Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy

Abstract

BACKGROUND: Robotic-assisted minimally invasive esophagectomy (RAMIE) is a complex surgical procedure for treating esophageal cancer. Artificial intelligence (AI) is an uprising technology with increasing applications in the surgical field. This scoping review aimed to assess the current AI applications in RAMIE, with a focus on intraoperative video analysis. METHODS: To identify all articles utilizing AI in RAMIE, a comprehensive literature search was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis for scoping reviews of Medline and Embase databases and the Cochrane Library. Two independent reviewers assessed articles for quality and inclusion. RESULTS: One hundred and seventeen articles were identified, of which four were included in the final analysis. Results demonstrated that the main AI applications in RAMIE were intraoperative video assessment and the evaluation of surgical technical skills to evaluate surgical performance. AI was also used for surgical phase recognition to support clinical decision-making through intraoperative guidance and identify key anatomical landmarks. Various deep-learning networks were used to generate AI models, and there was a strong emphasis on using high-quality standardized video frames. CONCLUSIONS: The use of AI in RAMIE, especially in intraoperative video analysis and surgical phase recognition, is still a relatively new field that should be further explored. The advantages of using AI algorithms to evaluate intraoperative videos in an automated manner may be harnessed to improve technical performance and intraoperative decision-making, achieve a higher quality of surgery, and improve postoperative outcomes.

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