Evidence map›Paper›PMID 33836989›Full record

ArticleThe British journal of ophthalmology2022

AI-powered effective lens position prediction improves the accuracy of existing lens formulas.

Tingyang Li, Joshua Stein, Nambi Nallasamy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The British journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Tingyang LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Joshua SteinKellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Nambi NallasamyDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA nnallasa@med.umich.edu.ORCID http://orcid.org/0000-0001-7501-7198

Funding

Personalized Forecasting of Disease Trajectory for Patients with Open Angle GlaucomaR01EY026641 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LAVIERI-WILLIAMS, MARIEL SOFIA, STEIN, JOSHUA D · 2016 to 2020
$2.9M
NEI NIH HHS R01 EY026641
6 · The paper itself

Abstract

aimsTo assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas.

methodsA dataset of 4806 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction.

resultsWhen the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs±SD (in Diopters) in the testing set were: 0.356±0.329 for Haigis, 0.352±0.319 for Hoffer Q, 0.371±0.336 for Holladay, and 0.361±0.331 for SRK/T which were significantly lower (p<0.05) than those of the original formulas: 0.373±0.328 for Haigis, 0.408±0.337 for Hoffer Q, 0.384±0.341 for Holladay and 0.394±0.351 for SRK/T.

conclusionUsing a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.

Indexed as

Lenses, IntraocularPhacoemulsificationArtificial IntelligenceBiometryHumansOptics and PhotonicsRefraction, OcularRetrospective Studieslens and zonulestreatment surgery

Identifiers

PMID33836989
PMCPMC9411905

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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.