ArticleJournal of optometry2022
Prediction of manifest refraction using machine learning ensemble models on wavefront aberrometry data.
Article in Journal of optometry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
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Who cites it
8 citing papers in PubMed.
- Artificial Intelligence-Based Prediction of Subjective Refraction and Clinical Determinants of Prediction Error.Diagnostics (Basel, Switzerland) · 2026Article
- Prevalence of uncorrected refractive error in low-resource high schools in the Free State, South Africa.African journal of primary health care & family medicine · 2025Article
- Refractive assessment by wavefront aberrometry compared to subjective refraction in PRK patients.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- Improving the estimation of the spherical equivalent subjective refraction using objective information on accommodation.Biomedical optics express · 2025Article
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Ensemble machine learning prediction model for clinical refraction using partial interferometry measurements in childhood.PloS one · 2025Article
- Ametropia detection using a novel, compact wavefront autorefractor.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2024Article
- Journal of Optometry Impact Factor: it's been a long way to here.Journal of optometryArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo assess the performance of machine learning (ML) ensemble models for predicting patient subjective refraction (SR) using demographic factors, wavefront aberrometry data, and measurement quality related metrics taken with a low-cost portable autorefractor.
methodsFour ensemble models were evaluated for predicting individual power vectors (M, J
resultsAll models considerably outperformed the predictions from the autorefractor, while ASB obtained the best results. The accuracy of the predictions for each individual power vector component was substantially improved resulting in a ± 0.63 D, ±0.14D, and ±0.08 D reduction in the 95% limits of agreement of the error distribution for M, J
conclusionsThese results suggest that ML is effective for improving precision in predicting patient's SR from objective measurements taken with a low-cost portable device.
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Registered trials
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