Demographic-Based Personalized Left Ventricular Hypertrophy Thresholds for Hypertrophic Cardiomyopathy Diagnosis.

Background: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death. Current diagnosis emphasizes the detection of left ventricular hypertrophy (LVH) using a fixed threshold of ≥15-mm maximum wall thickness (MWT). This study proposes a method that considers individual demographics to adjust LVH thresholds as an alternative to a 1-size-fits-all approach.

Automated Characterization of Abdominal MRI Exams Using Deep Learning.

Advances in magnetic resonance imaging (MRI) have revolutionized disease detection and treatment planning. However, as the volume and complexity of MRI data grow with increasing heterogeneity between institutions in imaging protocol, scanner technology, and data labeling, there is a need for a standardized methodology to efficiently identify, characterize, and label MRI sequences. Such a methodology is crucial for advancing research efforts that incorporate MRI data from diverse populations to develop robust machine learning models.

Denoising diffusion model for increased performance of detecting structural heart disease.

Recent advancements in generative artificial intelligence have shown promise in producing realistic images from complex data distributions. We developed a denoising diffusion probabilistic model trained on the CheXchoNet dataset, encoding the joint distribution of demographic data and echocardiogram measurements. We generated a synthetic dataset skewed towards younger patients with a higher prevalence of structural left ventricle disease.

Mapping rare protein-coding variants on multi-organ imaging traits.

Human organ structure and function are important endophenotypes for clinical outcomes. Genome-wide association studies (GWAS) have identified numerous common variants associated with phenotypes derived from magnetic resonance imaging (MRI) of the brain and body. However, the role of rare protein-coding variations affecting organ size and function is largely unknown.