Rapid Myopic Progression in Childhood Is Associated With Teenage High Myopia

ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study

PURPOSE

To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progression in children.

METHODS

The school-based prospective cohort study enrolled 1,997 individuals aged 7 to 9 years in 11 elementary schools in Mojiang, China. Cycloplegic refraction and biometry were performed at baseline and 4-year visits. The baseline fundus photographs were taken, and TD, defined as the percentage of exposed choroidal vessel area in the photographs, was quantified using an artificial intelligence-assisted semiautomatic labeling approach. After the exclusion of 330 ineligible participants because of loss to follow-up or ineligible fundus photographs, logistic models were used to assess the association of TD with rapid AL elongation (>0.36 mm/year) and SE progression (>1.00 D/year).

RESULTS

The prevalence of tessellation was 477 of 1,667 (28.6%) and mean TD was 0.008 ± 0.019. The mean AL elongation and SE progression in 4 years were 0.90 ± 0.58 mm and -1.09 ± 1.25 D. Higher TD was associated with longer baseline AL (β, 0.030; 95% confidence interval: 0.015-0.046; P < 0.001) and more myopic baseline SE (β, -0.017; 95% confidence interval: -0.032 to -0.002; P = 0.029). Higher TD was associated with rapid AL elongation (odds ratio, 1.128; 95% confidence interval: 1.055-1.207; P < 0.001) and SE progression (odds ratio, 1.123; 95% confidence interval: 1.020-1.237; P = 0.018).

CONCLUSION

Tessellation density is a potential indicator of rapid AL elongation and refractive progression in children. TD measurement could be a routine to monitor AL elongation.

Prevention and management of childhood progressive myopia: National consensus guidelines

Myopia is a major public health problem worldwide, including India, with the global prevalence of myopia increasing rapidly over decades. The clinical and socioeconomic impact of myopia is also expected to rise with rising prevalence. Therefore, the focus has now been shifted to prevent the incidence and progression of myopia. However, there is lack of any standardized guidelines for myopia management. This document aims to generate a national-level expert consensus statement on the management of childhood myopia in the Indian scenario. The expert panel of pediatric ophthalmologists consisted of 63 members who met in a hybrid meeting. A list of topics deliberating discussion in the meeting was provided to the experts in advance and they were instructed to provide their opinions on the matter during the meet. The panel of experts then gave their views on each of the items presented, deliberated on different aspects of childhood myopia, and reached a consensus regarding the practice patterns in the Indian scenario. In case of opposing views or lack of a clear consensus, we undertook further discussion and evaluated literature to help arrive at a consensus. A written document is prepared based on recommendations explaining definition of myopia, refraction techniques, components and methods of workup, initiation of anti-myopia treatment, type and timing of interventions, follow-up schedule, and indications for revised or combination treatment. This article formulates evidence-based guidelines for progressing myopes and pre-myopes and also establishes uniformity in the management of childhood myopia in the country.

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify "at-risk" children for early intervention.

Development and Validation of a Simple Nomogram for Predicting Rapid Myopia Progression in Children with Orthokeratology Management

PURPOSE

To develop and validate an ideal nomogram and an online calculator for predicting rapid myopia progression risk in children managed with orthokeratology (ortho-k).

METHODS

Data of children undergoing ortho-k treatment at Shanghai Children's Hospitals between January 2018 and April 2021 were retrospectively assessed. Potential predictors were screened using univariable analyses and a bidirectional stepwise procedure based on Akaike's information criterion. The final model was constructed using multivariable logistic regression and validated using an internal validation cohort. A nomogram and an online calculator were used to present the final model.

RESULTS

In this retrospective study with 1051 eyes of 560 myopia patients, the training cohort included 735 eyes, and the validation cohort included 316 eyes. Among 11 potential predictors of rapid myopia progression considered, the following four variables identified as independent predictive factors were included in the nomogram: age (odds ratio [OR], 0.69; 95% confidence interval [CI], 0.61-0.79), baseline spherical equivalent (OR, 1.53; 95% CI, 1.31-1.79), pupil diameter (OR, 0.56; 95% CI, 0.32-0.97), and horizontal visible iris diameter (OR, 0.57; 95% CI, 0.33-0.97). The mean concordance statistics for the training and validation cohorts were 0.705 (95% CI 0.664-0.747) and 0.707 (95% CI 0.639-0.774), respectively. The online calculator is publicly available (https://hycalculatoronline.shinyapps.io/dynnomapp/).

CONCLUSION

This study developed a simple-to-use nomogram and online calculator that predicted rapid myopia progression risk in children treated with ortho-k, who will likely benefit from early intervention and improved surveillance.

Association between axial length elongation and spherical equivalent progression in Chinese children and adolescents

BACKGROUND

It is generally believed that a 1-mm axial length (AL) elongation of the eye corresponds to a -3.00 D spherical equivalent (SE) progression, but this is disputed.

PURPOSE

To investigate the association between AL elongation and SE progression among children and adolescents.

METHODS

A prospective cohort study of 710 children and adolescents aged 6-16 years was included. Ophthalmic examinations, including cycloplegic SE, AL and corneal curvature, were performed at baseline and 1-year follow-up. The ratio of SE change (ΔSE) to AL change (ΔAL) (ΔSE/ΔAL) was calculated, and its association with age and refractive status was explored using a general linear model.

RESULTS

Among all participants, 396 (55.77%) were male, with 265 (37.32%) myopes at baseline. The average 1-year ΔSE and ΔAL were 0.61 ± 0.40 D and 0.33 ± 0.22 mm, respectively. Both ΔSE and ΔAL gradually decreased with age (p < 0.001). In the general linear model analyses, age and refractive status were independently associated with ΔSE/ΔAL after adjustment for covariates (age: β ̂ $$ \hat{\beta} $$  = 0.04, p < 0.05; myopia vs nonmyopia: β ̂ $$ \hat{\beta} $$  = 0.28, p < 0.05). Based on the developed formula ΔSE/ΔAL = 1.74 + 0.05*age (for myopes), mean ΔSE/ΔAL in myopes increased from 2.06 D/mm in the 6-year-olds to 2.59 D/mm in the 16-year-olds. In nonmyopes, ΔSE/ΔAL = 1.33 + 0.05*age, and the ratio increased from 1.65 D/mm in the 6-year-olds to 2.18 D/mm in the 16-year-olds.

CONCLUSIONS

The ratio of ΔSE/ΔAL varied with age and refractive status in children and adolescents. The age-specific ΔSE/ΔAL could be used to estimate SE progression through the actual AL change.

Pathologic myopia: advances in imaging and the potential role of artificial intelligence

Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.