ArticleSurgical endoscopy2026
Application of deep learning for surgical decision support during single-incision laparoscopic cholecystectomy.
Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop an Artificial intelligence (AI) model based on Mask2Former to identify safe and hazard zones during Single-incision laparoscopic cholecystectomy (SILC).
methods186 videos of SILC were included in the study, among which, 127 videos were used for training and the other 59 videos were used for testing the AI models. The model's performance was evaluated using Intersection over Union (IOU), Dice Score, F1 Score, Accuracy, Sensitivity, and Positive Predictive Value (PPV).
resultsA total of 186 videos with 9521 frames performed by two surgeons from two institutions were annotated simultaneously by an expert panel of 3 surgeons. The model achieved a high level of F1 score (0.966), IOU (0.805), Sensitivity (0.968) and PPV (0.942) for safe zones. For hazard zones, the model exhibited an exceptionally high Sensitivity (0.970), while the pixel-level overlap metrics for hazard zones (Dice = 0.825, IOU = 0.770) were lower than the object-level F1 score (0.923).
conclusionsThe proposed model could be used to identify safe and hazard zones of dissection during SILC, with high sensitivity and PPV for safe zones and high sensitivity for hazard zones. This technology might eventually be used to provide real-time guidance intraoperatively to minimize the risk of major bile duct injury during SILC.
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