Automatic Smoke Analysis in Minimally Invasive Surgery by Image-Based Machine Learning.

Introduction: Minimally Invasive Surgery uses electrosurgical tools that generate smoke. This smoke reduces the visibility of the surgical site and spreads harmful substances with potential hazards for the surgical staff. Automatic image analysis may provide assistance. However, the existing studies are restricted to simple clear versus smoky image classification.

Materials And Methods: We propose a novel approach using surgical image analysis with machine learning, including deep neural networks. We address three tasks: 1) smoke quantification, which estimates the visual level of smoke, 2) smoke evacuation confidence, which estimates the level of confidence to evacuate smoke, and 3) smoke evacuation recommendation, which estimates the evacuation decision. We collected three datasets with expert annotations. We trained end-to-end neural networks for the three tasks. We also created indirect predictors using task 1 followed by linear regression to solve task 2 and using task 2 followed by binary classification to solve task 3.

Results: We observe a reasonable inter-expert variability for tasks 1 and a large one for tasks 2 and 3. For task 1, the expert error is 17.61 percentage points (pp) and the neural network error is 18.45 pp. For tasks 2, the best results are obtained from the indirect predictor based on task 1. For this task, the expert error is 27.35 pp and the predictor error is 23.60 pp. For task 3, the expert accuracy is 76.78% and the predictor accuracy is 81.30%.

Conclusions: Smoke quantification, evacuation confidence, and evaluation recommendation can be achieved by automatic surgical image analysis with similar or better accuracy as the experts.