ArticleSurgical endoscopy2026
Pilot feasibility study of deep learning-based detection of malignant liver lesions in intraoperative ultrasound imaging.
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
backgroundIntraoperative ultrasound plays a central role in hepatobiliary surgery for the detection of liver lesions, but image interpretation is challenging and operator-dependent. Evidence supporting artificial intelligence-based detection tools in this setting remains limited. This study was designed as a preliminary pilot feasibility study of a future intraoperative support system for decision-making.
methodsA retrospective dataset of 1,035 intraoperative ultrasound images containing malignant liver lesions was collected and manually annotated by an experienced hepatobiliary surgeon. Images were divided into a training set (n = 935) and a validation set (n = 100), comprising 114 lesions. Because this was an exploratory pilot study, all malignant lesions were analyzed as a single detection class. Three detection strategies were evaluated: a two-stage convolutional architecture (Cascade R-CNN), a single-stage detector (YOLO11), and a transformer-based model (RF-DETR). Performance was assessed using sensitivity, precision, and mean average precision (mAP).
resultsYOLO11 Medium achieved the highest sensitivity (63%), detecting 72 of 114 lesions while maintaining a precision of 62% and rapid offline inference. RF-DETR demonstrated the highest precision (80%) and superior localization accuracy (mAP@0.50 approximately 70%), with sensitivity close to 60%. Cascade R-CNN showed balanced but inferior performance compared with YOLO11 and RF-DETR.
conclusionAutomated detection of malignant liver lesions in intraoperative ultrasound appears feasible in the preliminary pilot study. However, the observed sensitivity remains insufficient for autonomous intraoperative decision-making. YOLO11 and RF-DETR showed complementary strengths in sensitivity and precision, respectively, and should be regarded as exploratory support tools requiring further training, patient-level validation, and external multicenter evaluation before clinical implementation.
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