Evidence map›Paper›PMID 32344074›Full record

ArticleOphthalmology2020

Using Deep Learning to Automate Goldmann Applanation Tonometry Readings.

Ted Spaide, Yue Wu, Ryan T Yanagihara, Shu Feng, Omar Ghabra, Jonathan S Yi, Philip P Chen, Francy Moses, Aaron Y Lee, Joanne C Wen

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

Article in Ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Ted SpaideDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Yue WuDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Ryan T YanagiharaDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Shu FengDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Omar GhabraDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Jonathan S YiDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Philip P ChenDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Francy MosesDepartment of Ophthalmology, University of Washington, Seattle, Washington.
Aaron Y LeeDepartment of Ophthalmology, University of Washington, Seattle, Washington. Electronic address: leeay@uw.edu.
Joanne C WenDuke Eye Center, Duke University, Durham, North Carolina. Electronic address: joanne.wen@duke.edu.
University of Washington · USDuke University · US

Funding

Epidemiology and clinical outcomes of diabetic macular edemaK23EY029246 · NEI · UNIVERSITY OF WASHINGTON · PI LEE, AARON · 2018 to 2022
$1.1M
NEI NIH HHS K23 EY029246
6 · The paper itself

Abstract

purposeTo develop an objective and automated method for measuring intraocular pressure using deep learning and fixed-force Goldmann applanation tonometry (GAT) techniques.

designProspective cross-sectional study.

participantsPatients from an academic glaucoma practice.

methodsIntraocular pressure was estimated by analyzing videos recorded using a standard slit-lamp microscope and fixed-force GAT. Video frames were labeled to identify the outline of the reference tonometer and the applanation mires. A deep learning model was trained to localize and segment the tonometer and mires. Intraocular pressure values were calculated from the deep learning-predicted tonometer and mire diameters using the Imbert-Fick formula. A separate test set was collected prospectively in which standard and automated GAT measurements were collected in random order by 2 independent masked observers to assess the deep learning model as well as interobserver variability.

main outcome measuresIntraocular pressure measurements between standard and automated methods were compared.

resultsTwo hundred sixty-three eyes of 135 patients were included in the training and validation videos. For the test set, 50 eyes from 25 participants were included. Each eye was measured by 2 observers, resulting in 100 videos. Within the test set, the mean difference between automated and standard GAT results was -0.9 mmHg (95% limits of agreement [LoA], -5.4 to 3.6 mmHg). Mean difference between the 2 observers using standard GAT was 0.09 mmHg (LoA,-3.8 to 4.0 mmHg). Mean difference between the 2 observers using automated GAT videos was -0.3 mmHg (LoA, -4.1 to 3.5 mmHg). The coefficients of repeatability for automated and standard GAT were 3.8 and 3.9 mmHg, respectively. The bias for even-numbered measurements was reduced when using automated GAT.

conclusionsPreliminary measurements using deep learning to automate GAT demonstrate results comparable with those of standard GAT. Automated GAT has the potential to improve on our current GAT measurement standards significantly by reducing bias and improving repeatability. In addition, ocular pulse amplitudes could be observed using this technique.

Indexed as

Deep LearningAgedCross-Sectional StudiesFemaleGlaucomaHumansIntraocular PressureMaleMiddle AgedProspective StudiesReproducibility of ResultsROC CurveTonometry, Ocular

Identifiers

PMID32344074
PMCPMC7606368
OpenAlexW3017394028

What OpenQuestion holds

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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.