Detection of Traumatic Pediatric Elbow Joint Effusion Using a Deep Convolutional Neural Network

Abstract

Objective: The purpose of this study is to determine whether a deep convolutional neural network (DCNN) trained on a dataset of limited size can accurately diagnose traumatic pediatric elbow effusion on lateral radiographs.

Materials and methods: A total of 901 lateral elbow radiographs from 882 pediatric patients who presented to the emergency department with upper extremity trauma were divided into a training set (657 images), a validation set (115 images), and an independent test set (129 images). The training set was used to train DCNNs of varying depth, architecture, and parameter initialization, some trained from randomly initialized parameter weights and others trained using parameter weights derived from pretraining on an ImageNet dataset. Hyperparameters were optimized using the validation set, and the DCNN with the highest ROC AUC on the validation set was selected for further performance testing on the test set.

Results: The final trained DCNN model had an ROC AUC of 0.985 (95% CI, 0.966-1.000) on the validation set and 0.943 (95% CI, 0.884-1.000) on the test set. On the test set, sensitivity was 0.909 (95% CI, 0.788-1.000), specificity was 0.906 (95% CI, 0.844-0.958), and accuracy was 0.907 (95% CI, 0.843-0.951).

Conclusion: Accurate diagnosis of traumatic pediatric elbow joint effusion can be achieved using a DCNN.

Keywords: artificial intelligence; deep convolutional neural network; deep learning; elbow effusion; machine learning.