Last Posted: Jun 15, 2021
- ARTIFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW.
Stranák Z et al. Ceska a slovenska oftalmologie : casopis Ceske oftalmologicke spolecnosti a Slovenske oftalmologicke spolecnosti 2020 1(Ahead of print) 1-8
- A deep learning system for detecting diabetic retinopathy across the disease spectrum.
Dai Ling et al. Nature communications 2021 12(1) 3242
- Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
Gayathri S et al. Physical and engineering sciences in medicine 2021
- Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography.
Nagasawa Toshihiko et al. Journal of ophthalmology 2021 20216651175
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.
Yin Jiamin et al. Journal of medical Internet research 2021 23(4) e25759
- Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.
Aggarwal Ravi et al. NPJ digital medicine 2021 4(1) 65
- Application of machine learning methods to bridge the gap between non-interventional studies and randomized controlled trials in ophthalmic patients with neovascular age-related macular degeneration.
Sagkriotis Alexandros et al. Contemporary clinical trials 2021 106364
- Automated feature-based grading and progression analysis of diabetic retinopathy.
Al-Turk Lutfiah et al. Eye (London, England) 2021
- Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records.
Jacoba Cris Martin P et al. Seminars in ophthalmology 2021 1-8
- A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System.
Mokhashi Nikita et al. Journal of diabetes science and technology 2021 1932296821999370
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