JMIR Publications

ABSTRACT

Hemodialysis vascular access is the lifeline of patients receiving maintenance hemodialysis, yet access dysfunction remains a major cause of inadequate dialysis, repeated intervention, unplanned catheter use, hospitalization, and eventual access loss. Current vascular access care relies on physical examination, auscultation, dialysis-machine parameters, access-flow surveillance, duplex ultrasound, and angiography. Although these approaches remain clinically indispensable, they are episodic, operator-dependent, and poorly suited for continuous home-based warning. Recent advances in artificial intelligence, digital auscultation, photoplethysmography, pulse radar sensing, soft thermal sensors, wireless thrill sensors, ultrasound video analysis, and artificial intelligence of things systems have created new opportunities to transform vascular access surveillance from intermittent clinical assessment to continuous digital phenotyping. In this review, we propose a framework for artificial intelligence–enabled vascular access monitoring organized around four linked domains: pathophysiological signal generation, sensor-based digital phenotyping, artificial intelligence–based risk inference, and clinically actionable home-based warning. We summarize recent evidence from acoustic artificial intelligence, photoplethysmography-based classification, pulse radar sensing, wearable thermal and vibration sensors, artificial intelligence–assisted ultrasound, and multimodal clinical risk prediction models across the vascular access life cycle. We further discuss key barriers to clinical translation, including inconsistent definitions of access dysfunction, heterogeneous reference standards, small single-center datasets, limited external validation, device usability, alert fatigue, workflow integration, regulatory requirements, and the need for prospective outcome-based trials. Future studies should move beyond offline diagnostic accuracy toward standardized external validation, longitudinal patient-specific baselines, workflow-integrated monitoring systems, and prospective trials demonstrating reductions in thrombosis, unplanned catheter placement, delayed intervention, access loss, and patient burden. Hemodialysis vascular access is the lifeline of patients receiving maintenance hemodialysis, yet access dysfunction remains a major cause of inadequate dialysis, repeated intervention, unplanned catheter use, hospitalization, and eventual access loss. Current vascular access care relies on physical examination, auscultation, dialysis-machine parameters, access-flow surveillance, duplex ultrasound, and angiography. Although these approaches remain clinically indispensable, they are episodic, operator-dependent, and poorly suited for continuous home-based warning. Recent advances in artificial intelligence, digital auscultation, photoplethysmography, pulse radar sensing, soft thermal sensors, wireless thrill sensors, ultrasound video analysis, and artificial intelligence of things systems have created new opportunities to transform vascular access surveillance from intermittent clinical assessment to continuous digital phenotyping. In this review, we propose a framework for artificial intelligence–enabled vascular access monitoring organized around four linked domains: pathophysiological signal generation, sensor-based digital phenotyping, artificial intelligence–based risk inference, and clinically actionable home-based warning. We summarize recent evidence from acoustic artificial intelligence, photoplethysmography-based classification, pulse radar sensing, wearable thermal and vibration sensors, artificial intelligence–assisted ultrasound, and multimodal clinical risk prediction models across the vascular access life cycle. We further discuss key barriers to clinical translation, including inconsistent definitions of access dysfunction, heterogeneous reference standards, small single-center datasets, limited external validation, device usability, alert fatigue, workflow integration, regulatory requirements, and the need for prospective outcome-based trials. Future studies should move beyond offline diagnostic accuracy toward standardized external validation, longitudinal patient-specific baselines, workflow-integrated monitoring systems, and prospective trials demonstrating reductions in thrombosis, unplanned catheter placement, delayed intervention, access loss, and patient burden. Hemodialysis vascular access is the lifeline of patients receiving maintenance hemodialysis, yet access dysfunction remains a major cause of inadequate dialysis, repeated intervention, unplanned catheter use, hospitalization, and eventual access loss. Current vascular access care relies on physical examination, auscultation, dialysis-machine parameters, access-flow surveillance, duplex ultrasound, and angiography. Although these approaches remain clinically indispensable, they are episodic, operator-dependent, and poorly suited for continuous home-based warning. Recent advances in artificial intelligence, digital auscultation, photoplethysmography, pulse radar sensing, soft thermal sensors, wireless thrill sensors, ultrasound video analysis, and artificial intelligence of things systems have created new opportunities to transform vascular access surveillance from intermittent clinical assessment to continuous digital phenotyping. In this review, we propose a framework for artificial intelligence–enabled vascular access monitoring organized around four linked domains: pathophysiological signal generation, sensor-based digital phenotyping, artificial intelligence–based risk inference, and clinically actionable home-based warning. We summarize recent evidence from acoustic artificial intelligence, photoplethysmography-based classification, pulse radar sensing, wearable thermal and vibration sensors, artificial intelligence–assisted ultrasound, and multimodal clinical risk prediction models across the vascular access life cycle. We further discuss key barriers to clinical translation, including inconsistent definitions of access dysfunction, heterogeneous reference standards, small single-center datasets, limited external validation, device usability, alert fatigue, workflow integration, regulatory requirements, and the need for prospective outcome-based trials. Future studies should move beyond offline diagnostic accuracy toward standardized external validation, longitudinal patient-specific baselines, workflow-integrated monitoring systems, and prospective trials demonstrating reductions in thrombosis, unplanned catheter placement, delayed intervention, access loss, and patient burden. Hemodialysis vascular access is the lifeline of patients receiving maintenance hemodialysis, yet access dysfunction remains a major cause of inadequate dialysis, repeated intervention, unplanned catheter use, hospitalization, and eventual access loss. Current vascular access care relies on physical examination, auscultation, dialysis-machine parameters, access-flow surveillance, duplex ultrasound, and angiography. Although these approaches remain clinically indispensable, they are episodic, operator-dependent, and poorly suited for continuous home-based warning. Recent advances in artificial intelligence, digital auscultation, photoplethysmography, pulse radar sensing, soft thermal sensors, wireless thrill sensors, ultrasound video analysis, and artificial intelligence of things systems have created new opportunities to transform vascular access surveillance from intermittent clinical assessment to continuous digital phenotyping. In this review, we propose a framework for artificial intelligence–enabled vascular access monitoring organized around four linked domains: pathophysiological signal generation, sensor-based digital phenotyping, artificial intelligence–based risk inference, and clinically actionable home-based warning. We summarize recent evidence from acoustic artificial intelligence, photoplethysmography-based classification, pulse radar sensing, wearable thermal and vibration sensors, artificial intelligence–assisted ultrasound, and multimodal clinical risk prediction models across the vascular access life cycle. We further discuss key barriers to clinical translation, including inconsistent definitions of access dysfunction, heterogeneous reference standards, small single-center datasets, limited external validation, device usability, alert fatigue, workflow integration, regulatory requirements, and the need for prospective outcome-based trials. Future studies should move beyond offline diagnostic accuracy toward standardized external validation, longitudinal patient-specific baselines, workflow-integrated monitoring systems, and prospective trials demonstrating reductions in thrombosis, unplanned catheter placement, delayed intervention, access loss, and patient burden. Hemodialysis vascular access is the lifeline of patients receiving maintenance hemodialysis, yet access dysfunction remains a major cause of inadequate dialysis, repeated intervention, unplanned catheter use, hospitalization, and eventual access loss. Current vascular access care relies on physical examination, auscultation, dialysis-machine parameters, access-flow surveillance, duplex ultrasound, and angiography. Although these approaches remain clinically indispensable, they are episodic, operator-dependent, and poorly suited for continuous home-based warning. Recent advances in artificial intelligence, digital auscultation, photoplethysmography, pulse radar sensing, soft thermal sensors, wireless thrill sensors, ultrasound video analysis, and artificial intelligence of things systems have created new opportunities to transform vascular access surveillance from intermittent clinical assessment to continuous digital phenotyping. In this review, we propose a framework for artificial intelligence–enabled vascular access monitoring organized around four linked domains: pathophysiological signal generation, sensor-based digital phenotyping, artificial intelligence–based risk inference, and clinically actionable home-based warning. We summarize recent evidence from acoustic artificial intelligence, photoplethysmography-based classification, pulse radar sensing, wearable thermal and vibration sensors, artificial intelligence–assisted ultrasound, and multimodal clinical risk prediction models across the vascular access life cycle. We further discuss key barriers to clinical translation, including inconsistent definitions of access dysfunction, heterogeneous reference standards, small single-center datasets, limited external validation, device usability, alert fatigue, workflow integration, regulatory requirements, and the need for prospective outcome-based trials. Future studies should move beyond offline diagnostic accuracy toward standardized external validation, longitudinal patient-specific baselines, workflow-integrated monitoring systems, and prospective trials demonstrating reductions in thrombosis, unplanned catheter placement, delayed intervention, access loss, and patient burden. Hemodialysis vascular access is the lifeline of patients receiving maintenance hemodialysis, yet access dysfunction remains a major cause of inadequate dialysis, repeated intervention, unplanned catheter use, hospitalization, and eventual access loss. Current vascular access care relies on physical examination, auscultation, dialysis-machine parameters, access-flow surveillance, duplex ultrasound, and angiography. Although these approaches remain clinically indispensable, they are episodic, operator-dependent, and poorly suited for continuous home-based warning. Recent advances in artificial intelligence, digital auscultation, photoplethysmography, pulse radar sensing, soft thermal sensors, wireless thrill sensors, ultrasound video analysis, and artificial intelligence of things systems have created new opportunities to transform vascular access surveillance from intermittent clinical assessment to continuous digital phenotyping. In this review, we propose a framework for artificial intelligence–enabled vascular access monitoring organized around four linked domains: pathophysiological signal generation, sensor-based digital phenotyping, artificial intelligence–based risk inference, and clinically actionable home-based warning. We summarize recent evidence from acoustic artificial intelligence, photoplethysmography-based classification, pulse radar sensing, wearable thermal and vibration sensors, artificial intelligence–assisted ultrasound, and multimodal clinical risk prediction models across the vascular access life cycle. We further discuss key barriers to clinical translation, including inconsistent definitions of access dysfunction, heterogeneous reference standards, small single-center datasets, limited external validation, device usability, alert fatigue, workflow integration, regulatory requirements, and the need for prospective outcome-based trials. Future studies should move beyond offline diagnostic accuracy toward standardized external validation, longitudinal patient-specific baselines, workflow-integrated monitoring systems, and prospective trials demonstrating reductions in thrombosis, unplanned catheter placement, delayed intervention, access loss, and patient burden.