An Intelligent System for Surgical Tools Checking, using Vision and Deep Learning

Abstract Objective: Currently, Operating Rooms (OR) check surgical tools manually before and after every surgery and sterilisation, making the process tedious. In addition, each hospital’s OR teams have different work processes, making standardisation challenging and staff training obsolete as they move from one hospital to another. Coupled with manpower challenges in healthcare, this makes ORs significantly inefficient. We propose a standardised system which leverages on Deep Learning to increase inter-organisational effectiveness by expediting tool accounting. Photos of surgical tools were taken, making up a dataset which we trained with using TensorFlow API. We tested the detection confidence of our trained models on each surgical tool and tabulated our results. Results: Our initial test showed many False Negative results, with True Positive results for only 3 tools. After adjusting our model training process for the second test, an improvement was seen with only 1 False Negative result produced. Our project shows potential for deep learning to be used in the future to streamline backend surgical processes, if limitations such as similarities between tools, inconsistent number of training images, and insufficient training images are worked on.