Resumen de Implementation of a neural-network-based Surgical Workflow Analysis algorithm for surgical educational environments
C. Jiménez Torres, C. Guzmán García, P. Sánchez González, I. Oropesa, Enrique Javier Gómez Aguilera
The automatic analysis of surgical workflow plays an important role in the modeling of surgical processes. Although current automatic approaches to recognize surgical procedures’ phases achieve accuracies varying from 0.8 and 0.9, they do not usually analyze their performance per phase, which reports relevant data on their strong and weak points. In this study, an innovative algorithm is implemented to classify the different phases of a laparoscopic cholecystectomy, following an approach based on neural networks and probabilistic methods, and analyzing the model performance for each phase. The best results were achieved using a pre-trained convolutional neural network, VGG-16, in combination with a Hidden Markov Model (accuracy = 0.91). The potential of this algorithm may further be explored for the definition of training and assessment systems of surgeons’ cognitive and decision-making skills.
