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Year : 2022  |  Volume : 30  |  Issue : 3  |  Page : 196-202

Deep learning for detecting supraspinatus calcific tendinopathy on ultrasound images

1 Department of Civil Engineering, National Taiwan University, Taipei, Taiwan
2 Department of Physical Medicine and Rehabilitation, Centre Hospitalier de l'Université de Montréal, Montreal, Canada
3 Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei; Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
4 Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
5 Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara, Turkey

Correspondence Address:
Dr. Chueh-Hung Wu
No. 2, Sec. 1, Shengyi Road, Zhubei City, Hsinchu County 302
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmu.jmu_182_21

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Background: The aim of the study was to evaluate the feasibility of convolutional neural network (CNN)-based deep learning (DL) algorithms to dichotomize shoulder ultrasound (US) images with or without supraspinatus calcific tendinopathy (SSCT). Methods: This was a retrospective study pertaining to US examinations that had been performed by 18 physiatrists with 3–20 years of experience. 133,619 US images from 7836 consecutive patients who had undergone shoulder US examinations between January 2017 and June 2019 were collected. Only images with longitudinal or transverse views of supraspinatus tendons (SSTs) were included. During the labeling process, two physiatrists with 6-and 10-year experience in musculoskeletal US independently classified the images as with or without SSCT. DenseNet-121, a pre-trained model in CNN, was used to develop a computer-aided system to identify US images of SSTs with and without calcifications. Testing accuracy, sensitivity, and specificity calculated from the confusion matrix was used to evaluate the models. Results: A total of 2462 images were used for developing the DL algorithm. The longitudinal-transverse model developed with a CNN-based DL algorithm was better for the diagnosis of SSCT when compared with the longitudinal and transverse models (accuracy: 91.32%, sensitivity: 87.89%, and specificity: 94.74%). Conclusion: The developed DL model as a computer-aided system can assist physicians in diagnosing SSCT during the US examination.

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