Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.

Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening leading to enhance in early detection and improve healthcare quality, particularly in underserved areas. However, the widespread adoption of such AI-based technologies faces challenges primarily related to the trustworthiness of the system.

Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review.

Topic: This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification.

Clinical Relevance: Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting.

Rates of Laser Trabeculoplasty by Ophthalmologists and Optometrists: A Comparative Analysis of the CMS Medicare Public Use File.

Purpose: To provide a comparative analysis of rates of laser trabeculoplasty (LTP) among eye care providers in the USA.

Methods: This retrospective cohort analysis utilized the Centers for Medicare and Medicaid Services (CMS) Public Use File (PUF), 2015-2018. We used CPT code 65855 to select eye care providers who performed LTP in three key US states (KY, LA, and OK).

The Relevance of Arterial Blood Pressure in the Management of Glaucoma Progression: A Systematic Review.

Background: Glaucoma is one of the leading causes of global blindness and is expected to co-occur more frequently with vascular morbidities in the upcoming years, as both are aging-related diseases. Yet, the pathogenesis of glaucoma is not entirely elucidated and the interplay between intraocular pressure, arterial blood pressure (BP) and ocular perfusion pressure is poorly understood.

Objectives: This systematic review aims to provide clinicians with the latest literature regarding the management of arterial BP in glaucoma patients.

Novel Methods of Identifying Individual and Neighborhood Risk Factors for Loss to Follow-Up After Ophthalmic Screening.

Prcis: Residence in a middle-class neighborhood correlated with lower follow-up compared with residence in more affluent neighborhoods. The most common explanations for not following up were the process of making an appointment and lack of symptoms.

Purpose: To explore which individual-level and neighborhood-level factors influence follow-up as recommended after positive ophthalmic and primary care screening in a vulnerable population using novel methodologies.

Effect of 24-h blood pressure dysregulations and reduced ocular perfusion pressure in open-angle glaucoma progression.

Background: Low ocular perfusion pressure (OPP), which depends on the mean arterial pressure (MAP) and intraocular pressure (IOP), is associated with glaucoma. We studied 24-h MAP dysregulations and OPP in relation to the progression of glaucoma damage.

Methods: We retrospectively analyzed 155 normal-tension glaucoma (NTG) and 110 primary open-angle glaucoma (POAG) patients aged 18 years old followed at the University Hospital Leuven with repeated visual field tests ( n  = 7000 measures, including both eyes) who underwent 24-h ambulatory blood pressure monitoring.

Salary Negotiations: Gender Differences in Attitudes, Priorities, and Behaviors of Ophthalmologists.

Purpose: To investigate attitudes, priorities, and behaviors of ophthalmologists in salary negotiations.

Design: Cross-sectional study.

Methods: A Qualtrics survey was disseminated to U.

A Review of Cost-Effectiveness Analyses for Open Angle Glaucoma Management.

Purpose: Cost-effectiveness analyses (CEAs) quantify and compare both costs and measures of efficacy for different interventions. As the costs of glaucoma management to patients, payers, and physicians are increasing, we seek to investigate the role of CEAs in the field of glaucoma and how such studies impact clinical management.

Methods: We adhered to the "Preferred Reporting Items for Systematic Reviews and Meta-analyses" guidelines for our systematic review structure.

Environmental influences on ophthalmic conditions: A scoping review.

Background: Environmental factors have been implicated in various eye pathologies. The purpose of this review is to synthesise the published research on environmental effects on eye disease.

Methods: Four databases were searched for terms relating to environmental exposures and ophthalmic disease.

Federated AI, Current State, and Future Potential.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data.

Cost-effectiveness of Prophylactic Laser Peripheral Iridotomy in Primary Angle-Closure Suspects.

Purpose: To investigate the cost-effectiveness (CE) of prophylactic laser peripheral iridotomy (LPI) in primary angle-closure (PAC) suspects (PACSs).

Design: Cost-effectiveness analysis utilizing Markov models.

Subjects: Patients with narrow angles (PACSs).

Automated 360-degree goniophotography with the NIDEK Gonioscope GS-1 for glaucoma.

This study was registered with ClinicalTrials.gov (ID: NCT03715231). A total of 20 participants (37 eyes) who were 18 or older and had glaucoma or were glaucoma suspects were enrolled from the NYU Langone Eye Center and Bellevue Hospital.

The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications.

Objective: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity.

Applications of Artificial Intelligence and Deep Learning in Glaucoma.

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images.

Progression of functional and structural glaucomatous damage in relation to diurnal and nocturnal dips in mean arterial pressure.

Background: Systemic hypoperfusion plays a pivotal role in the pathogenesis of primary open-angle glaucoma (POAG). Extreme dips in mean arterial pressure (MAP) due to high 24-h variability are associated with POAG, however, whether this is driven by diurnal or nocturnal dips remains undocumented. We aimed this study to investigate the association of POAG damage with variability and dips in the diurnal and nocturnal MAP.

Methods and Impact for Using Federated Learning to Collaborate on Clinical Research.

Background: The development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model.

Validation of the New York University Langone Eye Test Application, a Smartphone-Based Visual Acuity Test.

Purpose: To validate and assess user satisfaction and usability of the New York University (NYU) Langone Eye Test application, a smartphone-based visual acuity (VA) test.

Design: Mixed-methods cross-sectional cohort study.

Participants: Two hundred forty-four eyes of 125 participants were included.

Topical Glaucoma Therapy Is Associated With Alterations of the Ocular Surface Microbiome.

Purpose: To investigate the ocular surface microbiome of patients with unilateral or asymmetric glaucoma being treated with topical ophthalmic medications in one eye and to determine whether microbial community changes were related to measures of ocular surface disease.

Methods: V3-V4 16S rRNA sequencing was conducted on ocular surface swabs collected from both eyes of 17 subjects: 10 patients with asymmetric/unilateral glaucoma using topical glaucoma therapy on only one eye and seven age-matched, healthy controls with no history of ocular disease or eyedrop use. Samples were categorized into three groups: patients' glaucomatous eye treated with eyedrops, patients' contralateral eye without eyedrops, and healthy control eyes.