Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare
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 a...
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| Format: | Article |
| Language: | English |
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Wiley
2025-07-01
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| Series: | Bioengineering & Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/btm2.70002 |
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| _version_ | 1849468974078623744 |
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| author | Kaidong Wang Bing Tan Xinfei Wang Shicheng Qiu Qiuping Zhang Shaolei Wang Ying‐Tzu Yen Nan Jing Changming Liu Xuxu Chen Shichang Liu Yan Yu |
| author_facet | Kaidong Wang Bing Tan Xinfei Wang Shicheng Qiu Qiuping Zhang Shaolei Wang Ying‐Tzu Yen Nan Jing Changming Liu Xuxu Chen Shichang Liu Yan Yu |
| author_sort | Kaidong Wang |
| collection | DOAJ |
| description | 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 healthcare systems face challenges such as delayed diagnoses, inadequate care, and rising medical costs. The advancement of machine learning techniques has sparked considerable interest in medical research and engineering, offering ways to enhance diagnostic accuracy and relevance. Improved data interoperability and seamless connectivity could enable real‐time, continuous monitoring of cardiovascular health. Recent breakthroughs in computing power and algorithmic design, particularly deep learning frameworks that emulate neural processes, have revolutionized POC devices for CVDs, enabling more frequent detection of abnormalities and automated, expert‐level diagnosis. However, challenges such as data privacy concerns and biases in dataset representation continue to hinder clinical integration. Despite these barriers, the translational potential of machine learning‐assisted POC devices presents significant opportunities for advancement in CVDs healthcare. |
| format | Article |
| id | doaj-art-abbe4b4bb0b24b479154ec210f9acae6 |
| institution | Kabale University |
| issn | 2380-6761 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Bioengineering & Translational Medicine |
| spelling | doaj-art-abbe4b4bb0b24b479154ec210f9acae62025-08-20T03:25:40ZengWileyBioengineering & Translational Medicine2380-67612025-07-01104n/an/a10.1002/btm2.70002Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcareKaidong Wang0Bing Tan1Xinfei Wang2Shicheng Qiu3Qiuping Zhang4Shaolei Wang5Ying‐Tzu Yen6Nan Jing7Changming Liu8Xuxu Chen9Shichang Liu10Yan Yu11Division of Cardiology, Department of Medicine, David Geffen School of Medicine University of California Los Angeles Los Angeles California USADepartment of Spine Surgery, The Third Hospital of Mianyang Sichuan Mental Health Center Mianyang ChinaDepartment of Bioengineering, Henry Samueli School of Engineering and Applied Science University of California Los Angeles Los Angeles California USADepartment of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong ChinaPostdoctoral Research Workstation Chongqing Orthopedic Hospital of Traditional Chinese Medicine Chongqing ChinaDepartment of Bioengineering, Henry Samueli School of Engineering and Applied Science University of California Los Angeles Los Angeles California USADepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine University of California Los Angeles Los Angeles California USADepartment of Nutrition University of California Davis Davis California USADepartment of Computer Engineering, School of Engineering and Applied Science University of Virginia Charlottesville Virginia USAHonghui Hospital Xi'an Jiaotong University Xi'an ChinaHonghui Hospital Xi'an Jiaotong University Xi'an ChinaHonghui Hospital Xi'an Jiaotong University Xi'an ChinaAbstract 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 healthcare systems face challenges such as delayed diagnoses, inadequate care, and rising medical costs. The advancement of machine learning techniques has sparked considerable interest in medical research and engineering, offering ways to enhance diagnostic accuracy and relevance. Improved data interoperability and seamless connectivity could enable real‐time, continuous monitoring of cardiovascular health. Recent breakthroughs in computing power and algorithmic design, particularly deep learning frameworks that emulate neural processes, have revolutionized POC devices for CVDs, enabling more frequent detection of abnormalities and automated, expert‐level diagnosis. However, challenges such as data privacy concerns and biases in dataset representation continue to hinder clinical integration. Despite these barriers, the translational potential of machine learning‐assisted POC devices presents significant opportunities for advancement in CVDs healthcare.https://doi.org/10.1002/btm2.70002cardiovascular diseases (CVDs)continuous health monitoringdeep learningmachine learningpoint‐of‐care (POC) diagnostics |
| spellingShingle | Kaidong Wang Bing Tan Xinfei Wang Shicheng Qiu Qiuping Zhang Shaolei Wang Ying‐Tzu Yen Nan Jing Changming Liu Xuxu Chen Shichang Liu Yan Yu Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare Bioengineering & Translational Medicine cardiovascular diseases (CVDs) continuous health monitoring deep learning machine learning point‐of‐care (POC) diagnostics |
| title | Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare |
| title_full | Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare |
| title_fullStr | Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare |
| title_full_unstemmed | Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare |
| title_short | Machine learning‐assisted point‐of‐care diagnostics for cardiovascular healthcare |
| title_sort | machine learning assisted point of care diagnostics for cardiovascular healthcare |
| topic | cardiovascular diseases (CVDs) continuous health monitoring deep learning machine learning point‐of‐care (POC) diagnostics |
| url | https://doi.org/10.1002/btm2.70002 |
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