Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare DOI Creative Commons
Kaidong Wang, Bing Tan, Xinfei Wang

et al.

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

Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare DOI Creative Commons
Kaidong Wang, Bing Tan, Xinfei Wang

et al.

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

Citations

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