Background/Objectives: This study aims to develop and validate a Glaucoma Health Score (GHS) that incorporates multiple individual glaucoma risk factors to enhance glaucoma detection in screening environments. Methods: The GHS was developed using a retrospective dataset from two clinical sites, including both eyes of glaucoma patients and controls. The model incorporated age, central corneal thickness, intraocular pressure, pattern standard deviation from a visual field threshold 24-2 test, and two parameters from an optical coherence tomography (OCT) test: the average circumpapillary retinal nerve fiber layer thickness and the minimum thickness of the six sectors of the macular ganglion cell plus the inner plexiform layer. The GHS was then validated in two independent datasets: one from primary care sites using Maestro OCT data (test dataset 1) and another from an academic center using DRI OCT Triton (test dataset 2). Results: Both eyes of 51 glaucoma patients and 67 controls were included in the development dataset. Setting the GHS cutoff at 75 points out of 100, test dataset 1, which comprised 41 subjects with glaucoma and 41 healthy controls, achieved an area under the receiver operating characteristic curve (AUROC) of 0.98, with a sensitivity of 71% and specificity of 98%; test dataset 2, which included 53 patients with glaucoma and 53 healthy controls, resulted in an AUROC of 0.95, with a sensitivity of 75% and specificity of 96%. A decision curve analysis across all datasets demonstrated a higher net benefit for the GHS model compared to individual OCT parameters. Conclusions: The GHS offers a feasible, standardized approach for early detection of glaucoma, providing strong specificity and acceptable sensitivity, with clear decision-making benefits in screening settings.

Background/Objectives: This study aimed to evaluate the clinical interchangeability of intraocular pressure (IOP) measurements between a non-contact tonometer (NCT), the TRK-3 OMNIA, and Goldmann applanation tonometry (GAT) and to assess the repeatability of TRK-3 measurements. Methods: This prospective, multicenter study included 120 subjects stratified into three IOP groups based on GAT measurements: low IOP (7–16 mmHg), intermediate IOP (>16 to <23 mmHg), and high IOP (≥23 mmHg). The study eye was randomly selected from each subject. IOP was measured using both TRK-3 OMNIA and GAT following a standardized protocol. Agreement between the two methods was evaluated using Bland–Altman analysis, 95% limits of agreement (LoA), and equivalence testing via the two one-sided test (TOST) approach with a predefined ±5 mmHg margin. Linear regression analysis was performed to characterize the relationship between TRK-3 and GAT measurements. The repeatability of TRK-3 measurements was assessed using the intraclass correlation coefficient (ICC), repeatability limit, and coefficient of variation (CV). Results: Across all subjects, the mean difference between TRK-3 OMNIA and GAT IOP measurements was −0.2 mmHg. TRK-3 OMNIA overestimated IOP in the low IOP group (mean difference: 2.1 mmHg, LoA: −1.2 to 5.4 mmHg) and underestimated in the high IOP group (mean difference: −2.4 mmHg, LoA: −5.9 to 1.1 mmHg), while agreement was highest in the intermediate IOP group (−0.2 mmHg, LoA: −2.9 to 2.5 mmHg). Despite the systematic trend, equivalence testing confirmed statistical equivalence across all groups, with 90% confidence intervals (CI) of 1.7 to 2.5 mmHg (low IOP group), −0.6 to 0.2 mmHg (intermediate IOP group), and −2.9 to −2.0 mmHg (high IOP group). Linear regression analysis found a strong correlation (r = 0.92) between TRK-3 and GAT. The TRK-3 OMNIA demonstrated good repeatability, with an ICC of 0.94, a repeatability limit of 3.12 mmHg, and a CV of 5.65%. The repeatability limits were 2.22 mmHg, 2.60 mmHg, and 4.19 mmHg in the low, intermediate, and high IOP groups, respectively. Conclusions: TRK-3 OMNIA and GAT measurements showed strong agreement, statistical equivalence, and a high correlation, supporting their clinical interchangeability. TRK-3 also demonstrated high repeatability. These findings indicate that this automated non-contact tonometer provides reliable and repeatable IOP measurements, supporting its integration into routine clinical workflows.

Background/Objectives: Glaucoma, a leading cause of blindness worldwide, is often associated with high intraocular pressure (IOP), which eventually leads to loss of retinal ganglion cells and the retinal nerve fiber layer. Visual field (VF) testing is a principal method of diagnosing and monitoring this disease. Suprathreshold VF test programs are quicker than threshold strategies and are often used as a screening tool. This study evaluates the TEMPO/IMOvifa (Topcon Healthcare/CREWT Medical Systems, Tokyo, Japan), a bilateral standard automated perimeter with a suprathreshold screening program by assessing the sensitivity in a glaucoma cohort and the specificity in a healthy cohort. Methods: All subjects were tested at a single site and underwent a comprehensive ocular examination to categorize them into either a healthy or glaucoma group. As part of the testing procedure, two TEMPO suprathreshold VFs were conducted in sequence and accompanied by a threshold VF test. Results: A total of 193 eyes (randomized study eye) (193 subjects) were evaluated in the final analysis (101 healthy and 92 glaucoma), and average suprathreshold test time (SD) per eye was 39.4 (±4.86) seconds. Specificity was at 91% in the healthy group and sensitivity was at 49% in the glaucoma group. Sensitivity was at 100% when applied to glaucoma cases with an MD of less than −3 dB. Conclusions: The TEMPO screening program demonstrated strong specificity in detecting true healthy cases. It also demonstrated a strong sensitivity when screening mild to moderate glaucoma. Early glaucoma and glaucoma suspects would benefit from complementary modalities such as optical coherence tomography and threshold perimetry to effectively diagnose. Utilizing this screening program in optometric and ophthalmic settings could yield benefits for both the practitioner and the patient.

Purpose: Real-world ocular imaging datasets are essential for advancing research in artificial intelligence (AI), autonomous disease screening, and clinical decision support. The Primary Eye Care dataset is a large-scale collection of de-identified retinal imaging data from routine optometric care, made available through the Institute for Digital Health (IDHea)—a secure research platform established by Topcon Healthcare, Inc. This dataset provides an opportunity to study eye health in a community setting and will be available via this cloud-based platform. Methods: Data were collected and de-identified from individuals who underwent imaging as part of their routine care across 40 optometry practices in the United States and one practice in Australia. The dataset includes three-dimensional optical coherence tomography (OCT), and color fundus photographs acquired using Maestro devices (Topcon Corp., Tokyo, Japan), along with demographic data including age and sex. Imaging data were converted to DICOM format, and OCT analysis metrics such as retinal layer thicknesses were derived. Additional labels including image quality, vessel metrics, and retinal pigment score were generated using open-source AI models. Results: TThe dataset comprises 873,291 image acquisitions from 276,061 subjects with a mean age of 43.8 years (standard deviation = 19.5). 48.7% were female, 36.2% as male, and 15.1% not reported. Most OCT scans followed the 12 × 9 mm 3D Wide protocol (86.3%), with additional 3D Macula, 3D Disc, anterior segment, radial, and line scans. 59,049 subjects (21.4%) had two or more scans separated by ≥ 365 days. Pre-processed metrics and AI-derived labels, such as TopQ image quality scores, glaucoma risk score, and AutoMorph features are included. 89.4% of OCT scans scored above 25 on the TopQ scale, indicating reliable image quality. A propensity score-matched test subset (∽10%) was held out to enable consistent benchmarking across studies. Conclusion: The Primary Eye Care Dataset provides a large-scale, real-world collection of ocular imaging data, reflecting a largely healthy, community-based population attending routine optometric visits. This makes it particularly valuable for developing AI models aimed at early detection and prevention at the population level, where most eyes are healthy, and disease prevalence is low. Data access is governed by an independent committee to ensure ethical and responsible use; more information is available at IDHea.net

Background/Objectives: To evaluate the clinical performance of two optical coherence tomography angiography (OCTA) devices, including a semi-automated device, with respect to image quality and pathology detection, with fluorescein angiography (FA) and indocyanine green angiography (ICGA) serving as the reference standards. Methods: In this prospective cross-sectional study, normal eyes and those with various retinal and choroidal pathologies were enrolled and underwent OCTA scanning using semi-automated 3D OCT-1 Maestro2 and Cirrus™ HD-OCT 5000 devices, as well as FA/ICGA imaging. OCTA scans and FA/ICGA images were independently graded for image quality and the visibility of prespecified anatomic vascular features, along with the presence or absence of pathology on the OCTA scans and the FA/ICGA images (within regions corresponding to the OCTA scan areas). Positive percent agreement (PPA), defined as the proportion of eyes in which the OCTA demonstrated pathology when the corresponding FA/ICGA showed pathology, and negative percent agreement (NPA), defined as the proportion of eyes in which the OCTA showed no pathology when the FA/ICGA also showed no pathology, were calculated. Results: In total, 38 normal eyes and 86 pathologic eyes were enrolled in the study. The majority of images for both devices were considered clinically useful. The PPA and NPA were high for both devices, indicating a good ability to identify disease when present and to rule it out when not present. Conclusions: The findings of this study suggest that the semi-automated Maestro2 and Cirrus have comparably good clinical performance, particularly with regard to accuracy when identifying vascular pathologies.

Purpose: Ocular screening in primary care is increasingly recognized as a valuable opportunity to detect a wide range of eye conditions at an early stage, especially among individuals with systemic risk factors. The Primary Care Screening dataset is a large-scale, real-world collection of de-identified color fundus photographs (CFP) acquired during routine diabetic retinopathy (DR) screening visits in primary care clinics across the United States, and is available through the Institute for Digital Health (IDHea)—a secure research platform established by Topcon Healthcare, Inc. Methods: The dataset includes CFP from individuals who participated in eye screening at 643 clinical sites in the United States. The majority of images were obtained using the TRC-NW400 (Topcon Corp., Tokyo, Japan), although other imaging devices were also used. Each image was graded by an eye care specialist using the International Clinical Diabetic Retinopathy (ICDR) grading system. Graders also recorded image quality and a wide range of other retinal findings. The dataset also includes patient-level demographics including age, sex, and 3-digit ZIP code, image metadata, along with model-generated annotations such as AutoMorph image quality, vascular metrics, and retinal pigment scores. Results: As of March 2025, the dataset includes 427,182 CFP from 161,705 subjects, representing 372,528 eyes and 186,264 individual visits. Image quality was graded as Excellent or Good (218,139 eyes), Fair (109,127 eyes), or Unreadable (37,491 eyes). Most eyes (n = 293,391) had no diabetic retinopathy present (NDRP), while 15,514 had mild non-proliferative DR (NPDR), 10,856 moderate NPDR, 1,140 severe NPDR, and 1,644 proliferative DR. Diabetic macular edema was present in 4–34% of NPDR categories and 18% of proliferative DR cases. Additional findings included drusen or pigmentary changes (n = 9,649), glaucoma suspect (n = 4,257), and macular degeneration (n = 3,581). Conclusion: The Primary Care Screening dataset represents one of the largest real-world collections of retinal images acquired in primary care settings. Its size, diversity, and detailed expert grading make it a valuable resource for research into automated screening, ocular disease prevalence, and AI model development. An independent Data Access and Governance committee oversees research applications to ensure responsible use. The dataset will be made available via the secure IDHea research platform. More information is available at IDHea.net .