The Influence of Accommodation on Retinal Peripheral Refraction Changes in Different Measurement Areas.

Background: The change in refraction caused by accommodation inevitably affects the peripheral defocus state and thus may influence the effect of retinal peripheral myopic defocus measures in myopia control. This study investigated accommodation changes in different peripheral retinas under cycloplegia to help improve myopia control. Methods: Fifty-six eyes of fifty-six myopic subjects were recruited for this prospective study. The center and peripheral retina refractions were measured using multispectral refractive topography. The subjects were divided into low-to-moderate myopia group (range: -1.25 D to -6.00 D) and high myopia group (range: -6.25 D to -9.75 D) according to spherical equivalent (SE). The compound tropicamide (0.5% tropicamide and 0.5% phenylephrine) was used to relax the accommodation. The difference between cycloplegia and non-cycloplegia peripheral retinal refraction was analyzed using the t-test. The correlation between eccentricity and changes in peripheral refraction was analyzed using Pearson's correlation analysis. Results: The manifest refraction of the retina significantly decreased with an increase in eccentricity after cycloplegia. The annular refraction difference value at 50°-53° (ARDV 50-53) showed the largest refraction decrease of 1.31 D compared with the central retinal refraction decrease of 0.84 D. The inferior quadrantal refraction difference value had the least change compared to the other quadrants. The relative peripheral refraction (RPR) changes in refraction difference value (RDV) at 15° (RDV-15), RDV-30, and RDV-45 were less than 0.15 D. When the range of annulus narrowed to 5°, the narrower annulus showed faster change with eccentricity increase in ARDV 30-35, ARDV 35-40, ARDV 40-45, ARDV 45-50, and ARDV 50-53. The RPR was highly correlated with eccentricity (R = 0.938 and P < 0.001). The high myopia group had a greater hyperopic shift in the periphery than the low-to-moderate group after cycloplegia. Conclusions: Peripheral refraction showed a significant hyperopic shift after cycloplegia with an increase in eccentricity. The RPR became more hyperopic than the central refraction. The high myopia group showed more hyperopic shifts in the peripheral region. Accommodation should be taken into consideration in peripheral defocus treatment.

Association between choriocapillaris perfusion and axial elongation in children using defocus incorporated multiple segments (DIMS) spectacle lenses.

PURPOSE: To investigate choroidal and ocular biological variables that influence axial length (AL) elongation in children wearing defocused incorporated multiple segments (DIMS) spectacle lenses. METHODS: This cohort study included 106 myopic children aged 7-14 years with a 1-year follow-up. Participants were divided into two groups according to the increase in AL in one year: rapid (>0.2 mm) and slow (≤0.2 mm) axial elongation groups. Cycloplegic autorefraction and AL were measured at baseline and after 6 and 12 months. The area of choriocapillaris flow voids (FVs) and choroidal thickness (ChT) at baseline were measured. RESULTS: Univariate linear regression analysis showed that AL elongation were significantly associated with the FVs area (standardised ß = 0.198, P < 0.05) and age (standardised ß = -0.201, P < 0.05). Multiple linear regression showed that the FVs area, age, and average K reading were associated with AL elongation. Multiple logistic regression analyses showed that greater degrees of myopia and larger FVs areas were risk factors for rapid axial elongation, while older age, large pupil diameter and steeper cornea were protective factors. In estimating axial elongation, the FVs area alone demonstrated an area under the curve (AUC) of 0.672 (95% CI, 0.569-0.775, P < 0.01), and that of FVs area and other ocular variables was 0.788 (95% CI, 0.697-0.878, P < 0.001). CONCLUSION: Larger choriocapillaris FVs area at baseline may help to predict axial elongation in myopic eyes. The association between FVs area and axial elongation should be taken into consideration in further myopic cohort studies.

Evaluating the optimised font size and viewing time of online learning in young children: a multicentre cross-sectional study.

OBJECTIVES: Near viewing distance (VD) and longer viewing times are associated with myopia. This study aimed to identify the font size and viewing time that guarantee the appropriate VD and pixels per degree (PPD) for children's online learning. DESIGN: This cross-sectional study comprised two experiments. In experiment A, participants read text in five font sizes on three backlit displays (a personal computer, a smartphone and a tablet), an E-ink display and paper for 5 min per font size. In experiment B, participants watched videos for 30 min on three backlit displays. SETTING: The Peking University People's Hospital in Beijing (China) and the School of Ophthalmology and Optometry, Wenzhou Medical University (Zhejiang Province, China). PARTICIPANTS: Thirty-five participants completed experiment A. Ten of them participated in experiment B. PRIMARY AND SECONDARY OUTCOME MEASURES: VDs were measured by Clouclip. The corresponding PPD was calculated. RESULTS: In experiment A, font size and display type significantly affected VD (F(4840)=149.44, p<0.001, ES (Effect size)=0.77; F(4840), p<0.001, ES=0.37). VDs were >33 cm for all five font sizes on the PC, the tablet and paper and for 18-pt on the smartphone and 16-pt on E-ink. PPD for 16-pt on the PC, 14-pt on the tablet and all five font sizes on the phone were >60. In experiment B, VD increased over the four previous 5 min periods but decreased slightly on tablets and PCs in the fifth 5 min period. PPD was >60. CONCLUSION: Children demonstrated different VDs and PPDs based on font size and display type. To ensure a 33 cm VD and 60 PPD, the minimum font size for online reading should be 18-pt on smartphones, 16-pt on PCs and E-ink, 10.5-pt on tablets and 9-pt on paper. More attention should be given to children's VD with continuous video viewing of more than 25 min. TRIAL REGISTRATION NUMBER: ChiCTR2100049584.

An investigation into the causes of abnormal waste of Ortho-K lenses.

Purpose: To investigate the reasons for wasting orthokeratology (OK) lenses due to breakage or loss, provide more comprehensive guidelines for the clinical care of lenses and minimize time and costs for patients due to excessive broken and lost lenses. Methods: A survey was administered to clinic outpatients who had broken or lost their OK lenses before the regularly scheduled replacement cycle (1-1.5 years). The association between the frequency of OK lens breakage and daily care was assessed using Fisher's exact test and multivariable ordered logistic regression analysis. Results: A total of 306 valid questionnaires were collected. Among the subjects, 141 were male, and 165 were female, with a mean age of 10.57 ± 2.00 years (range: 6-18 years). In the investigation of the causes of OK lens waste, 81.4% of the patients reported lens breakage, 13.1% lost their lenses, and 5.6% of patients experienced both fragmentation and lens loss. More than half of the patients (52.90%) used incorrect lens cleaning techniques. In further analysis of the relationship between the frequency of OK lens fragmentation within a year and daily care habits, a significant difference was observed between the caregiver (P = 0.03) and whether the lenses were cleaned promptly after removal (P < 0.001). Mothers as daily caregivers of OK lenses had a lower frequency of fragmentation in a year compared to nanny or grandparents (P = 0.014, OR = 0.33, 95% CI = 0.13, 0.80). The failure to clean the lenses according to eye care practitioners' guidance was a risk factor for the frequent breakage of OK lenses (P < 0.001. OR = 5.29, 95% CI = 3.15, 8.89). Conclusions: The causes of OK lens waste were mainly attributed to caregivers, care practices and some unexpected situations that can be avoided through optometrists' reminders. Regardless of the reasons for noncompliant behavior leading to breakage or loss of OK lenses, all of the complications can probably be addressed by better and more frequent reinforcement of care procedures by practitioners. Better clinical guidance measures and more frequent reminders could prevent a large proportion of abnormal waste of OK lenses.

Machine learning algorithm improves accuracy of ortho-K lens fitting in vision shaping treatment.

PURPOSE: To construct a machine learning (ML)-based model for estimating the alignment curve (AC) curvature in orthokeratology lens fitting for vision shaping treatment (VST), which can minimize the number of lens trials, improving efficiency while maintaining accuracy, with regards to its improvement over a previous calculation method. METHODS: Data were retrospectively collected from the clinical case files of 1271 myopic subjects (1271 right eyes). The AC curvatures calculated with a previously published algorithm were used as the target data sets. Four kinds of machine learning algorithms were implemented in the experimental analyses to predict the targeted AC curvatures: robust linear regression models, support vector machine (SVM) regression models with linear kernel functions, bagging decision trees, and Gaussian processes. The previously published calculation method and the novel machine learning method were then compared to assess the final parameters of ordered lenses. RESULTS: The linear SVM and Gaussian process machine learning models achieved the best performance. The input variables included sex, age, horizontal visible iris diameter (HVID), spherical refraction (SER), cylindrical refraction, eccentricity value (e value), flat K (K1) and steep K (K2) readings, anterior chamber depth (ACD), and axial length (AL). The R-squared values for the output AC1K1, AC1K2 and AC2K1 values were 0.91, 0.84, and 0.73, respectively. The previous calculation method and machine learning methods displayed excellent consistency, and the proposed methods performed best based on flat K reading and e values. CONCLUSIONS: The ML model can provide practitioners with an efficient method for estimating the AC curvatures of VST lenses and reducing the probability of cross-infection originating from trial lenses, which is especially useful during pandemics, such as that for COVID-19.

Machine learning based strategy surpasses the traditional method for selecting the first trial Lens parameters for corneal refractive therapy in Chinese adolescents with myopia.

PURPOSE: Return zone depth (RZD) and landing zone angle (LZA) are important parameters of corneal refractive therapy (CRT) lenses. A new machine learning algorithm is proposed for prescribing CRT lens parameters in Chinese adolescents with myopia. METHODS: This is a retrospective study. In total, 1037 Chinese adolescents with myopia (1037 right eyes) were enrolled. A calculation model based on corneal elevation maps was constructed to calculate RZD and LZA for the four quadrants. Furthermore, multiple linear regression and optimized machine learning models were established to predict RZD and LZA values for different combinations of age, sex, and ocular parameters. The four methods (sliding card, linear regression, calculation and optimized machine learning) were then compared to the parameters of the final ordered lens. RESULTS: The optimized machine learning pipeline achieved the best performance. Age, sex, horizontal visible iris diameter (HVID), spherical equivalent refraction degree (SER), eccentricity (e), keratometric (K) readings, corneal astigmatism (CA), axial length (AL), AL/corneal curvature ratio (AL/MK), and anterior chamber depth (ACD) were significant to the machine learning model. The R values for the nasal, temporal, superior and inferior LZA based on machine learning were 0.843, 0.693, 0.866 and 0.762, respectively, and those for the RZD were 0.970, 0.964, 0.975 and 0.964, respectively. CONCLUSIONS: The feasibility and efficiency of an optimized machine learning method to predict LZA and RZD parameters has been demonstrated. The advantage of the proposed method is that it is more accurate, easier to use and faster to implement than the traditional sliding card method.

A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children.

BACKGROUND: Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation of AL, have been conducted. The purpose of our study was to construct a machine learning (ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children. METHODS: In total, 1011 myopic children aged 6 to 18 years participated in this study. Cross-sectional datasets were used to optimize the ML algorithms. The input variables included age, sex, central corneal thickness (CCT), spherical equivalent refractive error (SER), mean K reading (K-mean), and white-to-white corneal diameter (WTW). The output variable was AL. A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups, including 4 groups used as training data and one group used as validation data. Six types of ML algorithms were implemented in our models. The best-performing algorithm was applied to predict AL, and estimates of the physiological elongation of AL were obtained as the partial derivatives of AL predicted -age curves based on an unchanged SER value with increasing age. RESULTS: Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a R 2 value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the AL predicted -age curves, the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age, SER and K-mean. According to the model, the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean. CONCLUSIONS: The physiological elongation of the AL is rarely recorded in clinical data in China. In cases of unavailable clinical data, an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL, which is especially useful when monitoring myopia progression in orthokeratology lens wearers.