Evidence map›Paper›PMID 41275196›Full record

ArticleBMC ophthalmology2025

Utilization of an automated machine learning approach for the detection of granular corneal dystrophy via slit lamp photographs.

Negin Yavari, S Saeed Mohammadi, Jared T Sokol, Dalia El Feky, MohammadBagher Rajabi, Jia-Horung Hung, Christopher Or, Osama Elaraby, Frances A Anover, Aim-On Saengsirinavin and 9 more

Abstract read
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In one paragraph

Article in BMC ophthalmology, 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

19 authors.

Negin YavariByers Eye Institute, Stanford University, Palo Alto, CA, USA.
S Saeed MohammadiByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Jared T SokolByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Dalia El FekyByers Eye Institute, Stanford University, Palo Alto, CA, USA.
MohammadBagher RajabiByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Jia-Horung HungByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Christopher OrByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Osama ElarabyByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Frances A AnoverByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Aim-On SaengsirinavinByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Amir AkhavanrezayatByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Azadeh MobasserianByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Ngoc Trong Tuong ThanByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Jingli GuoByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Yue BaiByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Cigdem YasarByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Natalie A AfshariShiley Eye Institute, University of San Diego, La Jolla, CA, USA.
Charles C LinByers Eye Institute, Stanford University, Palo Alto, CA, USA.
Quan Dong NguyenByers Eye Institute, Stanford University, Palo Alto, CA, USA. ndquan@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis study aims to apply automated machine learning (AutoML) techniques for the diagnosis of granular corneal dystrophy (GCD), a rare inherited condition characterized by progressive protein deposition in the corneal stroma.

methodsPatients diagnosed with GCD who had slit-lamp photographs of the affected eye(s) were enrolled in the study. Individuals with concomitant corneal conditions, ungradable imaging data, or uncertain diagnoses were excluded from the study. Slit-lamp photos depicting the GCD and non-GCD were obtained from the Byers Eye Institute, Stanford University. Image processing included resizing and cropping, focusing solely on the cornea. A deep learning model was subsequently deployed, utilizing Vertex-AI, the AutoML platform developed by Google (Menlo Park, CA). The area under the precision‒recall curve (AUPRC) was plotted, and the sensitivity, specificity, positive predictive value (PPV), accuracy (AC), and F1 score were calculated.

resultsThe model was trained on a dataset comprising 223 images, consisting of 72 GCD and 151 non-GCD images. One hundred seventy six images were used for training, 24 were used for validation, and 23 were used for testing the model. The AUPRC for the model was 0.995 and precision and recall were both 95.70% at a confidence threshold of 0.5. The sensitivity, specificity, PPV, AC, and F1 score of the model were 93.30%, 100%, 100%, 95.70%, and 0.965, respectively.

conclusionsA clinician-derived AutoML model successfully identified GCD from slit lamp photographs with high accuracy.

Indexed as

Corneal Dystrophies, HereditaryMachine LearningPhotographySlit LampSlit Lamp MicroscopyAdultAgedDeep LearningFemaleHumansMaleMiddle AgedSensitivity and SpecificityYoung AdultAutomated machine learningGranular corneal dystrophyMachine learning

Identifiers

PMID41275196
PMCPMC12642381

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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.