Evidence map›Paper›PMID 32818086›Full record

ArticleTranslational vision science & technology2020

Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD.

Alauddin Bhuiyan, Tien Yin Wong, Daniel Shu Wei Ting, Arun Govindaiah, Eric H Souied, R Theodore Smith

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 4 pooled it
6.5field-weighted citation impact, top 2% of its field
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

62 citing papers in PubMed, 4 syntheses or guidelines pooled it, 113 citations in OpenAlex.

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2 more citing papers are in PubMed but not listed here.

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

6 authors at 6 institutions in 3 countries.

Alauddin BhuiyaniHealthScreen Inc., New York, NY, USA.
Tien Yin WongSingapore Eye Research Institute, Singapore National Eye Center, Singapore.
Daniel Shu Wei TingSingapore Eye Research Institute, Singapore National Eye Center, Singapore.
Arun GovindaiahiHealthScreen Inc., New York, NY, USA.
Eric H SouiedDepartment of Ophthalmology, Hôpital Intercommunal de Créteil, Université, Creteil, France.
R Theodore SmithIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Duke-NUS Medical School · SGEcoHealth Alliance · USHôpital Intercommunal de Créteil · FRIcahn School of Medicine at Mount Sinai · USNew York University · USSingapore National Eye Center · SG

Funding

A Novel Image Based Screening System for Early Detection of Age-related Macular DegenerationR43EY026841 · NEI · IHEALTHSCREEN, INC. · PI BHUIYAN, ALAUDDIN · 2016 to 2017
$289k
NEI NIH HHS R43 EY026841
6 · The paper itself

Abstract

Purpose: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years. Methods: The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study. First Step: An ensemble of deep learning screening methods was trained and validated on 116,875 color fundus photos from 4139 participants in the AREDS study to classify them as no, early, intermediate, or advanced AMD and further stratified them along the AREDS 12 level severity scale. Second step: the resulting AMD scores were combined with sociodemographic clinical data and other automatically extracted imaging data by a logistic model tree machine learning technique to predict risk for progression to late AMD within 1 or 2 years, with training and validation performed on 923 AREDS participants who progressed within 2 years, 901 who progressed within 1 year, and 2840 who did not progress within 2 years. For those found at risk of progression to late AMD, we further predicted the type (dry or wet) of the progression of late AMD. Results: For identification of early/none vs. intermediate/late (i.e., referral level) AMD, we achieved 99.2% accuracy. The prediction model for a 2-year incident late AMD (any) achieved 86.36% accuracy, with 66.88% for late dry and 67.15% for late wet AMD. For the NAT-2 dataset, the 2-year late AMD prediction accuracy was 84%. Conclusions: Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment. Translational Relevance: Noninvasive, highly accurate, and fast AI methods to screen for referral level AMD and to predict late AMD progression offer significant potential improvements in our care of this prevalent blinding disease.

Indexed as

Artificial IntelligenceWet Macular DegenerationFundus OculiHumansMachine LearningSeverity of Illness IndexAMD predictiondeep learningdry AMDwet AMD

Identifiers

PMID32818086
PMCPMC7396183
OpenAlexW3018398338

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.