Enhancing anatomical recognition by surgeons during pelvic lymph node dissection using artificial intelligence
5 affiliations
- 1Department of Colorectal Surgery, National Cancer Center Hospital East
- 2AI Digital Device Innovation Office, National Cancer Center Hospital East
- 3Department of Urology, National Cancer Center Hospital East
- 4Department of Gynecology, National Cancer Center Hospital East
- 5Jmees Inc.
Abstract
The lateral pelvis is a critical anatomical region in colorectal, gynecological, and urological surgeries. However, its anatomical complexity and variability pose significant challenges for pelvic lymph node dissection (PLND). This study aimed to develop an artificial intelligence (AI) model to identify key anatomical structures relevant to PLND and evaluate whether AI assistance enhances surgeons’ ability to recognize pelvic anatomical features. Thirty-six surgeons representing colorectal, gynecological, and urological specialties, with varying experience levels, reviewed 640 video snippets (0.5 s each) from PLND procedures. The model was trained on 23,259 annotated and 653 unannotated images extracted from 293 PLND procedure videos. Threefold cross-validation yielded Dice similarity coefficients of 0.6483 for the ureter, 0.8654 for the obturator nerve, 0.8619 for the external iliac artery, and 0.8736 for the external iliac vein. Across all structures, AI assistance led to a significant improvement in sensitivity and specificity among participating surgeons (p < .001). Our findings suggest that the proposed AI model may assist surgeons in identifying pelvic anatomical structures across different specialties and experience levels. Further studies using continuous intraoperative workflows will be required to determine its impact on clinical practice.
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BibTeX
@article{kitaguchi2026enhancing,
title = {{Enhancing anatomical recognition by surgeons during pelvic lymph node dissection using artificial intelligence}},
author = {Daichi Kitaguchi and Shin Takenaka and Yasukazu Nakanishi and Shun Yamaguchi and Tomohiro Noda and Suguru Odajima and Junki Onishi and Yuki Koike and Kohei Hirose and Madoka Kataoka and Mitsumasa Homma and Atsushi Kouno and Hiroki Matsuzaki and Nobuyoshi Takeshita and Hitoshi Masuda and Hiroshi Tanabe and Yuichiro Tsukada},
journal = {npj Digital Medicine},
year = {2026},
month = jun,
doi = {10.1038/s41746-026-02936-4},
url = {https://doi.org/10.1038/s41746-026-02936-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - Enhancing anatomical recognition by surgeons during pelvic lymph node dissection using artificial intelligence
AU - Daichi Kitaguchi
AU - Shin Takenaka
AU - Yasukazu Nakanishi
AU - Shun Yamaguchi
AU - Tomohiro Noda
AU - Suguru Odajima
AU - Junki Onishi
AU - Yuki Koike
AU - Kohei Hirose
AU - Madoka Kataoka
AU - Mitsumasa Homma
AU - Atsushi Kouno
AU - Hiroki Matsuzaki
AU - Nobuyoshi Takeshita
AU - Hitoshi Masuda
AU - Hiroshi Tanabe
AU - Yuichiro Tsukada
PY - 2026
DA - 2026/06/01
JO - npj Digital Medicine
DO - 10.1038/s41746-026-02936-4
UR - https://doi.org/10.1038/s41746-026-02936-4
AB - The lateral pelvis is a critical anatomical region in colorectal, gynecological, and urological surgeries. However, its anatomical complexity and variability pose significant challenges for pelvic lymph node dissection (PLND). This study aimed to develop an artificial intelligence (AI) model to identify key anatomical structures relevant to PLND and evaluate whether AI assistance enhances surgeons’ ability to recognize pelvic anatomical features. Thirty-six surgeons representing colorectal, gynecological, and urological specialties, with varying experience levels, reviewed 640 video snippets (0.5 s each) from PLND procedures. The model was trained on 23,259 annotated and 653 unannotated images extracted from 293 PLND procedure videos. Threefold cross-validation yielded Dice similarity coefficients of 0.6483 for the ureter, 0.8654 for the obturator nerve, 0.8619 for the external iliac artery, and 0.8736 for the external iliac vein. Across all structures, AI assistance led to a significant improvement in sensitivity and specificity among participating surgeons (p < .001). Our findings suggest that the proposed AI model may assist surgeons in identifying pelvic anatomical structures across different specialties and experience levels. Further studies using continuous intraoperative workflows will be required to determine its impact on clinical practice.
SN - 2398-6352
ER -