Evidence map›Paper›PMID 39875492›Full record

ArticleScientific reports2025

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Observational
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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.

Md Mahmudul HasanSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia. md_mahmudul.hasan@unsw.edu.au.
Jack PhuSchool of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia.
Henrietta WangSchool of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia.
Arcot SowmyaSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
Michael KalloniatisSchool of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia.
Erik MeijeringSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.

Funding

National Health and Medical Research Council 1186915
6 · The paper itself

Abstract

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. A total of 334 normal and 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) were included, signal processing theory was employed, and model interpretability was rigorously evaluated. Leveraging SHapley Additive exPlanations (SHAP)-based global feature ranking and partial dependency analysis (PDA) estimated decision boundary cut-offs on machine learning (ML) models, a novel algorithm was developed to implement an XAI tool. Using the selected features, ML models produce an AUC of 0.96 (95% CI: 0.95-0.98), 0.98 (95% CI: 0.96-1.00) and 1.00 (95% CI: 1.00-1.00) respectively on differentiating early, moderate and advanced glaucoma patients. Overall, machine outperformed clinicians in the early stage and overall glaucoma diagnosis with 10.4 -11.2% higher accuracy. The developed user-friendly XAI software tool shows potential as a valuable tool for eye care practitioners, offering transparent and interpretable insights to improve decision-making.

Indexed as

GlaucomaMachine LearningTomography, Optical CoherenceAgedAlgorithmsFemaleHumansMaleMiddle AgedExplainable machine learningGlaucomaOptical coherence tomographyPartial dependency analysisPerimetrySHAP analysis

Identifiers

PMID39875492
PMCPMC11775169

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Registered trials

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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.