and test sets. External validation used 408 images and 16 video clips, including 10 videos from the other institution. The Fast R-CNN architecture, pretrained on the COCO dataset, was finetuned using stochastic gradient descent optimization. Performance was evaluated via recall rate and accuracy (Intersection over Union threshold: 50%), assessed by video annotation speed (frames per second, fps).
resultsThe model achieved high recall rates: 98.7% (internal), 97.8% (external images), 96.5% (video validation) and 94.8% (additional external video validation), with accuracies of 93.9, 84.2, 83.1, and 82.0%, respectively. Annotation averaged 3.8 fps, enabling seamless integration into surgical workflows. False positives were predominant (73.3% with confidence > 0.8 in internal validation), often involving multiple detections per frame (80%).
conclusionsThis DL-based IOUS system enables accurate MPD identification, mitigating operator variability and supporting surgical training. While false positives require refinement, the model enhances safety in PE by preventing MPD injury. Future integration into clinical ultrasound platforms and optimization for postoperative imaging contexts are warranted.
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