ArticlePeerJ2019
Improving clinical refractive results of cataract surgery by machine learning.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 56 citations in OpenAlex.
- Advanced Artificial-Intelligence-Based Jiang Formula for Intraocular Lens Power in Congenital Ectopia Lentis.Translational vision science & technology · 2025Article
- Intraocular lens calculation formula developed using artificial intelligence for ultrasonic biometry.Arquivos brasileiros de oftalmologia · 2025Article
- Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis.International ophthalmology · 2024Article
- Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- The Zhu-Lu formula: a machine learning-based intraocular lens power calculation formula for highly myopic eyes.Eye and vision (London, England) · 2023Article
- Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence in ophthalmology: A multidisciplinary approach.Integrative medicine research · 2022Article
- Application of artificial intelligence in cataract management: current and future directions.Eye and vision (London, England) · 2022Review
- Applications of Artificial Intelligence in Myopia: Current and Future Directions.Frontiers in medicine · 2022Review
- Machine learning adaptation of intraocular lens power calculation for a patient group.Eye and vision (London, England) · 2021Article
- Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2021Review
- Determining the Theoretical Effective Lens Position of Thick Intraocular Lenses for Machine Learning-Based IOL Power Calculation and Simulation.Translational vision science & technology · 2021Article
- Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery.Journal of clinical medicine · 2021Article
- Improvement of Multiple Generations of Intraocular Lens Calculation Formulae with a Novel Approach Using Artificial Intelligence.Translational vision science & technology · 2021Article
- Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.Translational vision science & technology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow.
backgroundCurrent IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient.
methodsA dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR).
resultsBoth models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the ±0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula.
conclusionIn comparison to the current clinical method, both SVM-RM and MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.
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Registered trials
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.