Optimization of an artificial neural network for predicting stress in robot-assisted laparoscopic surgery based on...

This study aims to optimize tunable hyperparameters of the multilayer perceptron (MLP) setup. The optimization procedure is aimed at more accurately predicting potential health risks to the surgeon during robotic-assisted surgery (RAS).

Data related to physiological parameters (electrodermal activity-EDA, blood pressure and body temperature) were collected during twenty RAS sessions completed by nine surgeons with different levels of experience. Once the dataset was generated, two preprocessing techniques (scaling and normalized) were applied. These datasets were divided into two subsets: with 80% data for training and cross-validation and 20% for testing. MLP was selected as the prediction technique. Three MLP hyperparameters were selected for optimization: number of epochs, learning rate and momentum. A central composite design (CCD) was applied with a full factorial design with five center points, with 31 combinations for each dataset. Once the models were generated on the training dataset, the optimized models were selected and then validated on the cross-validation and test datasets.

The optimized models were generated with an optimal number of epochs (500), the most applied learning rate was 0.01 and the most applied momentum was 0.05. These results showed significant improvement for EDA (R² = 0.9722), blood pressure (R² = 0.9977) and body temperature (R² = 0.9941).

MLP parameters have been successfully optimized, and the enhanced models were successfully validated on cross-validation and test datasets. This fact invites us to optimize different AI techniques that could improve results in clinical practice.