Evidence map›Paper›PMID 40298745›Full record

ArticleArquivos brasileiros de oftalmologia2025

Intraocular lens calculation formula developed using artificial intelligence for ultrasonic biometry.

Victor Antonio Kuiava, Eliseu Luiz Kuiava, Eduardo Ottobeli Chielli, Diane Marinho Ruschel, Samara Bárbara Marafon

Abstract read
In one paragraph

Article in Arquivos brasileiros de oftalmologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Victor Antonio KuiavaDepartamento de Oftalmologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.ORCID 0000-0002-0332-4604
Eliseu Luiz KuiavaDepartamento de Engenharia Elétrica, Centro Universitário Internacional, São Miguel do Oeste, Santa Catarina, Brazil.ORCID 0000-0002-6493-4510
Eduardo Ottobeli ChielliDepartamento de Ciências pela Vida, Universidade do Oeste de Santa Catarina São Miguel do Oeste, Santa Catarina, Brazil.ORCID 0000-0003-3566-1258
Diane Marinho RuschelDepartamento de Oftalmologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.ORCID 0000-0002-0050-5039
Samara Bárbara MarafonDepartamento de Oftalmologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil.ORCID 0000-0002-7397-9112

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe developed an artificial intelligence program for calculating intraocular lenses and analyzed its accuracy rate via ultrasonic biometry. This endeavor is aimed at enhancing precision and efficacy in the selection of intraocular lenses, particularly in cases where optical biometry is unavailable.

methodsData was collected from the Hospital de Clínicas de Porto Alegre, which included cases of phacoemulsification with intraocular lens implantation, in which the lens selection was based on ultrasonic biometry. The program, implemented in Python, Java, and PHP, employs the ridge regression method. Two design options were developed: a basic model, which uses only keratometry variables (K1 and K2), axial size and final target refraction in the spherical equivalent, and an advanced model, which incorporates preoperative refraction and the patient's age. The Universal Barrett II formula was used to compare both models.

resultsThe sample consisted of 486 eyes from 313 patients, with 350 eyes used for program training and 136 for program validation. The spherical equivalent hit rates, with a variation of ±0.5 D, were 86% and 87.5% for the basic and advanced models, respectively, with no statistically significant difference between them. With the Barret Universal II formula, the success rate was 69%, which was significantly different from the values of the two aforementioned models (p<0.0001). The system was better for medium and long eyes but worse for short eyes (<=22.00 mm).

conclusionThe developed artificial intelligence program was superior to the Barrett formula in terms of performance, in the general context and within the subgroup of patients with longer eyes. This innovation can considerably contribute to the selection of intraocular lenses, particularly in cases where optical biometry is unavailable.

Indexed as

Artificial IntelligenceBiometryLenses, IntraocularAdultAgedAged, 80 and overFemaleHumansLens Implantation, IntraocularMaleMiddle AgedPhacoemulsificationRefraction, OcularReproducibility of Results

Identifiers

PMID40298745
PMCPMC12997603

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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.