Gestational hypertension increases risk of seizures in children and mice.

Gestational hypertension (GH) is prevalent, with life-long health burdens for mothers and their children exposed in utero. We analyzed the nation-wide Epic Cosmos dataset and found significantly higher rates of seizures in children of mothers with GH than in children of normotensive mothers. Complementary studies of nested Iowa and Stanford cohorts and a large Taiwanese cohort also revealed significantly increased seizure risk after covariate adjustments.

Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes.

This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication names, routes, and frequencies from publicly available free-text ophthalmology progress notes of 480 patients. 5,520 lines of annotated text were divided into train (N=3,864), validation (N=1,104), and test sets (N=552). We evaluated ChatGPT-3.

Sexual identity differences in ideal cardiovascular health among cisgender adults in the All of Us Research Program.

Background: Cardiovascular health (CVH) disparities have been documented among sexual minority adults, yet prior research has focused on individual CVH metrics. We sought to examine sexual identity differences in CVH using the American Heart Association's composite measure of ideal CVH, which provides a more comprehensive assessment of future CVD risk.

Methods: Data from the All of Us Research Program were analyzed.

The Impact of Race and Ethnicity on Cataract Surgery in the Nationwide All of Us Cohort.

Purpose: Cataracts are a leading cause of blindness treatable with surgery. The purpose of this retrospective study was to investigate the association between cataract surgery and race/ethnicity, socioeconomic status, healthcare access, and other factors related to social determinants of health.

Methods: A total of 37,204 participants with at least one cataract diagnosis were included in this study from the All of Us Research Program using electronic health records and self-reported surveys.

Independent Evaluation of RETFound Foundation Model's Performance on Optic Nerve Analysis Using Fundus Photography.

Purpose: This study evaluates RETFound, a retinal image foundation model, as a feature extractor for predicting optic nerve metrics like cup-to-disc ratio (CDR) and retinal nerve fiber layer (RNFL) thickness using an independent clinical dataset.

Design: Retrospective observational study.

Participants: Patients who underwent fundus photography and RNFL OCT at the Byers Eye Institute, Stanford University.

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

Purpose: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography (RNFL OCT) scans.

Methods: EHR data (demographics and clinical eye examinations) and RNFL OCT scans were identified for patients with glaucoma from an academic center (2008-2023). Comparing the novel TabNet deep learning architecture to a baseline XGBoost model, we trained and evaluated single modality models using either EHR or RNFL features, as well as fusion models combining both EHR and RNFL features as inputs, to predict glaucoma surgery within 12 months (binary).

Artificial Intelligence Models to Identify Patients at High Risk for Glaucoma Using Self-reported Health Data in a United States National Cohort.

Purpose: Early glaucoma detection is key to preventing vision loss, but screening often requires specialized eye examination or photography, limiting large-scale implementation. This study sought to develop artificial intelligence models that use self-reported health data from surveys to prescreen patients at high risk for glaucoma who are most in need of glaucoma screening with ophthalmic examination and imaging.

Design: Cohort study.

Artificial Intelligence Models to Identify Patients with High Probability of Glaucoma Using Electronic Health Records.

Purpose: Early detection of glaucoma allows for timely treatment to prevent severe vision loss, but screening requires resource-intensive examinations and imaging, which are challenging for large-scale implementation and evaluation. The purpose of this study was to develop artificial intelligence models that can utilize the wealth of data stored in electronic health records (EHRs) to identify patients who have high probability of developing glaucoma, without the use of any dedicated ophthalmic imaging or clinical data.

Design: Cohort study.

Prediction Models for Glaucoma in a Multicenter Electronic Health Records Consortium: The Sight Outcomes Research Collaborative.

Purpose: Advances in artificial intelligence have enabled the development of predictive models for glaucoma. However, most work is single-center and uncertainty exists regarding the generalizability of such models. The purpose of this study was to build and evaluate machine learning (ML) approaches to predict glaucoma progression requiring surgery using data from a large multicenter consortium of electronic health records (EHR).

Social Determinants of Health and Perceived Barriers to Care in Diabetic Retinopathy Screening.

Importance: Regular screening for diabetic retinopathy often is crucial for the health of patients with diabetes. However, many factors may be barriers to regular screening and associated with disparities in screening rates.

Objective: To evaluate the associations between visiting an eye care practitioner for diabetic retinopathy screening and factors related to overall health and social determinants of health, including socioeconomic status and health care access and utilization.

Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models.

Purpose: Prior artificial intelligence (AI) models for predicting glaucoma progression have used traditional classifiers that do not consider the longitudinal nature of patients' follow-up. In this study, we developed survival-based AI models for predicting glaucoma patients' progression to surgery, comparing performance of regression-, tree-, and deep learning-based approaches.

Design: Retrospective observational study.