ArticleNPJ digital medicine2020
Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.Frontiers in endocrinology · 2023Pooled it
- Real-World Evaluation of AI-Driven Diabetic Retinopathy Screening in Public Health Settings: Validation and Implementation Study.JMIR medical informatics · 2025Article
- Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa.JMIR formative research · 2024Article
- Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review.Eye (London, England) · 2024Review
- Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.Progress in retinal and eye research · 2022Review
- Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks.Eye and vision (London, England) · 2022Article
- Artificial intelligence to detect malignant eyelid tumors from photographic images.NPJ digital medicine · 2022Article
- Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis.Diagnostics (Basel, Switzerland) · 2021Article
- Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.Scientific reports · 2021Article
- Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.Translational vision science & technology · 2021Article
- Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.Medical & biological engineering & computing · 2021Article
- Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy.Journal of diabetes research · 2021Article
- Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.