Comparative Study of Generative Models for Early Detection of Failures in Medical Devices
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
The medical device industry has significantly advanced by integrating
sophisticated electronics like microchips and field-programmable gate arrays
(FPGAs) to enhance the safety and usability of life-saving devices. These
complex electro-mechanical systems, however, introduce challenging failure
modes that are not easily detectable with conventional methods. Effective fault
detection and mitigation become vital as reliance on such electronics grows.
This paper explores three generative machine learning-based approaches for
fault detection in medical devices, leveraging sensor data from surgical
staplers,a class 2 medical device. Historically considered low-risk, these
devices have recently been linked to an increasing number of injuries and
fatalities. The study evaluates the performance and data requirements of these
machine-learning approaches, highlighting their potential to enhance device
safety.
