Assessments of patient safety for multidimensional risk management in robotic dentistry

Abstract

Dentistry is an essential practice to maintain the health of the oral cavity, but barriers to accessibility, dental fear, and the lack of oral health awareness have led to a high prevalence of dental diseases globally. Dental robotics has emerged as a new field of dentistry and can provide numerous benefits to dental professionals and society from increased accessibility to improved standardisation of dental techniques. The current design of the dental setup limits high autonomy applications and, without robot risk awareness, it is unlikely that robots will ever overcome the safety concerns for autonomous application on patients. This thesis re-evaluates the dental setup and defined levels of autonomy for medical robots. It also outlines a framework for risk supervision and automated risk management for dental robots. Along with a simplified model for decision-making, aspects of risk supervision detection methods were developed in MATLAB, tested in simulation with Unity, and then analysed in Python. Initial dental robot experiments were performed with virtual and physical simulation testing for a partially dynamic dental environment using a small Meca500 robot arm that is relatively non-confrontational and would be well suited to small dental practices. The multi-dimensional risk assessments can inform future risk analyses for operations on conscious patients. Correct implementation of robots is critical to develop trust in both patients and clinicians, which in turn helps to create a more stable and safe dental environment for robots to perform high quality dental procedures.