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Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence

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A Correction to this article was published on 24 October 2022

This article has been updated

Abstract

Background

Artificial intelligence (AI) has been largely investigated in the field of surgery, particularly in quality assurance. However, AI-guided navigation during surgery has not yet been put into practice because a sufficient level of performance has not been reached. We aimed to develop deep learning-based AI image processing software to identify the location of the recurrent laryngeal nerve during thoracoscopic esophagectomy and determine whether the incidence of recurrent laryngeal nerve paralysis is reduced using this software.

Methods

More than 3000 images extracted from 20 thoracoscopic esophagectomy videos and 40 images extracted from 8 thoracoscopic esophagectomy videos were annotated for identification of the recurrent laryngeal nerve. The Dice coefficient was used to assess the detection performance of the model and that of surgeons (specialized esophageal surgeons and certified general gastrointestinal surgeons). The performance was compared using a test set.

Results

The average Dice coefficient of the AI model was 0.58. This was not significantly different from the Dice coefficient of the group of specialized esophageal surgeons (P = 0.26); however, it was significantly higher than that of the group of certified general gastrointestinal surgeons (P = 0.019).

Conclusions

Our software’s performance in identification of the recurrent laryngeal nerve was superior to that of general surgeons and almost reached that of specialized surgeons. Our software provides real-time identification and will be useful for thoracoscopic esophagectomy after further developments.

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Acknowledgements

We greatly appreciate the members of the Division of Esophageal Surgery for their critical discussion of our manuscript. We thank the members of the Surgical Device Innovation Office for reviewing and discussing the study. We also thank Kelly Zammit, BVSc and Coren Walters-Stewart, PhD, from Edanz (https://jp.edanz.com/ac), for editing a draft of this manuscript.

Funding

This work was supported by the National Cancer Center Research and Development Foundation, Budding Researchers Program [Grant Number 2020-S-3]. Drs. Kazuma Sato, Takeo Fujita, Hiroki Matsuzaki, and Nobuyoshi Takeshita are co-founders of Interpretable AI.

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Authors and Affiliations

  1. Division of Esophageal Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Kazuma Sato, Takeo Fujita & Hisashi Fujiwara

  2. Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, Tokyo, 163-8001, Japan

    Kazuma Sato & Shuichi Mitsunaga

  3. Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan

    Hiroki Matsuzaki & Nobuyoshi Takeshita

  4. Division of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan

    Shuichi Mitsunaga

  5. Division of Biomarker Discovery, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Chiba, Japan

    Shuichi Mitsunaga

  6. Division of Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa, Japan

    Takashi Kojima

  7. Graduate School of Informatics, Nagoya University School of Medicine, Nagoya, Aichi, Japan

    Kensaku Mori

  8. Division of Esophageal Surgery, National Cancer Center Hospital, Tokyo, Japan

    Hiroyuki Daiko

Authors
  1. Kazuma Sato
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  2. Takeo Fujita
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Corresponding author

Correspondence to Takeo Fujita.

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Disclosures

Kazuma Sato, Takeo Fujita, Hiroki Matsuzaki, Nobuyoshi Takeshita, Hisashi Fujiwara, Shuichi Mistunaga, Takashi Kojima, Kensaku Mori, Hiroyuki Daiko have no conflicts of interest or financial ties to disclose.

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Sato, K., Fujita, T., Matsuzaki, H. et al. Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence. Surg Endosc 36, 5531–5539 (2022). https://doi.org/10.1007/s00464-022-09268-w

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