ArticleJournal of clinical medicine2020
Evaluating the Effect of Topical Atropine Use for Myopia Control on Intraocular Pressure by Using Machine Learning.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Cognitive benefits of exercise interventions: an fMRI activation likelihood estimation meta-analysis.Brain structure & function · 2021Pooled it
- Exploratory multivariate adaptive regression splines modeling of myopic progression in children receiving atropine.Journal of the Chinese Medical Association : JCMA · 2026Article
- Glucose-Lowering Therapies and Cognitive Decline: From Molecular Mechanisms to Clinical Evidence and Future Perspectives.Advances in therapy · 2026Review
- Dose-Related Associations Between Physical Activity and Multimorbidity Among Middle-Aged and Older Adults.Journal of prevention (2022) · 2026Article
- Intraocular Pressure Trends in Children with Myopia Receiving Atropine Therapy.Ophthalmology and therapy · 2025Article
- The Comparison of Insulin Resistance Between Normal and Early Menopause Women Younger than Fifty Years Old by Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2025Article
- IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025Review
- The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children.Medicina (Kaunas, Lithuania) · 2024Article
- Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.Journal of personalized medicine · 2024Article
- Bibliometric analysis of hotspots and trends of global myopia research.International journal of ophthalmology · 2024Article
- Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women.Life (Basel, Switzerland) · 2023Article
- Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.World journal of clinical cases · 2023Article
- Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.Journal of the Chinese Medical Association : JCMA · 2023Article
- Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese.World journal of clinical cases · 2023Article
- Symptoms and ocular findings associated with administration of 0.01% atropine in young adults.Clinical & experimental optometry · 2023Article
- Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.Journal of clinical medicine · 2023Article
- Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran.International journal of environmental research and public health · 2023Article
- Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.Risk management and healthcare policy · 2023Article
- Artificial intelligence technology for myopia challenges: A review.Frontiers in cell and developmental biology · 2023Review
- Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Atropine is a common treatment used in children with myopia. However, it probably affects intraocular pressure (IOP) under some conditions. Our research aims to analyze clinical data by using machine learning models to evaluate the effect of 19 important factors on intraocular pressure (IOP) in children with myopia treated with topical atropine. The data is collected on 1545 eyes with spherical equivalent (SE) less than -10.0 diopters (D) treated with atropine for myopia control. Four machine learning models, namely multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost), were used. Linear regression (LR) was used for benchmarking. The 10-fold cross-validation method was used to estimate the performance of the five methods. The main outcome measure is that the 19 important factors associated with atropine use that may affect IOP are evaluated using machine learning models. Endpoint IOP at the last visit was set as the target variable. The results show that the top five significant variables, including baseline IOP, recruitment duration, age, total duration and previous cumulative dosage, were identified as most significant for evaluating the effect of atropine use for treating myopia on IOP. We can conclude that the use of machine learning methods to evaluate factors that affect IOP in children with myopia treated with topical atropine is promising. XGBoost is the best predictive model, and baseline IOP is the most accurate predictive factor for endpoint IOP among all machine learning approaches.
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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.