A deep learning system for detecting diabetic retinopathy across the disease spectrum

Abstract

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Update on Optical Coherence Tomography and Optical Coherence Tomography Angiography Imaging in Proliferative Diabetic Retinopathy

“…Nevertheless, it should be noted that for a proper assessment, correct segmentation is necessary, and often there is improvement in NVC recognition with manual segmentation [79,81,86,93,103,104]. In the future, the accuracy of NVC detection in vitreoretinal slabs might increase with improvement of auto segmentation, deep learning and artificial intelligence [81,86,[107][108][109][110][111]. IRMAs seem to generate higher false positive rates due to the retinal slab image, despite the vitreoretinal slab image not showing extension into the vitreous cavity [82].…”

Artificial Intelligence in Translational Medicine

“…In the context of imaging in diagnosis and disease progression, applying ML-based techniques, ophthalmology is one of the medical fields in which these computational approaches have been successfully employed [133]. In fact, AI principally based on DL methods has been used to detect several ocular disorders, including retinopathy of prematurity [254], diabetic retinopathy [255,256], macular edema [257,258], age-related macular degeneration [259,260], and glaucoma [261][262][263], using fundus images, optical coherence tomography (OCT), and visual fields. Screening, diagnosis, and monitoring of major eye disorders for patients in primary care might be achievable using DL in ocular imaging combined with telemedicine.…”

Artificial Intelligence in Translational Medicine

“…In the context of imaging in diagnosis and disease-progression applying ML-based techniques, ophthalmology is one of the medical fields in which these computational approaches have been successfully employed [123]. In fact, AI principally based on DL methods has been used to detect several ocular disorders including retinopathy of prematurity [243], diabetic retinopathy [244,245], macular oedema [246,247], age-related macular degeneration [248,249], and glaucoma [250][251][252] using fundus images, optical coherence tomography (OCT), and visual fields. Screening, diagnosis, and monitoring of major eye disorders for patients in primary care might be achievable using DL in ocular imaging combined with telemedicine.…”

“…Finally, the statistical parameters, considering the external validation, ranging from 0.916 to 0.970 (area under the ROC curves). In summary, DeepDR showed significant accuracy and high sensitivity in detecting diabetic retinopathy from early-to late-stages [244]. Asaoka and colleagues reported a ML approach based on deep and transfer learning for an accurate diagnosis regarding the early-onset glaucoma using OCT images [252].…”