Background and design of the Physical Frailty and Symptom Monitoring and Management Behaviors in Heart Failure (PRISM-HF) study: A mixed methods study.

Background: Many adults with heart failure (HF) are physically frail and have worse outcomes. While the biological profile of physical frailty in HF has been examined, the behavioral profile remains unstudied. Physical frailty may impact self-care behaviors, particularly symptom monitoring and management (SMM), which in turn results in adverse outcomes.

The TRIPOD-LLM reporting guideline for studies using large language models.

Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.

3d freehand ultrasound reconstruction by reference-based point cloud registration.

Purpose: This study aims to address the challenging estimation of trajectories from freehand ultrasound examinations by means of registration of automatically generated surface points. Current approaches to inter-sweep point cloud registration can be improved by incorporating heatmap predictions, but practical challenges such as label-sparsity or only partially overlapping coverage of target structures arise when applying realistic examination conditions.

Methods: We propose a pipeline comprising three stages: (1) Utilizing a Free Point Transformer for coarse pre-registration, (2) Introducing HeatReg for further refinement using support point clouds, and (3) Employing instance optimization to enhance predicted displacements.

Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.

Purpose: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.