MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641108169 A) filed by Pragati Engineering College on September 09, 2026, for System And Method For Personalized Hemodialysis Monitoring Using Multi-Sensor Fusion, Edge Ai, And Explainable Decision Support.
Inventors include Dr. Venkateswarlu Chitteti; Mr. Chembrolu Satish; Mr. G. Srinivasa Siva Kumar; and Mr. Krishna Chaitanya Pidugu.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention provides a system and method for personalized hemodialysis monitoring, incorporating multi-sensor fusion, edge artificial intelligence and explainable clinical decision support, to improve the safety, reliability and effectiveness of hemodialysis treatment. The invention involves a multi-source physiological and dialysis data acquisition module, an intelligent signal conditioning and data pre-processing module, a multi-sensor fusion and patient state modeling module, an edge artificial intelligence prediction engine, an explainable clinical decision support system, a smart alert and communication module, and a continuous learning and treatment analytics module, and an integrated architecture that incorporates them. Patient physiological parameters, dialysis machine operating parameters are continuously recorded and processed in the system through synchronization processing of the recorded raw data by using the signal conditioning, feature extraction and multi-sensor fusion technology, and a comprehensive real-time representation of clinical condition is outputted. A fused data analysis function is performed locally by the dialysis side's edge artificial intelligence engine to anticipate such events as intradialytic hypotension, blood clot, blood leakage, air embolism, electrolyte imbalance, vascular access failure, and dialysis machine abnormalities before they happen. The explainable clinical decision support system offers transparency in the reasoning, confidence scores, and clinical interventions for clinical decision makers, while the personalized optimization engine suggests adaptive adjustment of dialysis treatment parameters based on the risks forecasted. The communication module ensures prioritised notifications and treatment data are securely transmitted to HIS and authorised clinicians, allowing for prompt medical action and remote monitoring. The continuous learning framework is able to retain historical treatment data, and to update prediction models continuously to better approximate the accuracy of future monitoring and the accuracy of patient-specific treatment recommendations. The invention provides a single monitoring system that includes predictive intelligence, adaptive optimization, explainable decision support, and continuous learning to provide proactive hemodialysis management, lower risk of complications during treatment, improved clinical decision support and increased patient safety and treatment outcomes over the long-term.
Disclaimer: Curated by HT Syndication.
