Background/objectives: Adaptive optics ophthalmoscopy (AOO) has the potential to provide insights into AMD pathology and to assess the risk of progression. We aim to utilise AOO to describe detailed features of intermediate AMD and to characterise microscopic changes during atrophy development.
Subjects/methods: Patients with intermediate AMD were recruited into PINNACLE, a prospective observational cohort study.
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues.
View Article and Find Full Text PDFBackground: During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt.
Objective: To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense.