Enhancing Surgical Gesture Recognition Using Bidirectional LSTM and Evolutionary Computation: A Machine Learning Approach to Improving Robotic-Assisted Surgery

Abstract

The integration of artificial intelligence (AI) and machine learning in the medical field has led to significant advancements in surgical robotics, particularly in enhancing the precision and efficiency of surgical procedures. This thesis investigates the application of a single-layer bidirectional Long Short-Term Memory (BiLSTM) model to the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) dataset, aiming to improve the recognition and classification of surgical gestures. The BiLSTM model, with its capability to process data in both forward and backward directions, offers a comprehensive analysis of temporal sequences, capturing intricate patterns within surgical motion data. This research explores the potential of BiLSTM models to outperform traditional unidirectional models in the context of robotic surgery.

In addition to the core model development, this study employs evolutionary computation techniques for hyperparameter tuning, systematically searching for optimal configurations to enhance model performance. The evaluation metrics include training and validation loss, accuracy, confusion matrices, prediction time, and model size. The results demonstrate that the BiLSTM model with evolutionary hyperparameter tuning achieves superior performance in recognizing surgical gestures compared to standard LSTM models.

The findings of this thesis contribute to the broader field of surgical robotics and human-AI partnership by providing a robust method for accurate gesture recognition, which is crucial for assessing and training surgeons and advancing automated and assistive technologies in surgical procedures. The improved model performance underscores the importance of sophisticated hyperparameter optimization in developing high-performing deep learning models for complex sequential data analysis.