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Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study

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Abstract

Background

The precise recognition of liver vessels during liver parenchymal dissection is the crucial technique for laparoscopic liver resection (LLR). This retrospective feasibility study aimed to develop artificial intelligence (AI) models to recognize liver vessels in LLR, and to evaluate their accuracy and real-time performance.

Methods

Images from LLR videos were extracted, and the hepatic veins and Glissonean pedicles were labeled separately. Two AI models were developed to recognize liver vessels: the “2-class model” which recognized both hepatic veins and Glissonean pedicles as equivalent vessels and distinguished them from the background class, and the “3-class model” which recognized them all separately. The Feature Pyramid Network was used as a neural network architecture for both models in their semantic segmentation tasks. The models were evaluated using fivefold cross-validation tests, and the Dice coefficient (DC) was used as an evaluation metric. Ten gastroenterological surgeons also evaluated the models qualitatively through rubric.

Results

In total, 2421 frames from 48 video clips were extracted. The mean DC value of the 2-class model was 0.789, with a processing speed of 0.094 s. The mean DC values for the hepatic vein and the Glissonean pedicle in the 3-class model were 0.631 and 0.482, respectively. The average processing time for the 3-class model was 0.097 s. Qualitative evaluation by surgeons revealed that false-negative and false-positive ratings in the 2-class model averaged 4.40 and 3.46, respectively, on a five-point scale, while the false-negative, false-positive, and vessel differentiation ratings in the 3-class model averaged 4.36, 3.44, and 3.28, respectively, on a five-point scale.

Conclusion

We successfully developed deep-learning models that recognize liver vessels in LLR with high accuracy and sufficient processing speed. These findings suggest the potential of a new real-time automated navigation system for LLR.

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Acknowledgements

We thank Kensaku Mori, Department of Intelligent Science, Graduate School of Informatics, Nagoya University, for developing and contributing to Nu-VAT as an annotation tool.

Funding

No funding sources were available for this study.

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Authors and Affiliations

  1. Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Norikazu Une, Shin Kobayashi, Taiki Sunakawa, Kimimasa Sasaki, Masashi Kudo, Motokazu Sugimoto & Naoto Gotohda

  2. Division of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Norikazu Une, Daichi Kitaguchi, Taiki Sunakawa, Kimimasa Sasaki, Tateo Ogane, Kazuyuki Hayashi, Norihito Kosugi, Hiro Hasegawa, Nobuyoshi Takeshita & Masaaki Ito

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  1. Norikazu Une
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  3. Daichi Kitaguchi
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Correspondence to Masaaki Ito.

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Disclosures

Norikazu Une, Shin Kobayashi, Daichi Kitaguchi, Taiki Sunakawa, Kimimasa Sasaki, Tateo Ogane, Kazuyuki Hayashi, Norihito Kosugi, Masashi Kudo, Motokazu Sugimoto, Hiro Hasegawa, Nobuyoshi Takeshita, Naoto Gotohda, and Masaaki Ito have no conflicts of interest to declare.

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Une, N., Kobayashi, S., Kitaguchi, D. et al. Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study. Surg Endosc 38, 1088–1095 (2024). https://doi.org/10.1007/s00464-023-10637-2

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