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Inducement and Evaluation of a Murine Model of Experimental Myopia
Published on: January 22, 2019
Evaluating efficacy of 0.125% atropine using a myopia progression machine learning model
Chang Yoon Han1, Sa Ra Kim1, Dae Hee Kim2,3
1Department of Ophthalmology, Kim's Eye Hospital, #136 Yeongsin-ro, Yeongdeungpo-gu, Seoul, 07301, Republic of Korea.
Insights
Low-dose atropine eye drops effectively slowed childhood myopia progression compared to a machine learning prediction model. This study highlights the potential of machine learning in evaluating myopia treatment efficacy.
Area of Science:
- Ophthalmology
- Pediatric Ophthalmology
- Artificial Intelligence in Medicine
Background:
- Childhood myopia is a growing public health concern.
- Accurate prediction of myopia progression is crucial for effective intervention.
- Machine learning (ML) models offer potential for predicting disease trajectories.
Purpose of the Study:
- To assess the utility of an ML model in predicting natural myopia progression in children.
- To evaluate the inhibitory effects of 0.125% atropine on childhood myopia progression using the ML model as a comparator.
- To determine the myopia suppression rate attributed to 0.125% atropine treatment.
Main Methods:
- Retrospective cohort study involving 397 children treated with 0.125% atropine eye drops.
- Participants were grouped by treatment duration (6, 12, 18, 24, 30 months).
- Comparison of actual spherical equivalent (SE) with ML-predicted SE to calculate myopia suppression rate.
Main Results:
- Treatment with 0.125% atropine resulted in significantly less myopic outcomes than predicted by the ML model, except in the 6-month group.
- The mean myopia suppression rate was 53.5%, indicating effective inhibition of progression.
- The ML model predicted a more advanced myopia progression compared to the actual treatment outcomes.
Conclusions:
- 0.125% atropine treatment demonstrates efficacy in suppressing myopia progression in children.
- ML models can serve as valuable tools for predicting myopia progression and evaluating treatment effectiveness.
- This approach aids in understanding the natural course of myopia and the impact of interventions.
Purpose:
To 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 Design:
Retrospective, cohort study.
Methods:
A 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.
Results:
A 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.
Conclusion:
Treatment 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.
