Joo Young Kim1, Kyunghan Ro2, Sungmin You1, Bo Rum Nam1, Sunhyun Yook1, Hee Seol Park3, Jae Chul Yoo3, Eunkyoung Park4, Kyeongwon Cho5, Baek Hwan Cho6, In Young Kim7. 1. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. 2. Bonbridge hospital, 562, Songpa-daero, Songpa-gu, Seoul, South Korea. 3. Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. 4. Biomedical Engineering Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea. 5. Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea. 6. Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea. Electronic address: baekhwan.cho@samsung.com. 7. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. Electronic address: iykim@hanyang.ac.kr.
Abstract
BACKGROUND AND OBJECTIVE: Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these diseases, but subjective intervention of the diagnosis is an obstacle in accurately detecting the correct region. METHODS: Therefore, in this paper, we propose a fully convolutional deep learning algorithm to quantitatively detect the fossa and muscle region by measuring the occupation ratio of supraspinatus in the supraspinous fossa. In the development and performance evaluation of the algorithm, 240 patients MRI dataset with various disease severities were included. RESULTS: As a result, the pixel-wise accuracy of the developed algorithm is 0.9984 ± 0.073 in the fossa region and 0.9988 ± 0.065 in the muscle region. The dice coefficient is 0.9718 ± 0.012 in the fossa region and 0.9463 ± 0.047 in the muscle region. CONCLUSIONS: We expect that the proposed convolutional neural network can improve the efficiency and objectiveness of diagnosis by quantifying the index used in the orthopedic rotator cuff tear.
BACKGROUND AND OBJECTIVE: Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these diseases, but subjective intervention of the diagnosis is an obstacle in accurately detecting the correct region. METHODS: Therefore, in this paper, we propose a fully convolutional deep learning algorithm to quantitatively detect the fossa and muscle region by measuring the occupation ratio of supraspinatus in the supraspinous fossa. In the development and performance evaluation of the algorithm, 240 patients MRI dataset with various disease severities were included. RESULTS: As a result, the pixel-wise accuracy of the developed algorithm is 0.9984 ± 0.073 in the fossa region and 0.9988 ± 0.065 in the muscle region. The dice coefficient is 0.9718 ± 0.012 in the fossa region and 0.9463 ± 0.047 in the muscle region. CONCLUSIONS: We expect that the proposed convolutional neural network can improve the efficiency and objectiveness of diagnosis by quantifying the index used in the orthopedic rotator cuff tear.
Authors: Kyung Jun Choi; Jung Eun Choi; Hyeon Cheol Roh; Jun Soo Eun; Jong Min Kim; Yong Kyun Shin; Min Chae Kang; Joon Kyo Chung; Chaeyeon Lee; Dongyoung Lee; Se Woong Kang; Baek Hwan Cho; Sang Jin Kim Journal: Sci Rep Date: 2021-11-04 Impact factor: 4.379
Authors: Guillermo Droppelmann; Manuel Tello; Nicolás García; Cristóbal Greene; Carlos Jorquera; Felipe Feijoo Journal: Front Med (Lausanne) Date: 2022-09-23