Machine Learning-Based Indirect Tip Force Sensing and Estimation for Robotic Uterine Manipulation System

Robotic-assisted Minimally Invasive Surgery (RMIS) has advanced laparoscopic gynecological procedures by improving precision and reducing invasiveness. However, the lack of direct force sensing during uterine manipulation remains a challenge, potentially increasing the risk of tissue damage and compromising surgical accuracy. This study presents an indirect force estimation method for robotic uterine manipulators using machine learning techniques to predict tip forces from handle force measurements. A dataset was developed by systematically varying uterine weight and ligament resistance, simulated through elastic bands of different colors and lengths. Three regression models—Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Support Vector Regression (SVR)—were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination $(\text {R}^{2})$ . All models demonstrated reliable performance across diverse testing conditions, with XGBoost and SVR achieving a balance of predictive accuracy and computational efficiency, while LSTM effectively leveraged temporal dependencies. These findings underscore the viability of machine learning-based indirect force sensing as a practical alternative in scenarios where direct measurement is infeasible, paving the way for improved haptic feedback and surgical safety.