Automated detection of enlarged extraocular muscle in Graves’ ophthalmopathy with computed tomography and deep neural network
Kaori Hanai, Hitoshi Tabuchi, Daisuke Nagasato, Mao Tanabe, Hiroki Masumoto, Sakurako Miya, Natsuno Nishio, Hirohiko Nakamura, Masato Hashimoto
Abstract
This study aimed to develop a diagnostic software system to evaluate the enlarged extraocular muscles (EEM) in patients with Graves' ophthalmopathy (GO) by a deep neural network.This prospective observational study involved 371 participants (199 EEM patients with GO and 172 controls with normal extraocular muscles) whose extraocular muscles were examined with orbital coronal computed tomography. When at least one rectus muscle (right or left superior, inferior, medial, or lateral) in the patients was 4.0 mm or larger, it was classified as an EEM patient with GO. We used 222 images of the data from patients as the training data, 74 images as the validation test data, and 75 images as the test data to "train" the deep neural network to judge the thickness of the extraocular muscles on computed tomography. We then validated the performance of the network. In the test data, the area under the curve was 0.946 (95% confidence interval (CI) 0.894-0.998), and receiver operating characteristic analysis demonstrated 92.5% (95% CI 0.796-0.984) sensitivity and 88.6% (95% CI 0.733-0.968) specificity. The results suggest that the deep learning system with the deep neural network can detect EEM in patients with GO.