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Background <p>Electrosurgical devices provide significant advantages for tissue dissection in laparoscopic procedures. However, achieving optimal hemostasis while minimizing tissue coagulation is challenging. Monitoring device-induced bleeding will provide viable information for surgical skills assessment. We aimed to automatically detect bleeding induced by electrosurgical device use in laparoscopic videos using machine learning.</p> Methods <p>We present a two-step methodology for the automated detection of device-induced bleeding. First, based on the color representation, a random forest classifier (RFC) detects blood pixels in the frames before and after the electrosurgical device activation. Subsequently, a logistic regression (LR) model decides whether bleeding has occurred based on the change in blood pixels. The moments of device activations during surgery can be extracted automatically by a synchronized recording of the laparoscopic video and energy generator data. The RFC and LR were developed on the manual annotation of 34 images and 2678 video fragments from forty-five patients who underwent diaphragmatic hernia repair between May 2023 and October 2024. The performance of the RFC was evaluated by an 80/20 split for training and testing, while a stratified threefold cross-validation assessed the LR performance.</p> Results <p>The blood pixel detection showed an accuracy of 94% and a Dice score of 0.472. The classification of automatically extracted video fragments showed that device-induced bleeding can be detected with a 78.2% accuracy, 4.6% precision, 78.1% specificity, and 81.0% sensitivity.</p> Conclusion <p>The presented work on device-induced bleeding detection is a step toward quantifying the effect of electrosurgery use. We showed a machine learning-based methodology that accurately identifies video fragments of device activations without bleeding but struggles to identify bleeding precisely. Future work should focus on developing device-induced bleeding detection in a larger, more diverse dataset.</p>

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Machine learning-based detection of electrosurgical device-induced bleeding in laparoscopic videos of diaphragmatic hernia repair

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Vincent J. Ribbens,
  • Simon C. Baltus,
  • Can Ozan Tan,
  • Ivo A. M. J. Broeders

摘要

Background

Electrosurgical devices provide significant advantages for tissue dissection in laparoscopic procedures. However, achieving optimal hemostasis while minimizing tissue coagulation is challenging. Monitoring device-induced bleeding will provide viable information for surgical skills assessment. We aimed to automatically detect bleeding induced by electrosurgical device use in laparoscopic videos using machine learning.

Methods

We present a two-step methodology for the automated detection of device-induced bleeding. First, based on the color representation, a random forest classifier (RFC) detects blood pixels in the frames before and after the electrosurgical device activation. Subsequently, a logistic regression (LR) model decides whether bleeding has occurred based on the change in blood pixels. The moments of device activations during surgery can be extracted automatically by a synchronized recording of the laparoscopic video and energy generator data. The RFC and LR were developed on the manual annotation of 34 images and 2678 video fragments from forty-five patients who underwent diaphragmatic hernia repair between May 2023 and October 2024. The performance of the RFC was evaluated by an 80/20 split for training and testing, while a stratified threefold cross-validation assessed the LR performance.

Results

The blood pixel detection showed an accuracy of 94% and a Dice score of 0.472. The classification of automatically extracted video fragments showed that device-induced bleeding can be detected with a 78.2% accuracy, 4.6% precision, 78.1% specificity, and 81.0% sensitivity.

Conclusion

The presented work on device-induced bleeding detection is a step toward quantifying the effect of electrosurgery use. We showed a machine learning-based methodology that accurately identifies video fragments of device activations without bleeding but struggles to identify bleeding precisely. Future work should focus on developing device-induced bleeding detection in a larger, more diverse dataset.