TABLE 2.
Top ten macular edema artificial intelligence citations.
| Rank | Source titles | Title of References | Count | Interpretation of findings |
|---|---|---|---|---|
| 1 | Jama-Journal Of The American Medical Association | Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs | 2,919 | Detecting diabetic retinopathy (DR) using deep learning (DL) |
| 2 | Cell | Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning | 1,465 | Using an artificial intelligence (AI) algorithm for retinal optical coherence tomography (OCT) image diagnoses |
| 3 | Nature Medicine | Clinically applicable deep learning for diagnosis and referral in retinal disease | 952 | Establishing a referral recommendation framework based on DL algorithms for retinal diseases which endanger vision |
| 4 | Investigative Ophthalmology and Visual Science | Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning | 460 | Using a convolutional network method to automatically detect DR when compared with other automated detection methods (IDx DR X2.1) |
| 5 | Biomedical Optics Express | Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search | 306 | A new framework automatically segmenting nine-layer boundaries in retinal OCT images |
| 6 | Biomedical Optics Express | ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks | 297 | A Relay Net strategy to segment multiple retinal layers and delineate fluid pockets in OCT images |
| 7 | Progress In Retinal and Eye Research | Artificial intelligence in retina | 278 | Introducing AI to the retina |
| 8 | Ophthalmology | Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning | 233 | A DL method which automatically detects and quantifies intra retinal cystic and subretinal fluid |
| 9 | Biomedical Optics Express | Deep-learning based, automated segmentation of macular edema in optical coherence tomography | 181 | A segmentation method based on DL and segmented intraretinal fluid |
| 10 | Progress in Retinal and Eye Research | Deep learning in ophthalmology: The technical and clinical considerations | 171 | Technologies and considerations are outlined for the construction of DL algorithms in ophthalmological/clinical settings |