An accelerated deep learning model can accurately identify clinically important humeral and scapular landmarks on plain radiographs obtained before and after anatomic arthroplasty.

Abstract

Purpose: Accurate identification of radiographic landmarks is fundamental to characterizing glenohumeral relationships before and sequentially after shoulder arthroplasty, but manual annotation of these radiographs is laborious. We report on the use of artificial intelligence, specifically computer vision and deep learning models (DLMs), in determining the accuracy of DLM-identified and surgeon identified (SI) landmarks before and after anatomic shoulder arthroplasty.

Materials & methods: 240 true anteroposterior radiographs were annotated using 11 standard osseous landmarks to train a deep learning model. Radiographs were modified to allow for a training model consisting of 2,260 images. The accuracy of DLM landmarks was compared to manually annotated radiographs using 60 radiographs not used in the training model. In addition, we also performed 14 different measurements of component positioning and compared these to measurements made based on DLM landmarks.

Results: The mean deviation between DLM vs. SI cortical landmarks was 1.9 ± 1.9 mm. Scapular landmarks had slightly lower deviations compared to humeral landmarks (1.5 ± 1.8 mm vs. 2.1 ± 2.0 mm, p < 0.001). The DLM was also found to be accurate with respect to 14 measures of scapular, humeral, and glenohumeral measurements with a mean deviation of 2.9 ± 2.7 mm.

Conclusions: An accelerated deep learning model using a base of only 240 annotated images was able to achieve low levels of deviation in identifying common humeral and scapular landmarks on preoperative and postoperative radiographs. The reliability and efficiency of this deep learning model represents a powerful tool to analyze preoperative and postoperative radiographs while avoiding human observer bias.

Level of evidence: IV.