Measurement method of tear meniscus height based on deep learning

Next-Generation Tear Meniscus Height Detecting and Measuring Smartphone-Based Deep Learning Algorithm Leads in Dry Eye Management

Purpose

This study aims to develop and assess an infrastructure using Python-based deep learning code for future diagnostic and management purposes related to dry eye disease (DED) utilizing smartphone images.

Design

Cross-sectional study using data which was gathered in Vision Health Research Clinic.

Participants

One thousand twenty-one eye images from 734 patients were included in this article that categorizes into 70% females and 30% males, with no sex and age limit.

Methods

One specialist captured eye images using Samsung A71 (601 images) and iPhone 11 (420 images) cell phones with the flashlight on and direct gaze to the camera. These images include the area of only 1 eye (left/right).

Main Outcome Measures

First, our specialist did 3 different segmentations for every eye image separately for 80% of the training data. This part contains eye, lower eyelid, and iris segmentation. In 20% of test data after automated cropping of the lower eyelid margin and upscaling by 8×, the appropriate tear meniscus height segmentation will be chosen and measured using a deep learning algorithm.

Results

The model was trained on 80% of the data and 20% of the data used for validation from both phones with different resolutions. The dice coefficient of the trained model for validation data is 98.68%, and the accuracy of the overall model is 95.39%.

Conclusions

It appears that this algorithm holds the potential to herald an evolution in the future of diagnosis and management of DED by homecare devices solely through smartphones.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Feasibility of Tear Meniscus Height Measurements Obtained with a Smartphone-Attachable Portable Device and Agreement of the Results with Standard Slit Lamp Examination

PURPOSE

We aimed to evaluate the feasibility of using a novel device, the Smart Eye Camera (SEC), for assessing tear meniscus height (TMH) after fluorescein staining and the agreement of the results with measurements obtained using standard slit lamp examination.

METHODS

TMH was assessed using both SEC and conventional slit lamp examination. The images were analyzed using the software ImageJ 1.53t (National Institutes of Health, Bethesda, MD, USA). A common measurement unit scale was established based on a paper strip, which was used as a calibration marker to convert pixels into metric scale. A color threshold was applied using uniform parameters for brightness, saturation, and hue. The images were then binarized to black and white to enhance the representation of the tear menisci. A 2 mm area around the upper and lower meniscus in the central eye lid zone was selected and magnified 3200 times to facilitate manual measurement. The values obtained using SEC were compared with those obtained with a slit lamp.

RESULTS

The upper and lower TMH values measured using the SEC were not statistically different from those obtained with a slit lamp (0.209 ± 0.073 mm vs. 0.235 ± 0.085, p = 0.073, and 0.297 ± 0.168 vs. 0.260 ± 0.173, p = 0.275, respectively). The results of Bland-Altman analysis demonstrated strong agreement between the two instruments, with a mean bias of -0.016 mm (agreement limits: -0.117 to 0.145 mm) for upper TMH and 0.031 mm (agreement limits: -0.306 to 0.368 mm) for lower TMH.

CONCLUSIONS

The SEC demonstrated sufficient validity and reliability for assessing TMH in healthy eyes in a clinical setting, demonstrating concordance with the conventional slit lamp examination.

2022 Glenn A. Fry Award lecture: Enhancing clinical assessment for improved ophthalmic management

ABSTRACT

Detailed clinical assessment is critical to allow sensitive evaluation of the eye and its management. As technology advances, these assessment techniques can be adapted and refined to improve the detection of pathological changes of ocular tissue and their impact on visual function. Enhancements in optical medical devices including spectacle, contact, and intraocular lenses have allowed for a better understanding of the mechanism and amelioration of presbyopia and myopia control. Advancements in imaging technology have enabled improved quantification of the tear film and ocular surface, informing diagnosis and treatment strategies. Miniaturized electronics, large processing power, and in-built sensors in smartphones and tablets capacitate more portable assessment tools for clinicians, facilitate self-monitoring and treatment compliance, and aid communication with patients. This article gives an overview of how technology has been used in many areas of eye care to improve assessments and treatment and provides a snapshot of some of my studies validating and using technology to inform better evidence-based patient management.

Dynamic tear meniscus parameters in complete blinking: insights into dry eye assessment

AIM

To investigate the relationship between dynamic tear meniscus parameters and dry eye using an automated tear meniscus segmentation method.

METHODS

The analysis of tear meniscus videos captured within 5s after a complete blink includes data from 38 participates. By processing video data, several key parameters including the average height of the tear meniscus at different lengths, the curvature of the tear meniscus's upper boundary, and the total area of the tear meniscus in each frame were calculated. The effective values of these dynamic parameters were then linearly fitted to explore the relationship between their changing trends and dry eye disease.

RESULTS

In 94.74% of the samples, the average height of central tear meniscus increased over time. Moreover, 97.37% of the samples exhibited an increase in the overall tear meniscus height (TMH) and area from the nasal to temporal side. Notably, the central TMH increased at a faster rate compared to the nasal side with the temporal side showing the slowest ascent. Statistical analysis indicates that the upper boundary curvature of the whole tear meniscus as well as the tear meniscus of the nasal side (2, 3, and 4 mm) aid in identifying the presence of dry eye and assessing its severity.

CONCLUSION

This study contributes to the understanding of tear meniscus dynamics as potential markers for dry eye, utilizing an automated and non-invasive approach that has implications for clinical assessment.

A Fully Automatic Estimation of Tear Meniscus Height Using Artificial Intelligence

Purpose

Accurate quantification measurement of tear meniscus is vital for the precise diagnosis of dry eye. In current clinical practice, the measurement of tear meniscus height (TMH) relies on doctors' manual operation. This study aims to propose a novel automatic artificial intelligence (AI) system to evaluate TMH.

Methods

A total of 510 photographs obtained by the oculus camera were labeled. Three thousand and five hundred images were finally attained by data enhancement to train the neural network model parameters, and 60 were used to evaluate the model performance in segmenting the cornea and tear meniscus region. One hundred images were used to test generalization ability of the model. We modified a segmentation model of the cornea and the tear meniscus based on the UNet-like network. The output of the segmentation model is followed by a calculation module that calculates and reports the TMH.

Results

Compared with ground truth (GT) manually labeled by clinicians, our modified model achieved a Dice Similarity Coefficient (DSC) and Intersection over union (Iou) of 0.99/0.98 in the corneal segmentation task and 0.92/0.86 for the detection of tear meniscus on the validation set, respectively. On the test set, the TMH automatically measured by our AI system strongly correlates with the results manually calculated by the ophthalmologists.

Conclusions

We developed a fully automated and reliable AI system to obtain TMH. After large-scale clinical testing, our method could be used for dry eye screening in clinical practice.