Glaucoma detection and staging from visual field images using machine learning techniques.

Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.

Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.

Improving Collaborative Management of Multiple Mental and Physical Health Conditions: A Qualitative Inquiry into Designing Technology-Enabled Services for Eliciting Patients' Values.

People with multiple chronic conditions (MCC) face challenges planning health care collaboratively with primary care clinicians, particularly when their priorities conflict. These challenges intensify with symptoms of anxiety or depression. Elicitation of patients' values is promoted as a means to aligning patient and clinician priorities in primary care, and as a component of psychotherapy for anxiety and depression.

Sexual and Gender Minority Adolescents' Preferences for HIV Pre-Exposure Prophylaxis Social Marketing Campaigns: Qualitative Preimplementation Study.

Background: Sexual and gender minority (SGM) adolescents in the United States are disproportionately affected by HIV. Pre-exposure prophylaxis (PrEP) is a highly effective biomedical HIV prevention method, but its awareness and uptake among SGM adolescents are low. There are no adolescent-centered PrEP social marketing campaigns in the United States that have the potential to increase awareness and interest in PrEP.

Spatially constrained hyperpolarized 13C MRI pharmacokinetic rate constant map estimation using a digital brain phantom and a U-Net.

Fitting rate constants to Hyperpolarized [1-C]Pyruvate (HP C13) MRI data is a promising approach for quantifying metabolism in vivo. Current methods typically fit each voxel of the dataset using a least-squares objective. With these methods, each voxel is considered independently, and the spatial relationships are not considered during fitting.

Psychometric Evaluation of the Scleroderma Skin Questionnaire: A Novel Patient Reported Outcome for Skin Disease in Patients with Systemic Sclerosis.

Objective: To evaluate the psychometric properties of the Scleroderma Skin Questionnaire (SSQ), a novel patient-reported outcome (PRO) to assess systemic sclerosis (SSc) related skin symptoms.

Methods: The SSQ was administered to 799 adults (mean age 52.7; 82% female) enrolled in the SSc Collaborative National Quality and Efficacy Registry (CONQUER).