Youngjune Kim1, Dongjun Choi1, Kyong Joon Lee2, Yusuhn Kang3, Joong Mo Ahn1, Eugene Lee1, Joon Woo Lee1, Heung Sik Kang1. 1. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. 2. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. kjoon31@gmail.com. 3. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. yskang0114@gmail.com.
Abstract
OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear. METHODS: The algorithm was developed using 6793 shoulder radiograph series performed between January 2015 and June 2018, which were labeled based on ultrasound or MRI conducted within 90 days, and clinical information (age, sex, dominant side, history of trauma, degree of pain). The output was the probability of significant rotator cuff tear (supraspinatus/infraspinatus complex tear with > 50% of tendon thickness). An operating point corresponding to sensitivity of 98% was set to achieve high negative predictive value (NPV) and low negative likelihood ratio (LR-). The performance of the algorithm was tested with 1095 radiograph series performed between July and December 2018. Subgroup analysis using Fisher's exact test was performed to identify factors (clinical information, radiography vendor, advanced imaging modality) associated with negative test results and NPV. RESULTS: Sensitivity, NPV, and LR- were 97.3%, 96.6%, and 0.06, respectively. The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients suspected of rotator cuff tear. The subgroup analysis showed that age < 60 years (p < 0.001), non-dominant side (p < 0.001), absence of trauma history (p = 0.001), and ultrasound examination (p < 0.001) were associated with negative test results. NPVs were higher in patients with age < 60 years (p = 0.024) and examined with ultrasound (p < 0.001). CONCLUSION: The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder radiographs. KEY POINTS: • The deep learning algorithm can rule out significant rotator cuff tear with a negative likelihood ratio of 0.06 and a negative predictive value of 96.6%. • The deep learning algorithm can guide patients with significant rotator cuff tear to additional shoulder ultrasound or MRI with a sensitivity of 97.3%. • The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients with clinically suspected rotator cuff tear.
OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear. METHODS: The algorithm was developed using 6793 shoulder radiograph series performed between January 2015 and June 2018, which were labeled based on ultrasound or MRI conducted within 90 days, and clinical information (age, sex, dominant side, history of trauma, degree of pain). The output was the probability of significant rotator cuff tear (supraspinatus/infraspinatus complex tear with > 50% of tendon thickness). An operating point corresponding to sensitivity of 98% was set to achieve high negative predictive value (NPV) and low negative likelihood ratio (LR-). The performance of the algorithm was tested with 1095 radiograph series performed between July and December 2018. Subgroup analysis using Fisher's exact test was performed to identify factors (clinical information, radiography vendor, advanced imaging modality) associated with negative test results and NPV. RESULTS: Sensitivity, NPV, and LR- were 97.3%, 96.6%, and 0.06, respectively. The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients suspected of rotator cuff tear. The subgroup analysis showed that age < 60 years (p < 0.001), non-dominant side (p < 0.001), absence of trauma history (p = 0.001), and ultrasound examination (p < 0.001) were associated with negative test results. NPVs were higher in patients with age < 60 years (p = 0.024) and examined with ultrasound (p < 0.001). CONCLUSION: The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder radiographs. KEY POINTS: • The deep learning algorithm can rule out significant rotator cuff tear with a negative likelihood ratio of 0.06 and a negative predictive value of 96.6%. • The deep learning algorithm can guide patients with significant rotator cuff tear to additional shoulder ultrasound or MRI with a sensitivity of 97.3%. • The deep learning algorithm could rule out significant rotator cuff tear in about 30% of patients with clinically suspected rotator cuff tear.
Entities:
Keywords:
Deep learning; Radiography; Rotator cuff tear