CT 영상 기반 근감소증 진단을 위한AI 영상분할 모델 개발 및 검증
Development and Validation of AI Image Segmentation Model for CT Image-Based Sarcopenia Diagnosis
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초록
근감소증은 국내는 2021년 질병으로 분류되었을 만큼 잘 알려져 있지 않지만 고령화사회에 진입한 선진국에서는 사회적 문제로 인식하고 있다. 근감소증 진단은 유럽노인근감소증 진단그룹(EWGSOP)과 아시아근감소증진단그룹(AWGS)에서 제시하는 국제표준지침을 따른다. 최근 진단방법으로 절대적 근육량 이외에 신체수행평가로 보행속도 측정과 일어서기 검사 등을 통하여 근육 기능을 함께 측정할 것을 권고하고 있다. 근육량을측정하기 위한 대표적인 방법으로 DEXA를 이용한 체성분 분석 방법이 임상에서 정식으로 실시하고 있다. 또한 MRI 또는 CT의 복부 영상을 이용하여근육량을 측정하는 다양한 연구가 활발하게 진행되고 있다. 따라서 본 논문에서는 근감소증 진단을 위해서 비교적 짧은 촬영시간을 갖는 CT의복부영상기반으로 AI 영상 분할 모델을 개발하고 다기관 검증한 내용을 기술한다. 우리는 CT 영상 중에 요추의 L3 영역을 분류하여 피하지방,내장지방, 근육을 자동으로 분할할 수 있는 인공지능 모델을 U-Net 모델을 사용하여 개발하였다. 또한 모델의 성능평가를 위해서 분할영역의IOU(Intersection over Union)를 계산하여 내부검증을 진행했으며, 타 병원의 데이터를 활용하여 동일한 IOU 방법으로 외부검증을 진행한 결과를보인다. 검증 결과를 토대로 문제점과 해결방안에 대해서 검증하고 보완하고자 했다.
Abstract
Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developedcountries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by theEuropean Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently,it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing testin addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body compositionanalysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle massusing abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model basedon abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat byselecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed bycalculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitalsare shown. Based on the verification results, we tried to review and supplement the problems and solutions.
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