ArticleMedical & biological engineering & computing2022
Classification of rotator cuff tears in ultrasound images using deep learning models.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Harnessing Artificial Intelligence for Shoulder Ultrasonography: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- Preoperative differentiation of borderline and malignant ovarian tumors using interpretable machine learning.Journal of ovarian research · 2026Article
- IMU-based rotator cuff injury recognition with varying configurations and combinations.Frontiers in physiology · 2026Article
- Quantitative assessment of rotator cuff injuries using synthetic MRI and IDEAL-IQ imaging techniques.Heliyon · 2024Article
- Application of Artificial Intelligence in Shoulder Pathology.Diagnostics (Basel, Switzerland) · 2024Review
- Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence.Journal of orthopaedics · 2024Review
- Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach.Diagnostics (Basel, Switzerland) · 2023Article
- The Correlation between Various Shoulder Anatomical Indices on X-Ray and Subacromial Impingement and Morphology of Rotator Cuff Tears.Orthopaedic surgery · 2023Article
- Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm.Scientific reports · 2023Article
- Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review.JSES reviews, reports, and techniques · 2023Review
- Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm.PloS one · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming diagnostic imaging tests such as magnetic resonance imaging or computed tomography. Deep learning algorithms are increasingly used to analyze medical images, but they have not been used to identify RCTs with ultrasound images. The aim of this study is to develop an approach to automatically classify RCTs and provide visualization of tear location using ultrasound images and convolutional neural networks (CNNs). The proposed method was developed using transfer learning and fine-tuning with five pre-trained deep models (VGG19, InceptionV3, Xception, ResNet50, and DenseNet121). The Bayesian optimization method was also used to optimize hyperparameters of the CNN models. A total of 194 ultrasound images from Kosin University Gospel Hospital were used to train and test the CNN models by five-fold cross-validation. Among the five models, DenseNet121 demonstrated the best classification performance with 88.2% accuracy, 93.8% sensitivity, 83.6% specificity, and AUC score of 0.832. A gradient-weighted class activation mapping (Grad-CAM) highlighted the sensitive features in the learning process on ultrasound images. The proposed approach demonstrates the feasibility of using deep learning and ultrasound images to assist RCTs' diagnosis.
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