Stroke is one of the major causes of death worldwide, and in Korea, it has the second highest mortality rate after cancer. Stroke patients require continuous observation and rehabilitation treatment after onset, and in particular, paralysis symptoms are likely to worsen during rehabilitation, emphasizing the need for a real-time monitoring system. Meanwhile, the importance of medical data quality control (QC) algorithms is increasing. In this study, various causes such as failure of sensors such as voltage, current, and temperature of the patient's imaging device diagnostic device, or power loss, may cause malfunctions and transmit inaccurate data. Therefore, in order to secure the reliability of the patient's imaging device diagnostic device data, we plan to design data analysis and algorithms based on QC data of the imaging device diagnostic device. In order to design data analysis and algorithms based on QC data, a system capable of measuring and analyzing sensor data of imaging device diagnostic equipment was built. The reference values of the algorithms to be developed, such as physical limit tests, continuity tests, step tests, median filter tests, and frequency distribution tests, were derived. Voltage, current, and temperature sensor data were statistically analyzed, and in the case of analysis that changes in real time, algorithm S/W was inserted to calculate in real time. It is judged that by monitoring in real time, efficient management and maintenance of the device, and rapid response to device failures will be possible. In the case of device failure, various accidents and high costs can occur. Therefore, if real-time failures are confirmed and rapid maintenance is possible, maintenance costs can be reduced and reliability can be improved, so it is judged that efficient management of the device will be possible.

The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

골반저근은 골반기관을 지지하는 기능을 가지고 있으며 요자제를 유지하는 여성의 주요 하부조직이다. 골반저근의 약화는 복압성 요실금의 원인이 되는데, 이러한 골반저근의 기능 정도는 복압성 요실금의 병증정도를 평가하는 지표로 사용될 수 있다. 이에 본 연구에서는 골반저근의 수축 압력을 측정하여 복압성 요실금의 병적 진행정도를 정량적으로 진단할 수 있는 요실금 진단 알고리즘을 제안하였다. 이를 위하여 골반저근의 수축압력 정보를 측정할 수 있는 시스템을 제작하였으며, 측정된 데이터의 특징 분석을 위한 측정 프로토콜을 제안하였다. 복압성 요실금 환자로부터 획득한 데이터를 이용하여 5개의 진단 파라미터를 추출하였으며, 이를 이용한 진단 알고리즘을 구현하였다. 임상시험을 통하여 진단 알고리즘의 정확성을 평가한 결과 80%의 정확성을 보였으며, 20%의 위양성 진단 결과를 보였다. 반면에 위음성 진단 결과는 확인되지 않았다. 본 연구에서 제안한 요실금 진단 알고리즘은 복압성 요실금의 병적 진행 정도를 정량적으로 진단할 수 있으며, 요실금 진단 시스템 개발에 활용될 수 있을 것으로 판단된다.