Evidence map›Paper›PMID 34615666›Full record

ArticleThe British journal of ophthalmology2023

Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure.

Alice Shen, Michael Chiang, Anmol A Pardeshi, Roberta McKean-Cowdin, Rohit Varma, Benjamin Y Xu

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

Article in The British journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact, top 87% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 0 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Review
  5. Review
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 2 institutions in 1 country.

Alice ShenDepartment of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA.
Michael ChiangDepartment of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA.
Anmol A PardeshiDepartment of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA.
Roberta McKean-CowdinDepartment of Preventive Medicine, USC Keck School of Medicine, Los Angeles, California, USA.
Rohit VarmaSouthern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, California, USA.
Benjamin Y XuDepartment of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA Benjamin.Xu@med.usc.edu.ORCID 0000-0003-1573-988X
University of Southern California · USKeck Hospital of USC · US

Funding

Chinese American Eye StudyU10EY017337 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VARMA, ROHIT · 2009 to 2013
$10.2M
Ophthalmic Therapeutics Engineering CoreP30EY029220 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Qifa Zhou · 2018 to 2026
$6.5M
Development and Validation of Quantitative Anterior Segment OCT-based Methods to Evaluate Patients with Primary Angle Closure DiseaseK23EY029763 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI XU, BENJAMIN Y. · 2019 to 2023
$1.1M
NEI NIH HHS K23 EY029763NEI NIH HHS P30 EY029220NEI NIH HHS U10 EY017337
6 · The paper itself

Abstract

BACKGROUND/

aimsTo identify biometric parameters that explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.

methodsChinese American Eye Study (CHES) participants underwent gonioscopy and AS-OCT of each angle quadrant. A subset of CHES AS-OCT images were analysed using a deep learning classifier to detect positive angle closure based on manual gonioscopy by a reference human examiner. Parameter measurements were compared between four prediction classes: true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FN). Logistic regression models were developed to differentiate between true and false predictions. Performance was assessed using area under the receiver operating curve (AUC) and classifier accuracy metrics.

results584 images from 127 participants were analysed, yielding 271 TPs, 224 TNs, 77 FPs and 12 FNs. Parameter measurements differed (p<0.001) between prediction classes among anterior segment parameters, including iris curvature (IC) and lens vault (LV), and angle parameters, including angle opening distance (AOD). FP resembled TP more than FN and TN in terms of anterior segment parameters (steeper IC and higher LV), but resembled TN more than TP and FN in terms of angle parameters (wider AOD). Models for detecting FP (AUC=0.752) and FN (AUC=0.838) improved classifier accuracy from 84.8% to 89.0%.

conclusionsMisclassifications by an OCT-based deep learning classifier for detecting gonioscopic angle closure are explained by disagreement between anterior segment and angle parameters. This finding could be used to improve classifier performance and highlights differences between gonioscopic and AS-OCT definitions of angle closure.

Indexed as

Deep LearningGlaucoma, Angle-ClosureAnterior Eye SegmentBiometryGonioscopyHumansIntraocular PressureProspective StudiesTaurineTomography, Optical Coherence2-(N-cyclohexylamino)ethanesulfonic acidTaurineanglediagnostic tests/investigationglaucomaimaging

Identifiers

PMID34615666
PMCPMC8983788
OpenAlexW3180575167

What OpenQuestion holds

Textmetadata
LicenceTDM
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.