Evidence map›Paper›PMID 40900875›Full record

ReviewWorld journal of methodology2025

Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.

Rajwinder Kaur, Arvind Kumar Morya, Parul C Gupta, Sarita Aggarwal, Nitin K Menia, Amanjot Kaur, Sukhchain Kaur, Sony Sinha

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Rajwinder KaurDepartment of Ophthalmology, Adesh Institute of Medical Sciences and Research, Bathinda 151101, Punjab, India.
Arvind Kumar MoryaDepartment of Ophthalmology, All India Institute of Medical Sciences, Hyderabad 508126, Telangana, India. bulbul.morya@gmail.com.
Parul C GuptaDepartment of Ophthalmology, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, Punjab, India.
Sarita AggarwalDepartment of Ophthalmology, Santosh Deemed to be University, Ghaziabad, Ghaziabad 201009, Uttar Pradesh, India.
Nitin K MeniaDepartment of Ophthalmology, All India Institute of Medical Sciences, Vijaypur 180001, Jammu and Kashmīr, India.
Amanjot KaurDepartment of Pharmacology, Adesh Institute of Medical Sciences and Research, Bathinda 151101, Punjab, India.
Sukhchain KaurCentre for Interdisciplinary Biomedical Research, Adesh Institute of Medical Sciences and Research, Bathinda 151101, Punjab, India.
Sony SinhaDepartment of Ophthalmology - Vitreo-Retina, Neuro-Ophthalmology and Oculoplasty, All India Institute of Medical Sciences, Patna 801507, Bihar, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI), encompassing machine learning and deep learning, is being extensively used in medical sciences. It is slated to positively impact the diagnosis and prognostication of various diseases. Deep learning, a subset of AI, has been instrumental in diagnosing diabetic retinopathy (DR), diabetic macular edema, glaucoma, age-related macular degeneration, and numerous other ocular diseases. AI performs equally well in the early prediction of glaucoma and age-related macular degeneration. Integrating AI with telemedicine promises to improve healthcare delivery, although challenges persist in implementing AI algorithms, especially in developing countries. This review provides a comprehensive summary of AI, its applications in ophthalmology, particularly DR, the diverse algorithms utilized for different ocular conditions, and prospects for the future integration of AI in eye care.

Indexed as

Age-related macular degenerationAlzheimer's diseaseArtificial intelligenceAutomatic retinal image analysisChronic kidney diseaseConvolutional neural networksDiabetic macular edemaDiabetic retinopathyInternational council of ophthalmologyMachine learningMassive training artificial neural networksNatural language processingOCT angiographyOptical coherence tomographyVision transformers

Identifiers

PMID40900875
PMCPMC12400382

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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.