Journal Article

Measuring the critical shoulder angle on radiographs: an accurate and repeatable deep learning model

Minelli, Marco, Galbusera, Fabio, Savevski, Victor, Sconfienza, Luca Maria, Castagna, Alessandro, Cina, Andrea

2022

Overview

Abstract PurposeSince the critical shoulder angle (CSA) is considered a risk factor for shoulder pathology and the intra- and inter-rater variabilities in its calculation are not negligible, we developed a deep learning model that calculates it automatically and accurately.MethodsWe used a dataset of 8467 anteroposterior x-ray images of the shoulder annotated with 3 landmarks of interest. A Convolutional Neural Network model coupled with a spatial to numerical transform (DSNT) layer was used to predict the landmark coordinates from which the CSA was calculated. The performances were evaluated by calculating the Euclidean distance between the ground truth coordinates and the predicted ones normalized with respect to the distance between the first and the second points, and the error between the CSA angle measured by a human observer and the predicted one.ResultsRegarding the normalized point distances, we obtained a median error of 2.9%, 2.5%, and 2% for the three points among the entire set. Considering CSA calculations, the median errors were 1° (standard deviation 1.2°), 0.88° (standard deviation 0.87°), and 0.99° (standard deviation 1°) for angles below 30°, between 30° and 35°, and above 35°, respectively.DiscussionThese results demonstrate that the model has the potential to be used in clinical settings where the replicability is important. The reported standard error of the CSA measurement is greater than 2° that is above the median error of our model, indicating a potential accuracy sufficient to be used in a clinical setting.

• Publisher: Springer Nature B.V
• Subject: Artificial neural networks / Deep learning / Error analysis / Euclidean geometry / Neural networks / Risk analysis / Risk factors / Shoulder / Standard deviation / Standard error