Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.

OBJECTIVE

To develop a deep learning algorithm capable of evaluating subscapularis tendon (SSC) tears based on axillary lateral shoulder radiography.

METHODS

A total of 2,779 axillary lateral shoulder radiographs (performed between February 2010 and December 2018) and the patients' corresponding clinical information (age, sex, dominant side, history of trauma, and degree of pain) were used to develop the deep learning algorithm. The radiographs were labeled based on arthroscopic findings, with the output being the probability of an SSC tear exceeding 50% of the tendon's thickness. The algorithm's performance was evaluated by determining the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, negative predictive value (NPV), and negative likelihood ratio (LR-) at a predefined high-sensitivity cutoff point. Two different test sets were used, with radiographs obtained between January and December 2019; Test Set 1 used arthroscopic findings as the reference standard (n = 340), whereas Test Set 2 used MRI findings as the reference standard (n = 627).

RESULTS

The AUCs were 0.83 (95% confidence interval, 0.79-0.88) and 0.82 (95% confidence interval, 0.79-0.86) for Test Sets 1 and 2, respectively. At the high-sensitivity cutoff point, the sensitivity, NPV, and LR- were 91.4%, 90.4%, and 0.21 in Test Set 1, and 90.2%, 89.5%, and 0.21 in Test Set 2, respectively. Gradient-weighted Class Activation Mapping identified the subscapularis insertion site at the lesser tuberosity as the most sensitive region.

CONCLUSION

Our deep learning algorithm is capable of assessing SSC tears based on changes at the lesser tuberosity on axillary lateral radiographs with moderate accuracy.

KEY POINTS: • We have developed a deep learning algorithm capable of assessing SSC tears based on changes at the lesser tuberosity on axillary lateral radiographs and previous clinical data with moderate accuracy. • Our deep learning algorithm could be used as an objective method to initially assess SSC integrity and to identify those who would and would not benefit from further investigation or treatment.