Optical coherence tomography–based diabetic macula edema screening with artificial intelligence. Issue 11 (22nd May 2020)

• Record Type: Journal Article
• Authors: Hwang, De-Kuang; Chou, Yu-Bai; Lin, Tai-Chi; Yang, Hsin-Yu; Kao, Zih-Kai; Kao, Chung-Lan; Yang, Yi-Ping; Chen, Shih-Jen; Hsu, Chih-Chien; Jheng, Ying-Chun
• Is Part Of: Journal of the Chinese Medical Association. Volume 83:Issue 11(2020)
• Page Start: 1034
• Page End: 1038
• Publication Date: 2020-05-22
• Subjects: Artificial intelligence -- Optical coherence tomography -- Vascular endothelial growth factor; Medicine -- Periodicals; 610.5
• DOI: 10.1097/JCMA.0000000000000351
• Languages: English
• ISSNs: 1726-4901

Abstract

Abstract : Background: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to evaluate the macular lesions. In patients with diabetes mellitus (DM), the existence of diabetic macular edema (DME) can cause significant vision impairment and further intravitreal injection (IVI) of anti–vascular endothelial growth factor (VEGF) is needed. However, the increasing number of DM patients makes it a big burden for clinicians to manually determine whether DME exists in the OCT images. The artificial intelligence (AI) now enormously applied to many medical territories may help reduce the burden on clinicians. Methods: We selected DME patients receiving IVI of anti-VEGF or corticosteroid at Taipei Veterans General Hospital in 2017. All macular cross-sectional scan OCT images were collected retrospectively from the eyes of these patients from January 2008 to July 2018. We further established AI models based on convolutional neural network architecture to determine whether the DM patients have DME by OCT images. Results: Based on the convolutional neural networks, InceptionV3 and VGG16, our AI system achieved a high DME diagnostic accuracy of 93.09% and 92.82%, respectively. The sensitivity of the VGG16 and InceptionV3 models was 96.48% and 95.15%., respectively. The specificity was corresponding to 86.67% and 89.63% for VGG16 and InceptionV3, respectively. We further developed an OCT-driven platform based on these AI models. Conclusion: We successfully set up AI models to provide an accurate diagnosis of DME by OCT images. These models may assist clinicians in screening DME in DM patients in the future.