Deep Learning for Detecting Supraspinatus Calcific Tendinopathy on Ultrasound Images
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".