Smartphone-based diabetic macula edema screening with an offline artificial intelligence
| Autor: | Zih Kai Kao, Chung Lan Kao, Tai Chi Lin, Wei Kuang Yu, Aliaksandr A. Yarmishyn, Ying Chun Jheng, Shih Jie Chou, Yi Ping Yang, Chih Chien Hsu, Shih-Jen Chen, De Kuang Hwang |
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| Rok vydání: | 2020 |
| Předmět: |
genetic structures
Diabetic macular edema Taiwan Diagnostic accuracy 030204 cardiovascular system & hematology Convolutional neural network Macular Edema 03 medical and health sciences 0302 clinical medicine Optical coherence tomography Artificial Intelligence Image Processing Computer-Assisted Humans Medicine General hospital Diabetic Retinopathy medicine.diagnostic_test business.industry Confusion matrix General Medicine eye diseases 030220 oncology & carcinogenesis Neural Networks Computer Smartphone Artificial intelligence business Tomography Optical Coherence Ai systems |
| Zdroj: | Journal of the Chinese Medical Association. 83:1102-1106 |
| ISSN: | 1726-4901 |
| DOI: | 10.1097/jcma.0000000000000355 |
| Popis: | Background Diabetic macular edema (DME) is a sight-threatening condition that need regular examinations and remedies. Optical coherence tomography (OCT) is the most common used examination to evaluate the structure and thickness of the macula but the software in the OCT machine does not tell the clinicians whether DME exists directly. Recently, artificial intelligence (AI) is expected to aid in diagnosis generation and therapy selection. We thus develop a smartphone-based offline AI system that provides diagnostic suggestions and medical strategies through analyzing OCT images from diabetic patients at risk of developing DME. Methods DME patients receiving treatments in 2017 at Taipei Veterans General Hospital were included in this study. We retrospectively collected the OCT images of these patients from January 2008 to July 2018. We established the AI model based on MobileNet architecture to classify the OCT images conditions. The confusion matrix has been applied to present the performance of the trained AI model. Results Based on the convolutional neural network with MobileNet model, our AI system achieved a high DME diagnostic accuracy of 90.02%, which is comparable to other AI systems such as InceptionV3 and VGG16. We further developed a mobile-application based on this AI model available at https://aicl.ddns.net/DME.apk. Conclusion We successful integrated an AI model into the mobile device to provide an offline method to provide the diagnosis for quickly screening the risk of developing diabetic macular edema (DME). With the offline property, our model could help those non-ophthalmological health-care providers in offshore islands or underdeveloped countries. |
| Databáze: | OpenAIRE |
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