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RetinaRegNet: A zero-shot approach for retinal image registration.

Comput Biol Med

January 2025

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32610, United States; Department of Medicine, University of Florida, Gainesville, FL, 32610, United States; Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, 32610, United States; Intelligent Clinical Care Center, University of Florida, Gainesville, FL, 32610, United States. Electronic address:

Retinal image registration is essential for monitoring eye diseases and planning treatments, yet it remains challenging due to large deformations, minimal overlap, and varying image quality. To address these challenges, we propose RetinaRegNet, a multi-stage image registration model with zero-shot generalizability across multiple retinal imaging modalities. RetinaRegNet begins by extracting image features using a pretrained latent diffusion model.

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Cuprous oxide (CuO) thin films were chemically deposited from a solution onto GaAs(100) and (111) substrates using a simple three-component solution at near-ambient temperatures (10-60 °C). Interestingly, a similar deposition onto various other substrates including Si(100), Si(111), glass, fluorine-doped tin oxide, InP, and quartz resulted in no film formation. Films deposited on both GaAs(100) and (111) were found alongside substantial etching of the substrates.

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Background: Consistent evidence shows stigma impedes healthcare access in people living with HIV (PLWH) and men who have sex with men (MSM). We evaluated the impact of a stigma reduction training for providers whose design was informed by direct observation of their clinical behaviors obtained through visits by incognito standardized patient (SP).

Setting: We conducted this study in in sexually transmitted infection clinics in Guangzhou, China.

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Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.

Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).

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Importance: Routine preoperative blood tests and electrocardiograms before low-risk surgery do not prevent adverse events or change management but waste resources and can cause patient harm. Given this, multispecialty organizations recommend against routine testing before low-risk surgery.

Objective: To determine whether a multicomponent deimplementation strategy (the intervention) would reduce low-value preoperative testing before low-risk general surgery operations.

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