Accuracy Analysis in Medical Device Quality Measurement by Software and New Perspective of Machine Vision System

Abstract

This dissertation investigates the impact of software and machinery on the accuracy of quality measurements for medical products, specifically focusing on key parameters of medical devices. The primary objective is to streamline quality control processes, reducing human errors. To achieve this, we leverage software and artificial intelligence in conjunction with visual inspection machinery. Our study aims to assess the quality of medical products quantitatively by analyzing them as a percentage of the ideal quality. We seek to adapt the existing quality control framework to tailor it to the specific parameters that significantly influence equipment quality. In doing so, we adhere to ISO regulations and associated standards, which guide us in identifying these critical parameters and optimizing the quality control processes for various medical equipment. It is important to note that our goal is not to replace established quality management standards but rather to introduce more efficient processes that can complement these standards. We also evaluate the accuracy of traditional quality control methods in comparison to the accuracy of quality control through visual inspection systems. In this dissertation, we present two case studies as empirical evidence, illustrating the impact of human error on quality control and the effectiveness of the visual inspection system.