Application of deep learning for surgical decision support during single-incision laparoscopic cholecystectomy

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Single-incision laparoscopic cholecystectomy (SILC) offers benefits but poses higher risks of complications compared to conventional laparoscopic cholecystectomy.
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Expert surgeons can better navigate complex surgical anatomy, reducing risks during SILC through enhanced intraoperative judgment.
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Artificial intelligence, particularly Mask2Former, shows promise in improving real-time surgical guidance by identifying safe and hazardous dissection zones.
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Training deep learning models on SILC videos aims to enhance surgical decision-making by accurately recognizing critical anatomical zones.
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The study utilized a dataset of 186 SILC videos to develop AI models for improving safety and quality in laparoscopic cholecystectomy procedures.

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