Evidence map›Paper›PMID 40372543›Full record

ArticleJapanese journal of ophthalmology2025

Evaluating efficacy of 0.125% atropine using a myopia progression machine learning model.

Chang Yoon Han, Sa Ra Kim, Dae Hee Kim

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Article in Japanese journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Chang Yoon HanDepartment of Ophthalmology, Kim's Eye Hospital, #136 Yeongsin-ro, Yeongdeungpo-gu, Seoul, 07301, Republic of Korea.
Sa Ra KimDepartment of Ophthalmology, Kim's Eye Hospital, #136 Yeongsin-ro, Yeongdeungpo-gu, Seoul, 07301, Republic of Korea.
Dae Hee KimDepartment of Ophthalmology, Kim's Eye Hospital, #136 Yeongsin-ro, Yeongdeungpo-gu, Seoul, 07301, Republic of Korea. skdh17@hanmail.net.ORCID http://orcid.org/0000-0002-9048-551X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo investigate the usefulness of a machine learning (ML) model that can predict the natural course of childhood myopia in evaluation of the inhibitory effects of 0.125% atropine on the progression of childhood myopia. STUDY

designRetrospective, cohort study.

methodsA retrospective analysis was conducted on children treated daily with 0.125% atropine eye drops. The children were classified into 6-, 12-, 18-, 24-, and 30-month group based on the treatment duration. Spherical equivalents (SE) at the last treatment time point were compared with the pretreatment and ML-predicted SE. The myopia suppression rate due to treatment was calculated based on the first- and ML-predicted SE.

resultsA total of 771 eyes (402 boys and 369 girls) from 397 children were included. The participants' mean age was 8.0 ± 1.5 years. The first SE of -2.87 ± 1.67 diopters (D), treatment led to a mean SE of -3.44 ± 1.90 D, showing a significant reduction in myopia progression compared to the ML model's prediction of -4.12 ± 1.75 D. Except for the 6-month group, the last SE was statistically significantly less myopic than the predicted SE, indicating that treatment suppressed progression compared to the natural course. The mean myopia suppression rate was 53.5%, with definite differences between the groups.

conclusionTreatment with 0.125% atropine may suppress myopia progression in children compared with the ML child myopia prediction model. The application of a machine learning model to predict myopia progression may assist in evaluating the efficacy of 0.125% atropine treatment.

Indexed as

AtropineMachine LearningMyopiaRefraction, OcularChildChild, PreschoolDisease ProgressionFemaleFollow-Up StudiesHumansMaleMydriaticsOphthalmic SolutionsRetrospective StudiesTreatment OutcomeAtropineMydriaticsOphthalmic SolutionsAtropineChildhoodMachine learningMyopia

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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.