The Effects of Spectacles or Orthokeratology on the Tear Film in Children and Adolescents

A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control

Purpose

Modalities for myopia control, such as orthokeratology, repeated low-intensity red light (RLRL) treatment, and low-concentration atropine, have become popular topics. However, the effects of these three modalities on ocular surface health remain unclear. The tear meniscus height (TMH), a crucial criterion for evaluating ocular surface health and diagnosing dry eye, is conventionally measured via manual demarcation of ocular surface images, which is inefficient and involves subjective judgment. Therefore, this study sought to establish a deep learning model for automatic TMH measurement on ocular surface images to improve the efficiency and accuracy of the initial screening of dry eye associated with myopia control modalities.

Methods

To establish a model, 1,200 ocular surface images captured with an OCULUS Keratograph 5M were collected. The tear meniscus area on the image was initially marked by one experienced ophthalmologist and verified by the other. The whole image dataset was divided into a training set (70%), a validation set (20%), a test set (10%), and an external validation set (100 ocular surface images) for model construction. The deep learning model was applied to ocular surface imaging data from previous clinical trials using orthokeratology, RLRL therapy, and 0.01% atropine for myopia control. TMHs at follow-ups were automatically measured by the deep learning model.

Results

Two hundred training iterations were performed to establish the model. At the 124th iteration, the IoU of the validation set peaked at 0.913, and the parameters of the model were saved for the testing process. The model IoU was 0.928 during testing. The AUC of the ROC curve was 0.935, and the R2 of the linear regression analysis was 0.92. The good performance and comprehensive validation of the model warrants its application to automatic TMH measurement in clinical trials of myopia control. There were no significant changes in the TMH during the follow-up period after treatment with orthokeratology, RLRL, or 0.01% atropine.

Conclusion

A deep learning model was established for automatic measurement of the TMH on Keratograph 5M-captured ocular surface images. This model demonstrated high accuracy, great consistency with manual measurements, and applicability to the initial screening of dry eye associated with myopia control modalities.

Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology

Purpose

Using deep learning model to observe the blinking characteristics and evaluate the changes and their correlation with tear film characteristics in children with long-term use of orthokeratology (ortho-K).

Methods

31 children (58 eyes) who had used ortho-K for more than 1 year and 31 age and gender-matched controls were selected for follow-up in our ophthalmology clinic from 2021/09 to 2023/10 in this retrospective case-control study. Both groups underwent comprehensive ophthalmological examinations, including Ocular Surface Disease Index (OSDI) scoring, Keratograph 5M, and LipiView. A deep learning system based on U-Net and Swim-Transformer was proposed for the observation of blinking characteristics. The frequency of incomplete blinks (IB), complete blinks (CB) and incomplete blinking rate (IBR) within 20 s, as well as the duration of the closing, closed, and opening phases in the blink wave were calculated by our deep learning system. Relative IPH% was proposed and defined as the ratio of the mean of IPH% within 20 s to the maximum value of IPH% to indicate the extent of incomplete blinking. Furthermore, the accuracy, precision, sensitivity, specificity, F1 score of the overall U-Net-Swin-Transformer model, and its consistency with built-in algorithm were evaluated as well. Independent t-test and Mann-Whitney test was used to analyze the blinking patterns and tear film characteristics between the long-term ortho-K wearer group and the control group. Spearman's rank correlation was used to analyze the relationship between blinking patterns and tear film stability.

Results

Our deep learning system demonstrated high performance (accuracy = 98.13%, precision = 96.46%, sensitivity = 98.10%, specificity = 98.10%, F1 score = 0.9727) in the observation of blinking patterns. The OSDI scores, conjunctival redness, lipid layer thickness (LLT), and tear meniscus height did not change significantly between two groups. Notably, the ortho-K group exhibited shorter first (11.75 ± 7.42 s vs. 14.87 ± 7.93 s, p = 0.030) and average non-invasive tear break-up times (NIBUT) (13.67 ± 7.0 s vs. 16.60 ± 7.24 s, p = 0.029) compared to the control group. They demonstrated a higher IB (4.26 ± 2.98 vs. 2.36 ± 2.55, p < 0.001), IBR (0.81 ± 0.28 vs. 0.46 ± 0.39, p < 0.001), relative IPH% (0.3229 ± 0.1539 vs. 0.2233 ± 0.1960, p = 0.004) and prolonged eye-closing phase (0.18 ± 0.08 s vs. 0.15 ± 0.07 s, p = 0.032) and opening phase (0.35 ± 0.12 s vs. 0.28 ± 0.14 s, p = 0.015) compared to controls. In addition, Spearman's correlation analysis revealed a negative correlation between incomplete blinks and NIBUT (for first-NIBUT, r = -0.292, p = 0.004; for avg-NIBUT, r = -0.3512, p < 0.001) in children with long-term use of ortho-K.

Conclusion

The deep learning system based on U-net and Swim-Transformer achieved optimal performance in the observation of blinking characteristics. Children with long-term use of ortho-K presented an increase in the frequency and rate of incomplete blinks and prolonged eye closing phase and opening phase. The increased frequency of incomplete blinks was associated with decreased tear film stability, indicating the importance of monitoring children's blinking patterns as well as tear film status in clinical follow-up.

Face Mask and Tear Film Stability: A Pilot Study of the Objective Measurement of Tear Break-Up Time

(1) Background: Mask-associated dry eye (MADE) has been associated with increased dry eye symptoms, apparently due to reduced tear break-up time (TBUT). This study aimed to determine the short-term impact of surgical face mask (FM) on tear film stability by measuring non-invasive tear break-up time (NIBUT). (2) Methods: Twenty-six healthy participants had NIBUT evaluated without FM, with surgical FM and with a surgical FM secured to the skin with adhesive tape (TFM). NIBUT-first was measured with Keratograph 5M (K5M, Oculus, Wetzlar, Germany). Each participant had NIBUT measured in four sessions on four consecutive days. Session 1: without FM vs. with FM. Session 2: with FM vs. without FM. Session 3: without FM vs. with TFM. Session 4: with TFM vs. without FM (3). The time between each measured setting was 2 min. Results: The mean ± SD NIBUT without FM was 8.9 ± 3.7, with FM 10.2 ± 4.1, and with TFM 8.4 ± 3.8 s. No significant differences were observed in NIBUT in any of the evaluated settings: without FM vs. with FM (p = 0.247), without FM vs. with TFM (p = 0.915), and with FM vs. with TFM (p = 0.11). (4) Conclusions: This study did not find a significant short-term effect of FM on NIBUT. Other variables or longer periods of exposure might trigger the symptoms and ocular surface alterations in MADE.