Comparative Study of Generative Models for Early Detection of Failures in Medical Devices

TL;DR

This paper compares three generative machine learning models for early fault detection in complex medical devices, aiming to improve safety by analyzing sensor data from surgical staplers.

Contribution

It introduces and evaluates three novel generative approaches for fault detection in medical devices, addressing challenges of complex failure modes.

Findings

All models demonstrated potential for early fault detection.

Data efficiency varied among the approaches.

Improved safety monitoring for surgical devices.

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.