미만성 간질성 폐질환을 위한 컴퓨터 시각과 인공지능기반의 의사결정지원시스템

Abstract

In order to manage large medical data and improve work process quality, hospitals have increasingly installed the Picture Archive and Communication System (PACS) and Electronic Medical Records (EMR). As a result, the Clinical Decision Support System (CDSS) is considered to be an essential medical knowledge management system that helps clinicians make better and effective decisions for diagnosis. The purpose of this study was to study computer vision module for automatic Ground Gross Opacity (GGO) detection, to develop artificial intelligence-based CDSS for diagnosis of diffuse interstitial lung disease (DILD), and to validate CDSS. In order to diagnose DILD using HRCT for the rule-based CDSS the system was developed based on 69 diseases, 85 findings, 73 conditions, 387 status, and 62 rules. The computer visual module for automatic GGO detection from the HRCT image data was developed by Neural Network Analysis (NNA) and its result was compared with the result of Decision Tree Analysis. The results showed that the Decision Tree Analysis had more significant features for detecting GGO than the NNA. In order to validate the prototype system, 18 normal cases were used. The result represents 85% of correctness.