DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES
| العنوان: | DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES |
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| المؤلفون: | Qingsheng Peng, Baoyi Liu, Pingting Zhong, Songfu Feng, Xiaohong Yang, Honghua Yu, Dan Cao, Yu Xiao, Xiaomin Zeng, Bin Zhang, Dawei Yang, Manqing Huang, Yijun Hu, Ying Fang, Cong Li, Qiaowei Wu, Hongmin Cai |
| المصدر: | Retina (Philadelphia, Pa.) |
| بيانات النشر: | Retina, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0301 basic medicine, medicine.medical_specialty, genetic structures, Diabetic macular edema, Visual Acuity, Serous Retinal Detachment, Macular Edema, 03 medical and health sciences, 0302 clinical medicine, Text mining, Optical coherence tomography, Ophthalmology, Occlusion, Medicine, Humans, Original Study, Internal validation, Macular edema, Retrospective Studies, optical coherence tomography, Diabetic Retinopathy, medicine.diagnostic_test, Receiver operating characteristic, business.industry, deep learning, General Medicine, medicine.disease, artificial intelligence, complication of diabetic retinopathy, eye diseases, 030104 developmental biology, ROC Curve, 030221 ophthalmology & optometry, sense organs, business, diabetic macular edema, Tomography, Optical Coherence, Follow-Up Studies |
| الوصف: | An image-based deep learning model was developed to detect different morphological patterns of diabetic macular edema based on optical coherence tomography images with high accuracy and transparency. This model could help ophthalmologists to make personalized therapeutic strategies for patients with diabetic macular edema. Purpose: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images. Methods: In the training set, 12,365 OCT images were extracted from a public data set and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation. The presence or absence of three OCT patterns of DME, including diffused retinal thickening, cystoid macular edema, and serous retinal detachment, was labeled with 1 or 0, respectively. A DL model was trained to detect three OCT patterns of DME. The occlusion test was applied for the visualization of the DL model. Results: Applying 5-fold cross-validation method in internal validation, the area under the receiver operating characteristic curve for the detection of three OCT patterns (i.e., diffused retinal thickening, cystoid macular edema, and serous retinal detachment) was 0.971, 0.974, and 0.994, respectively, with an accuracy of 93.0%, 95.1%, and 98.8%, respectively, a sensitivity of 93.5%, 94.5%, and 96.7%, respectively, and a specificity of 92.3%, 95.6%, and 99.3%, respectively. In external validation, the area under the receiver operating characteristic curve was 0.970, 0.997, and 0.997, respectively, with an accuracy of 90.2%, 95.4%, and 95.9%, respectively, a sensitivity of 80.1%, 93.4%, and 94.9%, respectively, and a specificity of 97.6%, 97.2%, and 96.5%, respectively. The occlusion test showed that the DL model could successfully identify the pathologic regions most critical for detection. Conclusion: Our DL model demonstrated high accuracy and transparency in the detection of OCT patterns of DME. These results emphasized the potential of artificial intelligence in assisting clinical decision-making processes in patients with DME. |
| اللغة: | English |
| تدمد: | 1539-2864 0275-004X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f3eb9cc7bd425e2e66f04b922cc53cf http://europepmc.org/articles/PMC8078116 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....6f3eb9cc7bd425e2e66f04b922cc53cf |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15392864 0275004X |
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