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Digital stethoscopes provide a possible cost-effective solution to accessible screening of cardiovascular diseases in low-to-middle-income countries. Heart sound segmentation is an essential step in computer-aided screening. This paper examines the underlying adult-based assumptions and presumptions of state-of-the-art heart sound segmentation algorithms, and then proposes an age-based heart sound segmentation to provide higher accuracy performance for pediatric phonocardiograms. CirCor DigiScope Dataset was utilised, containing 3163 heart sound recordings from 942 pediatric patients ranging from neonate to young adult age groups. Compared to existing adult-based assumptions and presumptions, 5.4%-80% of patients were outside the expected heart rate range, and expected S1 and S2 duration distributions showed an overlap of 53.5%-96.3% and 76.3%-91.3% respectively, with younger age groups showing the largest differences in most cases. Additionally, the assumption of the linear relationship between mean systole duration with total systolic interval was weaker for younger age groups. Utilising a pediatric-based denoising algorithm and appropriate modification of major parameters and relationships within an existing heart sound segmentation algorithm, it was shown that S1 and S2 heart segmentation F1-Scores improved in all age groups from 69.0%-93.7% to 91.4%-99.8%.Clinical relevance- Accurate heart sound segmentation is a necessary preliminary step for automated clinical decision assistance tools for cardiovascular disease screening.
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http://dx.doi.org/10.1109/EMBC53108.2024.10781977 | DOI Listing |
Diabetes Care
September 2025
Department of Epidemiology and Welch Center for Prevention, Epidemiologic, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Medicine (Baltimore)
September 2025
Al Mouwasat University Hospital, Damascus University, Damascus, Syria.
Rationale: Systemic sclerosis (SS) is an immune-mediated connective disease characterized by skin fibrosis, microvascular damage, and multisystem manifestations. One of the most important processes in connective tissue disorders is vasculitis. The clinical findings can differ when the disease is presented with an antineutrophil cytoplasmic antibody.
View Article and Find Full Text PDFBiomed Eng Lett
September 2025
Department of Anesthesiology and Pain Medicine, Brain Korea 21 Project, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpagu, 05505 Seoul, Republic of Korea.
Heart sounds provide essential information about cardiac function; however, their clinical meaning and potential for minimally invasive hemodynamic monitoring in real world clinical settings remain underexplored. This study assessed relationships between heart sound indices and hemodynamic parameters during liver transplant surgery. Data from 80 liver transplant recipients were analyzed across five procedural phases (approximately 1,680k cardiac beats).
View Article and Find Full Text PDFMultidiscip Respir Med
September 2025
Department of Chest Diseases, Faculty of Medicine, Al-Azhar University, Cairo, Egypt.
Background: Chest examination alone may be insufficient to declare cardiorespiratory diseases specially in its early stages and/or silent forms, also it is impractical for the CXR and cardiac consultation to be requested for every patient in the outpatient clinic, therefore involving the chest US and FoCUS (Focused Cardiac Ultra Sound) examination in the bedside practice of outpatient chest clinic may influence the clinical diagnosis and management plan.
Objective: To determine how the bedside thoracic US including FoCUS can alter the clinical diagnosis in patients who are clinically diagnosed as acute bronchitis in the outpatient chest clinic.
Subjects And Methods: This study was conducted at Chest outpatient clinic, Al-Azhar University in the period between January 2024 to March 2025.
Background: Primary care providers (PCPs) are not successful in accurately identifying Still's murmur with no available clinical tools to aid in the process. Existing deep learning (DL) methods primaryly focused on adult pathological murmurs or murmur detection, lacking dedicated approaches for Still's murmur. Furthermore, the absence of a specialized database hampers the development and validation of AI models for pediatric populations.
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