Search Results (178)

Presbyopia is a ubiquitous age-related condition characterized by reduced near focusing ability due to lenticular stiffening. Pharmacologic agents such as pilocarpine have re-emerged as a less-invasive treatment option by inducing miosis and thereby enhancing depth of focus. However, the optimal pharmacologically induced pupil size that balances improved near vision with sufficient retinal illuminance remains undetermined. In this work, we present for the first time a direct integration of advanced theoretical modeling with a systematic synthesis of clinical trial outcomes to define the optimal target pupil size for pharmacologic presbyopia correction. We modeled visual performance using the Visual Strehl Ratio of the Optical Transfer Function (VSOTF) and convolved images of optotypes across a range of pupil diameters from 1.5 mm to 3.5 mm. This combined optical–clinical approach allowed us to quantitatively compare modeled image quality and depth of focus predictions with real-world clinical efficacy data from pilocarpine-based interventions. Simulations showed that smaller pupil sizes (1.5–2.5 mm) significantly extended depth of focus compared to standard multifocal optics while maintaining image quality within acceptable limits. These findings align with clinical trials of pilocarpine formulations, which commonly achieve post-treatment pupil diameters in the 2.0–2.5 mm range and are associated with clinically meaningful gains in near vision. Our analysis uniquely demonstrates that these clinically achieved pupil sizes closely match the theoretically optimal 2.0–3.0 mm range identified in our modeling, strengthening the evidence base for drug design and patient selection. These results reinforce the role of pharmacologically controlled pupil size as a central target in presbyopia management. By explicitly linking predictive optical modeling with aggregated clinical outcomes, we introduce a novel framework to guide future pharmacologic development strategies and refine clinical counseling in the emerging era of presbyopia therapeutics. Full article

Since the advent of lasers, coherent optical imaging has found significant applications in optical precision measurement. Among these, digital holography is a key research area. In detection studies, accurately obtaining the amplitude and phase of the object being measured through digital holographic image reconstruction is a critical research task. However, current coherent optical imaging formulas can only calculate the amplitude distribution of the image light field when the object size is smaller than one-quarter of the diameter of the optical system’s entrance pupil, making it difficult to meet the needs of applied research. This paper derives formulas that are not restricted by the size of the incident pupil and can calculate the amplitude and phase distributions of the image light field. Based on the mathematical analysis of the formulas, this paper introduces a technique for obtaining the amplitude and phase distribution of detected objects and provides experimental evidence. Full article

Background: Ear advantage (EA) reflects hemispheric asymmetries in auditory processing. While a right-ear advantage (REA) for speech and a left-ear advantage (LEA) for music are well documented in typically developing individuals, it is unclear how these patterns manifest in young children with cochlear implants (CIs). This study investigated whether pupillometry could reveal asymmetric listening efforts in toddlers with bilateral CIs when listening to speech and music under monaural stimulation. Methods: Thirteen toddlers (mean age = 36.2 months) with early bilateral CIs participated. Pupillary responses were recorded during passive listening to speech and music stimuli, presented in quiet or with background noise. Each child was tested twice, once with only the left CI active and once with only the right CI active. Linear mixed-effects models assessed the influence of session (left/right CI), signal type (speech/music), and background noise. Results: A significant interaction between session and signal type was observed (p = 0.047). Speech elicited larger pupil sizes when processed through the left CI, while music showed no significant lateralized effects. Age and speech therapy frequency moderated pupil responses in speech and music trials, respectively. Conclusions: Pupillometry reveals subtle asymmetric listening effort in young CI users depending on the listening ear, suggesting early emerging functional lateralization despite sensory deprivation and device-mediated hearing. Full article

Background/Objectives: To assess the optical performance of two refractive premium IOLs across pupil sizes and values of corneal spherical aberration (SA). Methods: Two refractive IOLs were evaluated in this study: Tecnis Eyhance and Mini Well. The surface profiles were obtained to calculate the through-object MTF (TO MTF) curves and simulate optotype images. Entrance pupil sizes ranging from 2 to 5.5 and three corneal models were analyzed in the simulation: an average population aberrated cornea, an aberration-free cornea and a post-Lasik myopic cornea. Results: For Model 1 and pupil sizes between 3.0 and 3.5 mm, Mini Well provided acceptable visual quality from far to near distances, whereas Eyhance struggled to maintain visual quality at distances closer than intermediate. For patients with lower-than-normal corneal SA (i.e., more prolate corneas, such as post-hyperopic LASIK) both IOLs exhibited a hyperopic shift in far focus. Conversely, for patients with higher-than-normal corneal SA (i.e., more oblate corneas, such as post-myopic LASIK), the shift occurred in the myopic direction. Despite the implementation of an optimized IOL power to circumvent any shift, the TO MTF nevertheless reflected the interaction between corneal and IOL SA. Furthermore, the Mini Well demonstrated increased tolerance to less negative SA values, while Eyhance exhibited behavior consistent with a monofocal lens for more positive SA values. Conclusions: Surgeons should consider each patient’s corneal asphericity and typical pupil diameter when selecting and calculating the power of the premium IOLs studied, particularly in patients with a history of refractive surgery. Full article

Pupil cycle time (PCT) estimates the dynamics of a biofeedback loop established between pupil size and stimulus luminance, size or colour. The PCT is useful for probing the functional integrity of the retinopupillary circuits, and is therefore potentially applicable for assessing the effects of damage due to retinopathies or neuropathies. In previous studies, PCT was measured by manually counting the number of pupil oscillations during a fixed period to calculate the PCT. This method is scarce, requires a good expertise and cannot be used to estimate several PCT parameters, such as the oscillation amplitude or variability. We have developed a computerised setup based on eye-tracking that expands the possibilities of characterising PCT along several dimensions: oscillation frequency and regularity, amplitude and variability, which can be used with a large palette of stimuli (different colours, sizes, shapes or locations), and further allows measuring blinking frequency and eye movements. We used this method to characterise the PCT in young control participants as well as in patients with several pathologies, including age-related macular degeneration (AMD), diabetic retinopathy (DR), retinitis pigmentosa (RP), Stargardt disease (SD), and Leber hereditary optic neuropathy (LHON). We found that PCT is very regular and stable in young healthy participants, with little inter-individual variability. In contrast, several PCT features are altered in older healthy participants as well as in ocular diseases, including slower dynamics, irregular oscillations, and reduced oscillation amplitude. The distinction between patients and healthy participants based on the calculation of the area under the curve of the receiver operating characteristics (AUC of ROC) were dependent on the pathologies and stimuli (0.7 < AUC < 1). PCT nevertheless provides relevant complementary information to assess the physiopathology of ocular diseases and to probe the functioning of retino-pupillary circuits. Full article

Background and Objectives: Vision uses about half of the pathways within the brain, and these anatomical structures are susceptible to injury in concussion. Authors have suggested that subconcussive head impacts, common in soccer, may disrupt visual function. The following study aimed to explore and compare quantitative pupillometry and Vestibular Ocular Motor Screening (VOMS) in female soccer athletes. Materials and Methods: Twenty-six Division 1 female soccer athletes (20.46 ± 2.36 years) received baseline quantitative pupillometry and VOMS measurements. Results: Of the 26 tested athletes, 3 (11.5%) had clinically significant pupillometry findings at baseline. The mean Neurological Pupil Index or NPi, a composite generated from pupillometry, did not vary: 3.9 ± 0.4 (right eye) and 4.0 ± 0.4 (left eye). No difference in NPi was observed compared to the VOMS score (p > 0.05). Kruskal–Wallis H tests were significant in the right eye for constriction percentage (χ²(2) = 17.843, p < 0.001, E² = 0.69) and minimum pupil size (χ²(2) = 7.976, p = 0.019, E² = 0.31). A post hoc Dunn test showed significant differences in constriction percentage and minimum pupil size between low NPi and high NPi groups (p < 0.05). One athlete sustained a concussion. NPi was measured within 24 h and was normal, but VOMS was not (total score = 4). Conclusions: The components of pupillometry need more investigation, and there is a need for agreement on concussion-specific cutoffs for quantitative pupillometry for concussion assessment. The lack of a relationship between quantitative pupillometry and VOMS suggests that these tools evaluate different constructs. Athletes with an NPi < 3.8 had significantly less constriction percentage and larger minimum pupil size than athletes with higher NPi scores. More research should be carried out to determine the usefulness of the NPi score, and perhaps researchers should consider individual pupillometry components. Full article

This paper outlines a new system for processing of eye-tracking data in basketball live games with two pre-trained Artificial Intelligence (AI) models. blueThe system is designed to process and extract features from data of basketball coaches and referees, recorded with the Pupil Labs Neon Eye Tracker, a device that is specifically optimized for video analysis. The research aims to present a tool useful for understanding their visual attention patterns during the game, what they are attending to, for how long, and their physiological responses, blueas is evidenced through pupil size changes. AI models are used to monitor events and actions within the game and correlate these with eye-tracking data to provide understanding into referees’ and coaches’ cognitive processes and decision-making. This research contributes to the knowledge of sport psychology and performance analysis by introducing the potential of Artificial Intelligence (AI)-based eye-tracking analysis in sport with wearable technology and light neural networks that are capable of running in real time. Full article

Pain estimation is a critical aspect of healthcare, particularly for patients who are unable to communicate discomfort effectively. The traditional methods, such as self-reporting or observational scales, are subjective and prone to bias. This study proposes a novel system for non-invasive pain estimation using eye-tracking technology and advanced machine learning models. The methodology begins with preprocessing steps, including resizing, normalization, and data augmentation, to prepare high-quality input face images. DeepLabV3+ is employed for the precise segmentation of the eye and face regions, achieving 95% accuracy. Feature extraction is performed using VGG16, capturing key metrics such as pupil size, blink rate, and saccade velocity. Multiple machine learning models, including Random Forest, SVM, MLP, XGBoost, and NGBoost, are trained on the extracted features. XGBoost achieves the highest classification accuracy of 99.5%, demonstrating its robustness for pain level classification on a scale from 0 to 5. The feature analysis using SHAP values reveals that pupil size and blink rate contribute most to the predictions, with SHAP contribution scores of 0.42 and 0.35, respectively. The loss curves for DeepLabV3+ confirm rapid convergence during training, ensuring reliable segmentation. This work highlights the transformative potential of combining eye-tracking data with machine learning for non-invasive pain estimation, with significant applications in healthcare, human–computer interaction, and assistive technologies. Full article

The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to detect emotional states and deliver customized responses. The technology provides benefits through accessibility, responsiveness, and adaptability but generates multiple complex ethical issues. The combination of emotional profiling with biased algorithmic interpretations of culturally diverse expressions and affective data collection without meaningful consent presents major ethical concerns. The increased presence of these systems in classrooms, therapy sessions, and personal devices makes the potential for misuse or misinterpretation more critical. The paper integrates findings from literature review and initial emotion-recognition studies to create a conceptual framework that prioritizes data dignity, algorithmic accountability, and user agency and presents a conceptual framework that addresses these risks and includes safeguards for participants’ emotional well-being. The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. The authors present functional recommendations that guide developers to create ethically robust systems that match user principles and regulatory requirements. The development of real-time feedback loops for user awareness should be combined with clear disclosures about data use and participatory design practices. The successful oversight of these systems requires interdisciplinary work between researchers, policymakers, designers, and ethicists. The paper provides practical ethical recommendations for developing affective computing systems that advance the field while maintaining responsible deployment and governance in academic research and industry settings. The findings hold particular importance for high-stakes applications including healthcare, education, and workplace monitoring systems that use emotion-recognition technology. Full article

The purpose of this study is to evaluate the potential impact of small aperture optics on corneal aberrations in post-RK patients. Preoperative data was evaluated from 32 eyes of 23 post-RK patients. Scheimpflug tomography was used to obtain measurements of corneal HOAs at 6-mm, 4-mm, and 2-mm corneal plane aperture diameters. The data was extrapolated using a non-linear fit to estimate HOAs that would be obtained with the 1.6 mm effective pinhole IOL aperture at the corneal plane for individual patients. The average RMS HOAs estimated for the 1.6 mm aperture was 0.063 ± 0.015 μm compared to 0.185 ± 0.029 μm for the natural pupil size. A postoperative RK case with an IC-8^® Apthera™ unilateral implantation demonstrated a 70% reduction in HOAs by objective measurement and prediction, plus a 2-line improvement in CDVA. Prediction modeling revealed that HOAs may be reduced in post-RK patients following pinhole IOL implantation, compared to the natural photopic pupil size. Furthermore, the approach can be used to guide which post-RK patients would benefit from a small aperture IOL during cataract surgery. Full article

Background: Measuring contrast sensitivity for each of the three cone types separately allows for a more precise and clinically valuable assessment of color vision. This study examined how pupil size affects cone contrast sensitivity (CCS). Methods: This study included 50 participants of equal gender. The mean age was 20.88 (±1.8) years. Using the ColorDx, a Landolt C stimulus of various sizes in an adaptive screening mode, we sequentially determined contrast sensitivity for long-, medium-, and short-wavelength stimuli. Two consecutive measurements were performed on participants, one with their natural pupil size (range 4–5 mm diameter) and, subsequently, with six artificial eye pupils (1 mm to 6 mm). Results: Generally, the 1 mm pupil size caused the greatest reduction in contrast sensitivity for two of the three cones. There was no significant main effect of sex (F = 0.96, df = 1, p = 0.32) on the log cone contrast sensitivity of the L-cone. However, pupil size had a significant main effect (F = 116.1, df = 6, p < 0.001). Within each sex, the log CCS was significantly reduced as the pupil size decreased compared with the normal pupil size. Conclusions: New technologies that assess individual cone pathway functions could potentially assist in identifying early or progressive conditions that may impact color vision pathways from the retina to the brain. Standardized protocols—such as controlled retinal illumination—are critical to avoid misinterpretation. Full article

Eye movement detection technology holds significant potential across medicine, psychology, and human–computer interaction. However, traditional methods, which primarily rely on tracking the pupil and cornea during the open-eye state, are ineffective when the eye is closed. To address this limitation, we developed a novel system capable of real-time eye movement detection even in the closed-eye state. Utilizing a micro-camera based on the OV9734 image sensor, our system captures image data to construct a dataset of eyelid images during ocular movements. We performed extensive experiments with multiple versions of the YOLO algorithm, including v5s, v8s, v9s, and v10s, in addition to testing different sizes of the YOLO v11 model (n < s < m < l < x), to achieve optimal performance. Ultimately, we selected YOLO11m as the optimal model based on its highest AP0.5 score of 0.838. Our tracker achieved a mean distance error of 0.77 mm, with 90% of predicted eye position distances having an error of less than 1.67 mm, enabling real-time tracking at 30 frames per second. This study introduces an innovative method for the real-time detection of eye movements during eye closure, enhancing and diversifying the applications of eye-tracking technology. Full article

In recent times, 3D eye tracking methods have been actively studied to utilize gaze information in various applications. As a result, there is growing interest in gaze depth estimation techniques. This study introduces a monocular method for estimating gaze depth using DPI distance and pupil size. We acquired right eye images from eleven subjects and at ten gaze depth levels ranging from 15 cm to 60 cm at intervals of 5 cm. We used a camera equipped with an infrared LED to capture the images. We applied a contour-based algorithm to detect the first Purkinje image and pupil, then used a template matching algorithm for the fourth Purkinje image. Using the detected features, we calculated the pupil size and DPI distance. We trained a multiple linear regression model on data from eight subjects, achieving an R² value of 0.71 and a root mean squared error (RMSE) of 7.69 cm. This result indicates an approximate 3.15% reduction in error rate compared to the general linear regression model. Based on the results, we derived the following equation: depth fixation = 20.746 × DPI distance + 5.223 × pupil size + 16.495 × (DPI distance × pupil size) + 13.880. Our experiments confirmed that gaze depth can be effectively estimated from monocular images using DPI distance and pupil size. Full article

Eye-gaze writing technology holds significant promise but faces several limitations. Existing eye-gaze-based systems often suffer from slow performance, particularly under challenging conditions such as low-light environments, user fatigue, or excessive head movement and blinking. These factors negatively impact the accuracy and reliability of eye-tracking technology, limiting the user’s ability to control the cursor or make selections. To address these challenges and enhance accessibility, we created a comprehensive dataset by integrating multiple publicly available datasets, including the Eyes Dataset, Dataset-Pupil, Pupil Detection Computer Vision Project, Pupils Computer Vision Project, and MPIIGaze dataset. This combined dataset provides diverse training data for eye images under various conditions, including open and closed eyes and diverse lighting environments. Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. For object detection, we implemented YOLOv8, SSD, and Faster R-CNN. For image segmentation, we employed DeepLab and U-Net. Finally, for self-supervised learning, we utilized the SimCLR algorithm. Our results indicate that the Haar classifier achieves the highest accuracy (0.85) with a model size of 97.358 KB, while YOLOv8 demonstrates competitive accuracy (0.83) alongside an exceptional processing speed and the smallest model size (6.083 KB), making it particularly suitable for cost-effective real-time eye-gaze applications. Full article

Non-human primates (NHPs) are extensively utilized to investigate the neural mechanisms underlying face processing; however, measuring their brain activity necessitates a diverse array of technologies. Pupillometry emerges as a convenient, cost-effective, and non-invasive alternative for indirectly assessing brain activity. To evaluate the efficacy of pupillometry in assessing facial and emotional processing in NHPs, this study designed a face fixation task for experimental monkeys (Rhesus macaque) and recorded variations in their pupil size in response to face images with differing characteristics, such as species, emotional expression, viewing angles, and orientation (upright vs. inverted). All face images were balanced with luminance and spatial frequency. A sophisticated eye-tracking system (Eye-link 1000 plus) was employed to observe the pupils and track the viewing trajectories of monkeys as they examined images of faces. Our findings reveal that monkeys exhibited larger pupil sizes in response to carnivore faces (versus human faces, p = 0.035), negative conspecific faces (versus human faces, p = 0.018), and profile viewing angles (versus frontal view angles, p = 0.010). Notably, pupil size recorded during the 500–1000 ms post-stimulus interval was negatively correlated with their gaze durations directed at those images (r = −0.357, p = 0.016). Overall, this study demonstrates that pupillometry effectively captures subtle differences in facial and emotional processing, underscoring its potential as a valuable tool in future cognitive research and the diagnosis of disorders. Full article