Abstract
Background
Turner syndrome (TS) is one of the important causes of short stature in girls, but there are cases of misdiagnosis and missed diagnosis in clinical practice. Our aim is to analyze the hand skeletal characteristics of TS patients and establish a disease screening model using deep learning.
Methods
A total of 101 pediatric patients with TS were included in this retrospective case-control study. Their radiation parameters from hand X-rays were summarized and compared. Receiver operating characteristic (ROC) curves for parameters with differences between the groups were plotted. Additionally, we used deep learning networks to establish a predictive model.
Results
Four parameters were identified as having diagnostic value for TS: the length ratio of metacarpal IV and metacarpal III, the distance between ulnoradial tangents, the carpal angle, and the ulnar-radial angle. The ResNet50 deep neural network architecture was utilized, resulting in an accuracy of 78.89%, specificity of 76.67%, and sensitivity of 83.33% on a test dataset.
Conclusions
We propose that certain hand radiograph parameters have the potential to serve as diagnostic indicators for TS. The utilization of deep learning models has significantly enhanced the precision of disease diagnosis.