Eyelid Spontaneous Blink Analysis and Age-Related Changes Through High-Speed Imaging

Neural control of blinking

Blinking is a motor act characterized by the sequential closing and opening of the eyelids, which is achieved through the reciprocal activation of the orbicularis oculi and levator palpebrae superioris muscles. This stereotyped movement can be triggered reflexively, occur spontaneously, or voluntarily initiated. During each type of blinking, the neural control of the antagonistic interaction between the orbicularis oculi and levator palpebrae superioris muscles is governed by partially overlapping circuits distributed across cortical, subcortical, and brainstem structures. This paper provides a comprehensive overview of the anatomical and physiological foundations underlying the neural control of blinking. We describe the infra-nuclear apparatus, as well as the supra-nuclear control mechanisms, i.e., how cortical, subcortical, and brainstem structures regulate and coordinate the different types of blinking.

Advances in artificial intelligence applications for ocular surface diseases diagnosis

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Validating a Portable Device for Blinking Analyses through Laboratory Neurophysiological Techniques

Blinking analysis contributes to the understanding of physiological mechanisms in healthy subjects as well as the pathophysiological mechanisms of neurological diseases. To date, blinking is assessed by various neurophysiological techniques, including electromyographic (EMG) recordings and optoelectronic motion analysis. We recorded eye-blink kinematics with a new portable device, the EyeStat (Generation 3, blinktbi, Inc., Charleston, SC, USA), and compared the measurements with data obtained using traditional laboratory-based techniques. Sixteen healthy adults underwent voluntary, spontaneous, and reflex blinking recordings using the EyeStat device and the SMART motion analysis system (BTS, Milan, Italy). During the blinking recordings, the EMG activity was recorded from the orbicularis oculi muscles using surface electrodes. The blinking data were analyzed through dedicated software and evaluated with repeated-measure analyses of variance. The Pearson’s product-moment correlation coefficient served to assess possible associations between the EyeStat device, the SMART motion system, and the EMG data. We found that the EMG data collected during the EyeStat and SMART system recordings did not differ. The blinking data recorded with the EyeStat showed a linear relationship with the results obtained with the SMART system (r ranging from 0.85 to 0.57; p ranging from <0.001 to 0.02). These results demonstrate a high accuracy and reliability of a blinking analysis through this portable device, compared with standard techniques. EyeStat may make it easier to record blinking in research activities and in daily clinical practice, thus allowing large-scale studies in healthy subjects and patients with neurological diseases in an outpatient clinic setting.

Dermatochalasis Aggravates Meibomian Gland Dysfunction Related Dry Eyes

This study aimed to investigate the relationships between subjective symptoms, objective signs, and dermatochalasis severity in dry-eye patients and the effects of lid hygiene on dry-eye parameters. We retrospectively enrolled 2328 patients who underwent dry-eye examinations and classified them into four groups by dermatochalasis severity. The SPEED and OSDI questionnaires were used to evaluate subjective symptoms. LipiView® II interferometry was used to measure lipid-layer thickness (LLT) and blink/incomplete blink rates and perform meibography. A slit-lamp–aided standardized evaluator measured meibomian gland expressibility (MGE). A meiboscale was used to grade meibomian gland dropout. Fluorescein tear-film break-up time (FTBUT) and superficial punctate keratitis (SPK) were recorded. The Schirmer test II with anesthetics was used to evaluate aqueous tear secretion. The effects of lid hygiene were evaluated in 644 patients who underwent second comprehensive examinations. The median age of patients was 55.3 [46.0–66.0] years (76.0% female). Patients with more severe dermatochalasis were less symptomatic and had less MGE, higher meiboscale grades and average LLT. Dermatochalasis severity was significantly associated with MGE and meiboscale grade in the upper lid. There were no significant differences in the Schirmer test, FTBUT, and SPK among the severity groups. Females were older and had higher LLT and less severe dermatochalasis. Lid hygiene significantly decreased subjective symptoms, LLT, and Schirmer results, increased FTBUT, but did not change MGE or meiboscale grades. Dermatochalasis severity participated in the pathophysiology of dry eyes. Lid hygiene significantly improved subjective symptoms and reduced LLT, more significantly in patients with less severe dermatochalasis.

Impact of Incomplete Blinking Analyzed Using a Deep Learning Model With the Keratograph 5M in Dry Eye Disease

Purpose

To establish a deep learning model (DLM) for blink analysis, and investigate whether blink video frame sampling rate influences the accuracy of analysis.

Methods

This case-controlled study recruited 50 dry eye disease (DED) participants and 50 normal subjects. Blink videos recorded by a Keratograph 5M, symptom questionnaires, and ocular surface assessments were collected. After processing the blink images as datasets, further training and evaluation of DLM was performed. Blink videos of 30 frames per second (FPS) under white light, eight FPS extracted from white light videos, and eight FPS under infrared light were processed by DLM to generate blink profiles, allowing comparison of blink parameters, and their association with DED symptoms and signs.

Results

The blink parameters based on 30 FPS video presented higher sensitivity and accuracy than those based on eight FPS. The average relative interpalpebral height (IPH), the frequency and proportion of incomplete blinking (IB) were much higher in DED participants than in normal controls (P < 0.001). The IB frequency was closely associated with DED symptoms and signs (|R| ≥ 0.195, P ≤ 0.048), as was IB proportion and the average IPH (R ≥ 0.202, P ≤ 0.042).

Conclusions

DLM is a powerful tool for analyzing blink videos with high accuracy and sensitivity, and a frame rate ≥ 30 FPS is recommended. The IB frequency is indicative of DED.

Translational Relevance

The system of DLM-based blink analysis is of great potential for the assessment of IB and diagnosis of DED.

PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements

PURPOSE

To develop a deep learning semantic segmentation network to automate the assessment of 8 periorbital measurements DESIGN: Development and validation of an artificial intelligence (AI) segmentation algorithm METHODS: A total of 418 photographs of periorbital areas were used to train a deep learning semantic segmentation model to segment iris, aperture, and brow areas. These data were used to develop a post-processing algorithm that measured margin reflex distance (MRD) 1 and 2, medial canthal height (MCH), lateral canthal height (LCH), medial brow height (MBH), lateral brow height (LBH), medial intercanthal distance (MID), and lateral intercanthal distance (LID). The algorithm validity was evaluated on a prospective hold-out test set against 3 graders. The main outcome measures were dice coefficient, mean absolute difference, intraclass correlation coefficient, and Bland-Altman analysis. A smartphone video was also segmented and evaluated as proof of concept.

RESULTS

The AI algorithm performed in close agreement with all human graders, with a mean absolute difference of 0.5 mm for MRD1, MRD2, LCH, and MCH. The mean absolute difference between graders is approximately 1.5-2 mm for LBH and MBH and approximately 2-4 mm for MID and LID. The 95% confidence intervals for all graders overlapped in most cases, demonstrating that the algorithm performs similarly to human graders. The segmentation of a smartphone video demonstrated that MRD1 can be dynamically measured.

CONCLUSIONS

We present, to our knowledge, the first open-sourced, artificial intelligence system capable of automating static and dynamic periorbital measurements. A fully automated tool stands to transform the delivery of clinical care and quantification of surgical outcomes.

Analysis of Eyelid Blink Characteristics in Patients with Ptosis Using a Smartphone Camera

Purpose: To compare eyelid blink characteristics between patients with ptosis and healthy controls using a smartphone camera. Methods: The ptosis group consisted of 20 senile aponeurotic ptosis patients with margin reflex distance1 ≤2.5 mm and the control group consisted of 10 healthy subjects without ptosis. The ptosis group was further divided into two groups based on an age cutoff of 70 years. Palpebral fissure height, levator function, margin reflex distance1, inter-blink interval, blink duration, blink rate, and blink velocity were measured and compared between the three groups based on photographs of the eyelids and videos of blinking taken with a smartphone camera. Results: The palpebral fissure height, levator function, margin reflex distance1, and blink velocity were lower in the ptosis groups than in the control group but these values did not differ between the two ptosis groups. The palpebral fissure height, levator function, and margin reflex distance1 were correlated with blink velocity. In the receiver operating characteristic (ROC) curve of blink velocity, the area under the receiver operating characteristic (AUROC) curve value was as high as 0.969 and the cut-off value was 32.36 mm/s. Conclusions: It is possible to analyze eyelid blink characteristics using a smartphone camera and the results confirmed that palpebral fissure height, levator function, margin reflex distance1, and blink velocity were lower in the senile aponeurotic ptosis group than in the healthy control group and were unaffected by age. Additionally, blink velocity is valuable for diagnosis of ptosis due to the correlation between the degree of ptosis, blink velocity, and the ROC curve of blink velocity.

Orbicularis Oculi Muscle Size and Function: Exploring the Influence of Aging and Exercise Training

The orbicularis oculi muscle is the sphincter muscle of the eyelids that blinks and closes the eyes. In this review, our aim was threefold: (1) to introduce the performance characteristics of blinking activity in young and older adults, (2) to discuss the influence of aging on the orbicularis oculi muscle in healthy adults, and (3) to provide information about the effect of facial exercise training on the orbicularis oculi muscle. To achieve the purpose of this review, a search using two electronic databases (PubMed and Scopus) and a search engine (Google Scholar) was conducted. The amplitude and peak velocity of spontaneously blinking behavior, which is an index of muscle function of the orbicularis oculi, appear to be affected by aging. The muscle thickness of the orbicularis oculi tends to be low in older adults, but there are issues that need to be examined further, such as differences in sex and measurement positions. There was no study on the effect of exercise training; however, the results of a highly trained man indicate that the orbicularis oculi muscles might elicit muscle hypertrophy through non-traditional resistance exercise.

CLEAR - Anatomy and physiology of the anterior eye

A key element of contact lens practice involves clinical evaluation of anterior eye health, including the cornea and limbus, conjunctiva and sclera, eyelids and eyelashes, lacrimal system and tear film. This report reviews the fundamental anatomy and physiology of these structures, including the vascular supply, venous drainage, lymphatic drainage, sensory innervation, physiology and function. This is the foundation for considering the potential interactions with, and effects of, contact lens wear on the anterior eye. This information is not consistently published as academic research and this report provides a synthesis from all available sources. With respect to terminology, the report aims to promote the consistent use of nomenclature in the field, and generally adopts anatomical terms recommended by the Federative Committee for Anatomical Terminology. Techniques for the examination of the ocular surface are also discussed.