ReviewEye and vision (London, England)2020
Application of machine learning in ophthalmic imaging modalities.
Review in Eye and vision (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 154 citations in OpenAlex.
- Surgical outcomes and risk factors identification for rhegmatogenous retinal detachment repair by pneumatic retinopexy using pure air.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.ACM transactions on computing for healthcare · 2026Article
- Cataract Management in the Modern Era: Therapeutic Advances and Unmet Needs.Current pharmaceutical design · 2026Review
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- FOVEA: Preoperative and intraoperative retinal fundus images with optic disc and retinal vessel annotations.Scientific data · 2025Article
- OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.Scientific data · 2025Article
- Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study.International journal of retina and vitreous · 2025Article
- Conjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.Translational vision science & technology · 2025Article
- Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee.Pain practice : the official journal of World Institute of Pain · 2025Article
- Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data.Diagnostics (Basel, Switzerland) · 2024Article
- Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.PLOS digital health · 2024Review
- Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.Eye and vision (London, England) · 2024Article
- CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.Diagnostics (Basel, Switzerland) · 2024Article
- Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions.Diagnostics (Basel, Switzerland) · 2024Article
- Comparison of machine learning-based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.BMJ open ophthalmology · 2023Review
- An intelligent decision support system for acute postoperative endophthalmitis: design, development and evaluation of a smartphone application.BMC medical informatics and decision making · 2023Article
- Approach to glaucoma diagnosis and prediction based on multiparameter neural network.International ophthalmology · 2023Article
- Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images.Journal of biomedical optics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In clinical ophthalmology, a variety of image-related diagnostic techniques have begun to offer unprecedented insights into eye diseases based on morphological datasets with millions of data points. Artificial intelligence (AI), inspired by the human multilayered neuronal system, has shown astonishing success within some visual and auditory recognition tasks. In these tasks, AI can analyze digital data in a comprehensive, rapid and non-invasive manner. Bioinformatics has become a focus particularly in the field of medical imaging, where it is driven by enhanced computing power and cloud storage, as well as utilization of novel algorithms and generation of data in massive quantities. Machine learning (ML) is an important branch in the field of AI. The overall potential of ML to automatically pinpoint, identify and grade pathological features in ocular diseases will empower ophthalmologists to provide high-quality diagnosis and facilitate personalized health care in the near future. This review offers perspectives on the origin, development, and applications of ML technology, particularly regarding its applications in ophthalmic imaging modalities.
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What OpenQuestion holds
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.