DEPLOYING COMPUTER VISION DETECTION METHOD IN MEDICAL SIMULATION TRAINING USING MACHINE LEARNING

Abstract

A machine learning (ML) object detection algorithm was developed to replace the original color-based image detection algorithm for the Dynamic Haptic Robotic Trainer Plus (DHRT+). This image recognition system was used for medical training in Central Venous Catheterization (CVC). This image tracking allows for the training system to provide accurate performance feedback to the user during the training process. The ML object detection algorithm was developed and evaluated using training data. The results indicate that increasing the training data set improves the detection system’s accuracy. The system was found to have an overall precision rate of 90.9% and a recall rate of 81.69%. This new ML model will be implemented into the DHRT+ system and used to train medical residents.