Translational Vision Science & Technology最新文献

Purpose: Eye rubbing is considered to play a significant role in the progression of keratoconus and of corneal ectasia following refractive surgery. To our knowledge, no tool performs an objective quantitative evaluation of eye rubbing using a device that is familiar to typical patients. We introduce here an innovative solution for objectively quantifying and preventing eye rubbing. It consists of an application that uses a deep-learning artificial intelligence (AI) algorithm deployed on a smartwatch.

Methods: A Samsung Galaxy Watch 4 smartwatch collected motion data from eye rubbing and everyday activities, including readings from the gyroscope, accelerometer, and linear acceleration sensors. The training of the model was carried out using two deep-learning algorithms, long short-term memory (LSTM) and gated recurrent unit (GRU), as well as four machine learning algorithms: random forest, K-nearest neighbors (KNN), support vector machine (SVM), and XGBoost.

Results: The model achieved an accuracy of 94%. The developed application could recognize, count, and display the number of eye rubbings carried out. The GRU model and XGBoost algorithm also showed promising performance.

Conclusions: Automated detection of eye rubbing by deep-learning AI has been proven to be feasible. This approach could radically improve the management of patients with keratoconus and those undergoing refractive surgery. It could detect and quantify eye rubbing and help to reduce it by sending alerts directly to the patient.

Translational relevance: This proof of concept could confirm one of the most prominent paradigms in keratoconus management, the role of abnormal eye rubbing, while providing the means to challenge or even negate it by offering the first automated and objective tool for detecting eye rubbing.

Purpose: The purpose of this study was to measure biomechanical strains in the lamina cribrosa (LC) of living human eyes undergoing intraocular pressure (IOP) increase.

Methods: Healthy control subjects and patients with glaucoma underwent optical coherence tomographic (OCT) imaging of the LC before and after wearing of swim goggles that increased IOP (57 image pairs, 39 persons). Digital volume correlation was used to measure biomechanical strains in optic nerve head tissue and change in depth of the anterior border of the LC.

Results: The mean IOP increase in both glaucoma and control eyes was 7.1 millimeters of mercury (mm Hg) after application of the goggles. Among glaucoma eyes, strains that were significant were: contractile Ezz (average = -0.33%, P = 0.0005), contractile Eθθ (average = -0.23%, P = 0.03), Emax (average = 0.83%, P < 0.0001), and Γmax (average = 0.95%, P < 0.0001), whereas the average anterior LC depth (ALD) decreased by 2.39 µm (anterior; P = 0.0002). In glaucoma eyes, shear strain Ezθ was greater with worse mean deviation (MD) and visual function index (P = 0.044 and P = 0.006, respectively, multivariate models). Strain compliance for Erθ, Ezθ, and Eθθ all increased with greater MD worsening prior to imaging (P = 0.04, P = 0.007, and P = 0.03).

Conclusions: LC strains were measurable 20 minutes after IOP increase, producing axial compression and greater peripheral strain than centrally. Some strain compliances were greater with worse existing visual field loss or with more progressive past field loss.

Translational relevance: Biomechanical strains are related to measures of glaucoma damage, supporting the hypothesis that optic nerve head biomechanical responses represent a noninvasive biomarker for glaucoma.

Purpose: We evaluated through-focus visual performance and accommodative response in young subjects through three segmented multifocal designs for myopia control, mapped on the spatial light modulator of a monocular adaptive optics visual simulator (AOVS), and compared with single vision (SV).

Methods: The segmented multifocal patterns included a 4 mm diameter center distance zone and offset peripheral defocus (MP1), astigmatism and coma (MP2), or a combination (MP3). High-contrast logMAR visual acuity (VA) was measured with monochromatic stimuli (555 nm). Ocular aberrations were measured using the Hartmann-Shack aberrometry channel. Measurements were taken for distance viewing and five accommodative demands (AD, up to 4.5 D). Accommodative lag was calculated from the dioptric shift of the maximum retinal image quality metric from the corresponding wave aberrations.

Results: Best-corrected logMAR VA was -0.11 ± 0.02 (SV) and slightly reduced by multifocal patterns (-0.08 ± 0.03 [MP1], -0.07 ± 0.04 [MP2], -0.05 ± 0.04 [MP3]). Accommodative lag with SV was lower in emmetropes than myopes (by 0.43D for the largest demand). MP1 significantly decreased accommodative lag in myopes (P = 0.03), unlike MP2 or MP3. Multifocal patterns reduced pupil diameter in myopes at all distances. MP1 improved accommodative response in myopes without compromising distance vision.

Conclusions: AOVS helped to understand the interplay of physiological and lens design factors, potentially guiding custom corrections. A center distance with off-centered positive power in the lens periphery could feature suitable properties (peripheral focus and accommodative focus control) for myopia control.

Translational relevance: We demonstrate a two-zone contact lens design that provides excellent visual quality and accommodative response, important properties for myopia control lenses.

Purpose: This study aims to perform a clinical investigation of an innovative rebound technology-based device, the M-TONX, to simultaneously measure intraocular pressure (IOP) and central corneal thickness (CCT).

Methods: The IOP and CCT of the patients were first measured by the M-TONX. Then, the measurements were repeated by the Goldman applanation (GAT) and the Pentacam corneal topographer, as the standard devices. For the statistical analysis, the patients were stratified based on their IOPs to group 1 (IOP < = 16 millimeters of mercury [mm Hg]), group 2 (16 mm Hg < IOP < 23 mm Hg), and group 3 (IOP > = 23 mm Hg). The stratification was also performed for the CCTs as: class 1 (CCT < = 475 µm), class 2 (475 < CCT < 574 µm), and class 3 (CCT > = 575 µm).

Results: Of the 374 eyes (225 subjects and 43% women), 262, 66, and 46 eyes belonged to group 1, group 2, and group 3, respectively. Very high IOP (>35 mm Hg) was observed in 12 eyes. The overall confidence interval of the deviation (confidence level = 95%) from the standard devices was estimated to be 0.7 to 1.2 mm Hg for IOP, and -12.7 to -5.4 µm for CCT. The stratified analysis showed substantial agreement with the standard devices with the intraclass correlation > 0.65 and the Pearson Correlation > 0.8 calculated for all the groups and classes.

Conclusions: The M-TONX exhibited a reliable performance concerning the standards for measuring IOP and CCT. Its accuracy remains stable for a broad range of IOP and CCT. The M-TONX successfully incorporates two separate functionalities into a single compact user-friendly device.

Translational relevance: This study uncovers the conformity of the technology with the standards, linking fundamental research to clinical care.

Purpose: To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model.

Methods: Flat-mounted retinal images from mice killed at postnatal days 12 (P12), P17, and P25 used in prior OIR studies were compiled. Mice subjected to normoxic conditions were killed at P12, P17, and P25, and their retinas were flat-mounted for imaging. Major blood vessels from the OIR images were manually segmented by four graders (JSC, HKR, KBL, JM), with cross-validation performed to ensure similar grading.

Results: Overall, 1170 images were included in this dataset. Of these images, 111 were of normoxic mice retina, and 1048 were mice subject to OIR. The majority of images from OIR mice were obtained at P17. The 50 images obtained from an external dataset, OIRSeg, did not have age labels. All images were manually segmented and used in the training or testing of a previously published deep learning algorithm.

Conclusions: This is the first open-source dataset of original and segmented flat-mounted retinal images. The dataset has potential applications for expanding the development of generalizable and larger-scale artificial intelligence and analyses for OIR. This dataset is published online and publicly available at dx.doi.org/10.6084/m9.figshare.23690973.

Translational relevance: This open access dataset serves as a source of raw data for future research involving big data and artificial intelligence research concerning oxygen-induced retinopathy.

Purpose: The purpose of this study was to explore the effects of early failure criteria for participants in randomized clinical trials (RCTs) on overall trial conclusions.

Method: We simulated 10,000 hypothetical RCTs with 2 treatments, 1 linear improvement and 1 with increasing rate of improvement and 6 follow-up visits. Each RCT had 400 participants, with the same baseline stereoacuity distribution. We incorporated random test-retest noise for every visit, and scores were rounded to the nearest observable score. Early failure was defined as worsening of two or more levels. We compared mean outcome stereoacuity between treatment groups, with and without the failure rule, using the two-sample t-test and the proportion of erroneous RCTs (significantly different mean outcome values, where truth is known to be no different). Sensitivity analyses were performed to explore the influence of sample size, baseline distribution of stereoacuity, overall magnitude of mean improvement, magnitude of change for the failure rule, and distribution of noise.

Results: A greater proportion of 10,000 simulated RCTs had an erroneous mean difference in outcome with the early failure rule than without (5.49%, 95% confidence interval [CI] = 5.05% to 5.94% vs. 0, 0%, 95% CI = 0% to 0.000001%, difference 5.49%, P < 0.0001). Sensitivity analysis revealed that increased sample size and wider distribution of noise had the greatest influence on increasing proportions of erroneous RCT conclusions.

Conclusions: Study designs incorporating participant-level early failure rules increase the risk of erroneous RCT conclusions and should be avoided.

Translational relevance: We provide data informing the design of future clinical trials. Earlier failure rules at the participant level should be avoided.

Purpose: Inherited retinal disorders (IRD) are a complex group of conditions. By developing the first patient-reported experience measurement (PREM) questionnaire tailored for individuals with IRD participating in natural history studies, we gathered information on individuals' views of their experience while they are involved in research.

Methods: Adults with IRD who (i) were enrolled in a natural history study taking place at Moorfields Eye Hospital (London, UK), (ii) had attended at least two study visits, (iii) the most recent one being less than two weeks before the questionnaire, and (iv) who were not involved in interventional research, were considered for participation.

Results: Fifty individuals completed the PREM questionnaire at a mean age of 31.1 ± 11 years old and were diagnosed at a mean age of 14 ± 9.7 years old. Most individuals rated "getting closer to receiving treatment' as their main motivation to enroll in the study, and their biggest influence was their own curiosity. Individuals were more satisfied with the care they received, and least satisfied with the efficiency of the visit. After validity and reliability assessments, the final PREM was created, with 27 questions and five sections, and Cronbach alpha coefficient between 0.316 and 0.756 in each section.

Conclusions: The PREM instrument allowed us to assess the overall satisfaction of individuals with IRD involved in research, detect possible barriers to research participation, and ways of improving our care.

Translational relevance: The final version can be included in future research and other sites worldwide, to maintain high quality standards.

Purpose: The purpose of this study is to introduce a nonparametric hierarchical Bayesian model (HBM) that enables advanced statistical inference on contrast sensitivity (CS) both at individual spatial frequencies (SFs) and across multiple SFs in clinical trials, where CS measurements are crucial for assessing safety and efficacy.

Methods: The HBM computes the joint posterior distribution of CS at six Food and Drug Administration-designated SFs across the population, individual, and test levels. It incorporates covariances at both population and individual levels to capture the relationship between CSs across SFs. A Bayesian inference procedure (BIP) is also used to estimate the posterior distribution of CS at each SF independently. Both methods are applied to a quantitative CSF (qCSF) dataset of 112 subjects and compared in terms of precision, test-retest reliability of CS estimates, sensitivity, accuracy, and statistical power in detecting CS changes.

Results: The HBM reveals correlations between CSs in pairs of SFs and provides significantly more precise estimates and higher test-retest reliability compared to the BIP. Additionally, it improves the average sensitivity and accuracy in detecting CS changes for individual subjects, as well as statistical power for detecting group-level CS changes at individual and combinations of multiple SFs between luminance conditions.

Conclusions: The HBM establishes a comprehensive framework to enhance sensitivity, accuracy, and statistical power for detecting CS changes in hierarchical experimental designs.

Translational relevance: The HBM presents a valuable tool for advancing CS assessments in the clinic and clinical trials, potentially improving the evaluation of treatment efficacy and patient outcomes.