Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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An Integrated and Complementary Evaluation System for Judging the Severity of Knee Osteoarthritis Using CNN

CNN 기반 슬관절 골관절염 중증도 판단을 위한 통합 보완된 등급 판정 시스템

  • YeChan Yoon
  • 윤예찬 (고려대학교 산업경영공학과(산업인공지능))
  • Received : 2024.04.19
  • Accepted : 2024.08.15
  • Published : 2024.08.30
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Abstract

Knee osteoarthritis (OA) is a very common musculoskeletal disorder worldwide. The assessment of knee osteoarthritis, which requires a rapid and accurate initial diagnosis, is determined to be different depending on the currently dispersed classification system, and each classification system has different criteria. Also, because the medical staff directly sees and reads the X-ray pictures, it depends on the subjective opinion of the medical staff, and it takes time to establish an accurate diagnosis and a clear treatment plan. Therefore, in this study, we designed the stenosis length measurement algorithm and Osteophyte detection and length measurement algorithm, which are the criteria for determining the knee osteoarthritis grade, separately using CNN, which is a deep learning technique. In addition, we would like to create a grading system that integrates and complements the existing classification system and show results that match the judgments of actual medical staff. Based on publicly available OAI (Osteoarthritis Initiative) data, a total of 9,786 knee osteoarthritis data were used in this study, eventually achieving an Accuracy of 69.8% and an F1 score of 76.65%.

슬관절 골관절염(OA, Osteoarthritis)은 전 세계적으로 매우 흔한 근골격계 질환이다. 빠르고 정확한 초기 진단이 필요한 슬관절 골관절염의 등급은 현재 분산된 분류 시스템에 따라 다르게 판정되며, 각 분류 시스템마다 기준이 상이하다. 또한 의료진이 X-ray 사진을 직접 보고 판독하기 때문에 의료진의 주관적인 의견에 따라 달라지며 시간이 많이 소요되어 정확한 진단과 명확한 치료 계획 수립에 시간이 지연되고 있다. 따라서 본 연구는 딥러닝 기술인 CNN을 사용하여 슬관절 골관절염 등급 판단 기준이 되는 협착 부분의 길이 측정 알고리즘과 골극의 탐지 및 길이 측정 알고리즘을 따로 설계하였다. 또한 기존 분류 시스템을 통합 보완한 등급 분류 시스템을 만들어 실제 의료진의 판단과 일치하는 결과를 나타내고자 한다. 공개적으로 사용 가능한 OAI (Osteoarthritis Initiative) 데이터를 기반으로 하여, 총 9,786개의 슬관절 방사선 데이터가 본 연구에 사용되었으며, 최종적으로 Accuracy(정확도) 69.8%, F1 score 76.65%를 달성하였다.

Keywords

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