Global trends and frontiers of research on pathologic myopia since the millennium: A bibliometric analysis

Effect and safety posterior scleral reinforcement on controlling myopia in children: a meta-analysis

PURPOSE

The aim of this meta-analysis was to assess the efficacy of posterior scleral reinforcement (PSR) on the control of pediatric myopia. Electronic databases were systematically searched.

METHODS

Standardized mean differences (SMDs) of outcomes were calculated. Eight studies with 357 patients with pediatric myopia were included. The SMD for the increase in mean axial length (AL) in the PSR and control group was - 1.19 (95% CI - 1.71, - 0.68).

RESULTS

The SMD for decrease of best-corrected visual acuity (BCVA) LogMAR in the PSR and control group was 0.85 (95% CI 0.28, 1.43). The SMD for change in intraocular pressure (IOP) at the time of surgery and at the end of the follow-up period in the PSR group was - 0.01 (95% CI - 0.48, 0.47).

CONCLUSION

This meta-analysis indicates that PSR surgery may be an effective therapeutic strategy to control the progression of myopia in childhood with acceptable adverse effects.

Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model's performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.