Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning

Aoyama, Yoshimasa; Matsunobu, Yusuke; Etoh, Tsuyoshi; Suzuki, Kosuke; Fujita, Shunsuke; Aiba, Takayuki; Fujishima, Hajime; Empuku, Shinichiro; Kono, Yohei; Endo, Yuichi; Ueda, Yoshitake; Shiroshita, Hidefumi; Kamiyama, Toshiya; Sugita, Takemasa; Morishima, Kenichi; Ebe, Kohei; Tokuyasu, Tatsushi; Inomata, Masafumi

doi:10.1007/s00464-024-11117-x

Surg Endosc

2024

DOI: 10.1007/s00464-024-11117-x

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Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning

Yoshimasa Aoyama,

Yusuke Matsunobu,

Tsuyoshi Etoh

et al.

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Artificial Intelligence in Surgery: A Systematic Review of Use and Validation

Kenig,

Monton Echeverria,

Muntaner Vives

2024

JCM

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Background: Artificial Intelligence (AI) holds promise for transforming healthcare, with AI models gaining increasing clinical use in surgery. However, new AI models are developed without established standards for their validation and use. Before AI can be widely adopted, it is crucial to ensure these models are both accurate and safe for patients. Without proper validation, there is a risk of integrating AI models into practice without sufficient evidence of their safety and accuracy, potentially leading to suboptimal patient outcomes. In this work, we review the current use and validation methods of AI models in clinical surgical settings and propose a novel classification system. Methods: A systematic review was conducted in PubMed and Cochrane using the keywords “validation”, “artificial intelligence”, and “surgery”, following PRISMA guidelines. Results: The search yielded a total of 7627 articles, of which 102 were included for data extraction, encompassing 2,837,211 patients. A validation classification system named Surgical Validation Score (SURVAS) was developed. The primary applications of models were risk assessment and decision-making in the preoperative setting. Validation methods were ranked as high evidence in only 45% of studies, and only 14% of the studies provided publicly available datasets. Conclusions: AI has significant applications in surgery, but validation quality remains suboptimal, and public data availability is limited. Current AI applications are mainly focused on preoperative risk assessment and are suggested to improve decision-making. Classification systems such as SURVAS can help clinicians confirm the degree of validity of AI models before their application in practice.

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Artificial Intelligence in Surgery: A Systematic Review of Use and Validation

Kenig,

Monton Echeverria,

Muntaner Vives

2024

JCM

View full text Add to dashboard Cite

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Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning

Cited by 1 publication

References 35 publications

Artificial Intelligence in Surgery: A Systematic Review of Use and Validation

Artificial Intelligence in Surgery: A Systematic Review of Use and Validation

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