Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

Purpose: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to enhance AI model robustness and accuracy through the integration of domain knowledge into the synthetic image generation process.

Methods: The study employed two ultrasound datasets: a base dataset (Db) of knee recess distension images from non-hemophiliac patients and a target dataset (Dt) of hemarthrosis images from hemophiliac patients.

Utilizing artificial intelligence for the detection of hemarthrosis in hemophilia using point-of-care ultrasonography.

Background: Recurrent hemarthrosis and resultant hemophilic arthropathy are significant causes of morbidity in persons with hemophilia, despite the marked evolution of hemophilia care. Prevention, timely diagnosis, and treatment of bleeding episodes are key. However, a physical examination or a patient's assessment of musculoskeletal pain may not accurately identify a joint bleed.

[Tissue specificity of antioxidant system functioning and lipid peroxidation in different age groups of Amur carp].

Key features of tissue enzymes functioning in antioxidant system (AOS) in sexually mature and immature individuals of Amur carp were studied. The activity of antioxidant enzymes was highest in the myocardium and subjected to age-related changes. It was concluded that changes in the functioning of AOS and intensity of lipid peroxidation processes are characterized by organ-tissue metabolic features and age peculiarities of metabolism that is most expressed in the myocardium.