Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures

Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures: Orbital blow out fracture is a common disease in emergency department and a delay or failure in diagnosis can lead to permanent visual changes. This study aims to evaluate the ability of an automatic orbital blowout fractures detection system based on computed tomography (CT) data.

Orbital CT scans of adult orbital blowout fractures patients and normal cases were obtained from Shanghai Ninth People's Hospital between January and March 2017. The region of fractures was annotated using 3D Slicer. The Inception V3 convolutional neural networks were constructed utilizing the Python programming language with PyTorch as the framework to extract high dimension features from each slice in a CT scan. These extracted features are processed through a XGBoost model to make the final differentiation of fracture cases and nonfracture ones. Accuracy, receiver operating characteristics, and area under the curve were evaluated.

This study used 94 CT scans diagnosed with orbital blowout fractures and 94 healthy control cases. The automatic detection system showed accuracy of 92% in single-image classification and 87% in patient level detection. The area under the receiver operating characteristic curve was 0.9574.

Using a deep learning-based automatic detection system of orbital blowout fracture can accurately detect and classify orbital blowout fractures from CT scans. The convolutional neural networks model combined with an accurate annotation system could achieve good performance in a small dataset. Further studies with large and multicenter data are required to refine this technology for possible clinical applications.

Address correspondence and reprint requests to Xianqun Fan, MD, PhD, and Huifang Zhou, MD, PhD, Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200025, China; E-mail: fanxq@sjtu.edu.cn, fangzzfang@163.com

Received 23 August, 2019

Accepted 7 September, 2019

LL and XS are co-first authors.

This work was supported by the National Key R&D Program of China (2018YFC1106100, 2018YFC1106101, 2018YFF0301102, and 2018YFF0301105), the Science and Technology Commission of Shanghai (17DZ2260100), and the Interdisciplinary Program of Shanghai Jiao Tong University (ZH2018QNA07, ZH2018ZDA12). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The authors report no conflicts of interest.

© 2019 by Mutaz B. Habal, MD.