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Published on: November 4, 2022
Machine learning-based detection of electrosurgical device-induced bleeding in laparoscopic videos of diaphragmatic
Vincent J Ribbens1,2, Simon C Baltus3,4, Can Ozan Tan2
1Surgery Department, Meander Medical Centre, Maatweg, 3818 TZ, Amersfoort, Utrecht, The Netherlands.
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.
