Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare

Bioengineering & Translational Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Abstract Cardiovascular diseases (CVDs) continue to drive global mortality rates, underscoring an urgent need for advancements in healthcare solutions. The development of point‐of‐care (POC) devices that provide rapid diagnostic services near patients has garnered substantial attention, especially as traditional systems face challenges such delayed diagnoses, inadequate care, and rising medical costs. advancement machine learning techniques sparked considerable interest research engineering, offering ways enhance accuracy relevance. Improved data interoperability seamless connectivity could enable real‐time, continuous monitoring cardiovascular health. Recent breakthroughs computing power algorithmic design, particularly deep frameworks emulate neural processes, have revolutionized POC CVDs, enabling more frequent detection abnormalities automated, expert‐level diagnosis. However, privacy concerns biases dataset representation hinder clinical integration. Despite these barriers, the translational potential learning‐assisted presents significant opportunities CVDs healthcare.

Language: Английский

Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110153 - 110153

Published: April 18, 2025

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditional diagnostic methods, dependent on radiological interpretation, have low sensitivity, especially, in early stages disease, and present variability between evaluators. This study explores efficacy deep learning techniques automating screening staging silicosis using chest X-ray images. Utilizing comprehensive dataset, obtained from medical records cohort workers exposed artificial quartz conglomerates, we implemented preprocessing stage for rib-cage segmentation, followed classification state-of-the-art models. The segmentation model exhibited precision, ensuring accurate identification thoracic structures. In phase, our models achieved near-perfect accuracy, ROC AUC values reaching 1.0, effectively distinguishing healthy individuals those silicosis. demonstrated remarkable precision disease. Nevertheless, differentiating simple progressive massive fibrosis, evolved complicated form presented certain difficulties, during transitional period, when assessment can significantly subjective. Notwithstanding these an accuracy around 81% scores nearing 0.93. highlights potential generate clinical decision support tools increase effectiveness diagnosis silicosis, whose detection would allow patient moved away all sources exposure, therefore constituting substantial advancement diagnostics.

Language: Английский

TMEM175 activity in BK-deficient macrophages maintains lysosomal function and mediates silica-induced inflammatory response in macrophages

Inhalation Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10

Published: May 22, 2025

Objective: Lysosomal ion channel function in macrophages contributes to the development of silica-induced inflammation. Recent studies have shown that blocking K+ entry into lysosome via BK reduces damage and inflammation macrophages. This study aims explore mechanisms particle-induced BK-/- Methods: Bone marrow derived (BMdM) from C57BL/6 wildtype (WT) mice were exposed vitro silica IL-1β release cell death assessed. The effect on lysosomal pH, proteolytic activity, cholesterol accumulation was evaluated. Results: BMdM failed demonstrate a reduction or following exposure. had comparable WT suggesting compensatory mechanism maintaining function. demonstrated an upregulation second potassium channel, TMEM175. Inhibition TMEM175 activity caused increase pH reduced both BMdM. Conclusion: did not exhibit same phenotype seen with pharmaceutical abrogation showed no differences response Upregulation appears prevent changes accumulation. Inhibiting resulted inflammation, is dependent single but rather elevate pH.

Language: Английский