Evidence map›Paper›PMID 40975441›Full record

ReviewSurvey of ophthalmology

Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.

Euna Lee, David Hunt, Yavuz Cakir, David Kuo, Ziqi Zhou, Miroslav Pajic, Majda Hadziahmetovic

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Survey of ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Euna LeeUniversity of Massachusetts Chan Medical School, Worcester, Massachusetts, USA. Electronic address: euna.lee@umassmed.edu.
David HuntDepartment of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA. Electronic address: david.hunt@duke.edu.
Yavuz CakirDepartment of Ophthalmology, Duke University, Durham, North Carolina, USA. Electronic address: mdyavuzcakir@gmail.com.
David KuoDepartment of Ophthalmology, Duke University, Durham, North Carolina, USA. Electronic address: david.kuo@duke.edu.
Ziqi ZhouDepartment of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA. Electronic address: ziqi.zhou@duke.edu.
Miroslav PajicDepartment of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA; Department of Computer Science, Duke University, Durham, North Carolina, USA. Electronic address: miroslav.pajic@duke.edu.
Majda HadziahmetovicDepartment of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA; Department of Ophthalmology, Duke University, Durham, North Carolina, USA. Electronic address: majda.hadziahmetovic@duke.edu.

Funding

Learning-based 3D modeling of AMD to assess disease progression and response to treatmentR21EY033480 · NEI · DUKE UNIVERSITY · PI HADZIAHMETOVIC, MAJDA · 2023 to 2023
$434k
NEI NIH HHS R21 EY033480
6 · The paper itself

Abstract

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in older adults. While anti-vascular endothelial growth factor (anti-VEGF) therapy and novel treatments for geographic atrophy have improved management, timely diagnosis and personalized intervention remain a challenge. Artificial intelligence (AI), such as machine learning and deep learning models, shows promise in AMD diagnosis, classification, and treatment planning. This review summarizes AI's recent advancements, highlights its clinical utility, and addresses key limitations for wider real-world implementation in AMD. We conducted systematic search of PubMed from its conception up to August 1, 2024. Studies utilizing AI-based algorithms for AMD management were identified and categorized into early detection/classification and prediction of disease progression/treatment response. Data extraction focused on AI model performance, imaging modalities, and clinical applicability. Of 193 records screened, 47 studies were included, in which 19 studies focused on early detection/classification and 28 on prediction of disease progression/treatment response. AI models demonstrated high accuracy in AMD classification and progression prediction, including in real-world settings. Prediction models for treatment response, particularly anti-VEGF therapy, could provide recommendations on optimizing injection timelines. Recent studies have also begun tackling previous challenges, such as algorithmic biases, limited generalizability, and AI's "black-box" nature. AI-based models offer significant potential to transform AMD care through timely detection and personalized treatment; however, clinical integration depends on improving model interpretability and validating tools across diverse populations. As AI continues to evolve, ongoing research is needed to refine AI models and support their translation into evidence-based, real-world applicability in AMD.

Indexed as

Artificial IntelligenceMacular DegenerationAngiogenesis InhibitorsDeep LearningDisease ProgressionHumansPrognosisVascular Endothelial Growth Factor AAngiogenesis InhibitorsVascular Endothelial Growth Factor AAge-related macular degenerationAI IntegrationAlgorithmic biasArtificial IntelligenceConvolutional Neural NetworkDeep learningMachine learning algorithmsPredictive ModelingTransfer learning

Identifiers

PMID40975441
PMCPMC13165504

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.