ReviewBMC ophthalmology2024
Artificial intelligence in age-related macular degeneration: state of the art and recent updates.
Review in BMC ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed, 32 citations in OpenAlex.
- Machine Learning-Based Prediction of Strabismus with and without Amblyopia from Fixation Eye Movements.Ophthalmology science · 2026Article
- Comparing Interactive Machine Learning and Unassisted Segmentation of Geographic Atrophy From Fundus Autofluorescence.Translational vision science & technology · 2026Article
- Dissemination and implementation science for global ophthalmology: closing the last-mile translational gap.Frontiers in public health · 2026Review
- Real-world evaluation of an OCT-based AI decision-support system for neovascular AMD activity triage in teleophthalmology.Frontiers in ophthalmology · 2026Article
- CSF-net: a color space fusion network with self-attention-driven feature learning for feline ocular diseases classification.Frontiers in veterinary science · 2026Article
- Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging.Scientific reports · 2025Article
- AI-Based Response Classification After Anti-VEGF Loading in Neovascular Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2025Article
- A Comparative Study Between Clinical Optical Coherence Tomography (OCT) Analysis and Artificial Intelligence-Based Quantitative Evaluation in the Diagnosis of Diabetic Macular Edema.Vision (Basel, Switzerland) · 2025Article
- Pharmacoproteomics in the development of personalised medicine in Age-related Macular Degeneration (PHARPRO-AMD) study protocol.BMJ open ophthalmology · 2025Article
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- Advances in materials science for ocular diseases induced by cardiovascular risk factors.Frontiers in bioengineering and biotechnology · 2025Review
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score.Biomedicines · 2024Article
- Genetic Insights into Age-Related Macular Degeneration.Biomedicines · 2024Review
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age related macular degeneration (AMD) represents a leading cause of vision loss and it is expected to affect 288 million people by 2040. During the last decade, machine learning technologies have shown great potential to revolutionize clinical management of AMD and support research for a better understanding of the disease. The aim of this review is to provide a panoramic description of all the applications of AI to AMD management and screening that have been analyzed in recent past literature. Deep learning (DL) can be effectively used to diagnose AMD, to predict short term risk of exudation and need for injections within the next 2 years. Moreover, DL technology has the potential to customize anti-VEGF treatment choice with a higher accuracy than expert human experts. In addition, accurate prediction of VA response to treatment can be provided to the patients with the use of ML models, which could considerably increase patients' compliance to treatment in favorable cases. Lastly, AI, especially in the form of DL, can effectively predict conversion to GA in 12 months and also suggest new biomarkers of conversion with an innovative reverse engineering approach.
Indexed as
Identifiers
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.