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Background <p>Precise recognition of anatomical structures such as the pancreas, transverse mesocolon, and right gastroepiploic vein (RGEV) is critical for the safety and effectiveness of infrapyloric lymph node dissection during robotic distal gastrectomy (RDG). Misrecognizing these structures can increase the risk of complications. However, even expert surgeons may encounter difficulties identifying these structures due to variations in patient anatomy and surgical technique. This study aims to validate the performance of an artificial intelligence (AI) model for automated anatomical recognition during RDG across multiple institutions and evaluate its clinical utility in supporting surgeons.</p> Methods <p>Surgical videos from 90 patients who underwent RDG at multiple high-volume centers in Japan were analyzed. The AI model’s accuracy was evaluated using Intersection over Union (IoU) for the pancreas, transverse mesocolon, and RGEV across four high-volume institutions. The clinical utility of the AI model was assessed by comparing the accuracy of surgeons in identifying anatomical landmarks during infrapyloric lymph node dissection between procedures performed with and without AI assistance.</p> Results <p>During this surgical procedure, the IoU of our AI model for the pancreas, transverse mesocolon, and RGEV were 0.624, 0.575, and 0.618, respectively. Furthermore, despite differences in accuracy across the four facilities, the AI model achieved consistent IoU values ranging from 0.5 to 0.7 across all institutions, demonstrating minimal variability. AI-assisted surgeons showed significantly higher accuracy in recognizing anatomical landmarks, particularly the transverse mesocolon’s edge, compared with those without AI assistance (91.7% vs. 80.6%, <i>p</i> = 0.043). These findings highlight the model’s ability to enhance the safety and precision of infrapyloric lymph node dissection.</p> Conclusion <p>The AI model demonstrated strong external validity and improved surgeons’ recognition of anatomical structures during RDG. Its integration into clinical practice can potentially reduce the rate of surgical complications and improve patient outcomes.</p> Graphical Abstract <p></p>

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Assessment of AI-driven anatomical recognition in robotic gastrectomy: A multicenter retrospective analysis

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Jumpei Ikeda,
  • Masashi Takeuchi,
  • Hirofumi Kawakubo,
  • Masahiro Yura,
  • Takahiro Kinoshita,
  • Atsushi Morito,
  • Souya Nunobe,
  • Yosuke Morimoto,
  • Tomohisa Egawa,
  • Tasuku Furube,
  • Yusuke Maeda,
  • Satoru Matsuda,
  • Yuko Kitagawa

摘要

Background

Precise recognition of anatomical structures such as the pancreas, transverse mesocolon, and right gastroepiploic vein (RGEV) is critical for the safety and effectiveness of infrapyloric lymph node dissection during robotic distal gastrectomy (RDG). Misrecognizing these structures can increase the risk of complications. However, even expert surgeons may encounter difficulties identifying these structures due to variations in patient anatomy and surgical technique. This study aims to validate the performance of an artificial intelligence (AI) model for automated anatomical recognition during RDG across multiple institutions and evaluate its clinical utility in supporting surgeons.

Methods

Surgical videos from 90 patients who underwent RDG at multiple high-volume centers in Japan were analyzed. The AI model’s accuracy was evaluated using Intersection over Union (IoU) for the pancreas, transverse mesocolon, and RGEV across four high-volume institutions. The clinical utility of the AI model was assessed by comparing the accuracy of surgeons in identifying anatomical landmarks during infrapyloric lymph node dissection between procedures performed with and without AI assistance.

Results

During this surgical procedure, the IoU of our AI model for the pancreas, transverse mesocolon, and RGEV were 0.624, 0.575, and 0.618, respectively. Furthermore, despite differences in accuracy across the four facilities, the AI model achieved consistent IoU values ranging from 0.5 to 0.7 across all institutions, demonstrating minimal variability. AI-assisted surgeons showed significantly higher accuracy in recognizing anatomical landmarks, particularly the transverse mesocolon’s edge, compared with those without AI assistance (91.7% vs. 80.6%, p = 0.043). These findings highlight the model’s ability to enhance the safety and precision of infrapyloric lymph node dissection.

Conclusion

The AI model demonstrated strong external validity and improved surgeons’ recognition of anatomical structures during RDG. Its integration into clinical practice can potentially reduce the rate of surgical complications and improve patient outcomes.

Graphical Abstract