Anatomic Peculiarities Associated with Axial Elongation of the Myopic Eye

Ocular image-based deep learning for predicting refractive error: A systematic review

Background

Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors.

Main text

We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n ​= ​5), OCT-based (n ​= ​1), and external ocular photo-based (n ​= ​3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models.

Conclusions

The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Understanding Posterior Staphyloma in Pathologic Myopia: Current Overview, New Input, and Perspectives

Posterior staphyloma (PS) is considered the hallmark of pathologic myopia and is defined as an outpouching of a circumscribed portion of the eyeball with a radius of curvature smaller than that of the adjacent zone. Although more common in eyes with high myopia, it can affect those without it. The presence of PS is associated with a structurally and functionally worse course of high myopia that can lead to visual disability. Unfortunately, the pathogenesis of PS is unclear so far. Thus, due to the increasing prevalence of myopia which has been further exacerbated by the advent of COVID-19 lockdown, researchers are eager to elucidate the pathogenesis of pathologic myopia and that of its complications, especially PS, which will allow the development of preventive strategies. The aim of this work was to review the morphological characteristics of PS with emphasis on similarities with peripapillary staphyloma and to discuss the pathogenesis of PS considering recent suggestions about that of peripapillary staphyloma.