Automated Microsurgical Tool Categorization using a Surface-Based EMG System.

Abstract

The selection of micro-surgical tools and their usage form a critical component in today's surgical outcome. The success or failure of the surgery depends on how effectively the surgeon has applied the tools on the operating patient. Popular surgical tools, including forceps, clamp applicators, micro-scissors, and needle holders, are typically used in operating procedures, and characterising the same set of tools in conjunction with the surgeon's implementation remains a topic of continued interest. There have been several computer vision-based approaches to segment and detect the tools used in the surgery, where the microscopic recordings are evaluated, however surgeon's haptic feedback and the subtle variation drawn is not evidently reported. The video-based detection system also suffers from the problem of masking or shadowing critical information while in use. Hence, efforts to extract localised information during the surgery are very valuable in addition to the video-acquired outcome. The surface-based electromyography (sEMG) system offers electro-muscular signals at the skin that characterise the actions performed. The work attempts to evaluate the adoption of sEMG signals for the detection of micro-surgical tools. In this work, a pilot study of handling micro-surgical tools and classifying the same using two-channel sEMG signals from the machine learning (ML) model is performed. A two-channel sEMG acquisition system connected to the electrodes was designed and used to build a staged and supervised data set consisting of operating surgical tools. Five hand-crafted features, each from an individual channel, were employed and utilised to design an accurate model. An accuracy of 97.43% was achieved for running the ANN model on sEMG signals to classify five surgical tools based on their press-and-release action. The use of sEMG signals for tool detection is a step towards the development of tool characterisation and the assessment of surgeons' skills.Clinical relevance- A two-channel sEMG signal acquisition system to detect micro-surgical tool usage was developed. Mapping the surgery outcome with the tool handling characteristics will be a valuable addition to the existing video-based assessment of the surgery. The subtle tool handling characteristics of the surgeon are effective in improving the overall outcome of the surgery, and it is possible to extract this information only from the proposed approach.