Evidence map›Paper›PMID 41529934›Full record

Observational studyBMJ open ophthalmology2026

AI-MK: artificial intelligence for assessing and monitoring microbial keratitis.

Colby Hart, Xu Chen, Mahmoud Ahmed, Matteo Airaldi, Alfredo Borgia, Daniel Mahini, Tobi Somerville, Saaeha Rauz, Adela Hulpus, Vito Romano and 4 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMJ open ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Colby Hart *Cornea, The Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0002-3739-8177
Xu Chen *Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
Mahmoud AhmedDepartment of Eye and Vision Science, University of Liverpool, Liverpool, UK.
Matteo AiraldiDepartment of Biomedical and Clinical Science "Luigi Sacco", University of Milan, Milano, Italy.
Alfredo BorgiaDepartment of Corneal and External Eye Diseases, Royal Liverpool University Hospital, Liverpool, UK.ORCID http://orcid.org/0000-0002-3976-242X
Daniel MahiniThe Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia.
Tobi SomervilleDepartment of Eye and Vision Sciences, University of Liverpool, Institute of Ageing and Chronic Disease, Liverpool, UK.ORCID http://orcid.org/0009-0004-5662-5138
Saaeha RauzAcademic Unit of Ophthalmology, University of Birmingham, Birmingham, UK s.rauz@bham.ac.uk.
Adela HulpusDepartment of Eye and Vision Science, University of Liverpool, Liverpool, UK.
Vito RomanoUniversity of Brescia, Brescia, Italy.ORCID http://orcid.org/0000-0002-5148-7643
Gibran ButtAcademic Unit of Ophthalmology, University of Birmingham College of Medical and Dental Sciences, Birmingham, UK.ORCID http://orcid.org/0000-0001-5306-4647
Giulia CocoOphthalmology, Catholic University, Rome, Italy.ORCID http://orcid.org/0000-0002-2410-6366
Yalin ZhengDepartment of Eye and Vision Science, University of Liverpool, Liverpool, UK.ORCID http://orcid.org/0000-0002-7873-0922
Stephen KayeDepartment of Eye and Vision Science, University of Liverpool, Liverpool, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimsTo evaluate the performance of an artificial intelligence (AI) model for detecting and monitoring microbial keratitis (MK) using anterior segment optical coherence tomography (AS-OCT).

methodsThis is a prospective observational study. Patients with clinically suspected MK and healthy participants were included. In addition to routine assessment and treatment with topical fluoroquinolone therapy, patients underwent AS-OCT at each clinic visit. These images were tested on our DeepLabV3 network-based AI model, which aims to diagnose and record changes to infiltrate sizes of MK lesions over time.

resultsThe AI model accurately captured MK lesions in 93% of cases (152/163). MK was not detected in scans from healthy eyes, and there were no cases of artefact being falsely detected. The model had a sensitivity of 93% (95% CI 88% to 97%), specificity of 100% (95% CI 88% to 100%), positive predictive value of 100% (95% CI 98% to 100%) and negative predictive value of 73% (95% CI 61% to 83%). Using only the corneal component with masking of the anterior chamber, the AI model showed agreement on change with both observers in 76% (13/18) cases.

conclusionsThis AI framework reliably identified MK lesions using AS-OCT, with high sensitivity and specificity. The framework was able to identify change in most cases compared with corneal specialists.

Indexed as

Artificial IntelligenceCorneaEye Infections, BacterialKeratitisTomography, Optical CoherenceAdultAgedFemaleHumansMaleMiddle AgedProspective StudiesCorneaInfection

Identifiers

PMID41529934
PMCPMC12815086

What Socratic holds

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LicenceCC BY-NC
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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.