Deep learning model using cross-sequence learning to identify orbital fractures in radiographs of patients under 20 Years

Abstract

Orbit fractures under 20 years are a medical emergency requiring urgent surgery with the gold standard modality being high-resolution CT. If radiography could be used to identify patients without fractures, the number of unnecessary CT scans could be reduced. The purpose of this study was to develop and validate a deep learning-based multi-input model with a novel cross-sequence learning method, which outperforms the conventional single-input models, to detect orbital fractures on radiographs of young patients. Development datasets for this retrospective study were acquired from two hospitals (n = 904 and n = 910). The datasets included patients with facial trauma who underwent orbital rim view and CT. The development dataset was split into training, tuning, and internal test sets in 7:1:2 ratios. A radiology resident, pediatric radiologist, and ophthalmic surgeon participated in a two-session observer study examining an internal test set, with or without model assistance. The area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and 95% confidence intervals (CIs) were obtained. Our proposed model detected orbital fractures with an AUROC of 0.802. The sensitivity, specificity, PPV, and NPV of the model achieved 65.8, 86.5, 70.9, and 83.5%, respectively. With model assistance, all values for orbital fracture detection improved for the ophthalmic surgeon, with a statistically significant difference in specificity (P < 0.001). For the radiology resident, specificity exhibited significant improvement with model assistance (P < 0.001). Our proposed model was able to identify orbital fractures on radiographs, reducing unnecessary CT scans and radiation exposure.