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By: Dr. Kazi Kutubuddin Sayyad Liyakat and Heena T Shaikh.
1Professor & Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India.
2Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
This research presents the design and preliminary validation of an E-Kidney Filtration System (EKS), an innovative artificial kidney leveraging advanced sensor technology for precise and adaptable blood filtration. Traditional hemodialysis, while life-sustaining, is a passive process with inherent limitations in its ability to dynamically respond to individual patient needs and fluctuations in physiological parameters. EKS aims to overcome these limitations by integrating a suite of real-time biosensors and electrochemical sensors within a compact, modular filtration unit. These sensors continuously monitor crucial blood analytes, including urea, creatinine, electrolytes (sodium, potassium, calcium), glucose, and pH, alongside key hemodynamic parameters such as blood pressure and flow rate. This continuous data stream feeds into a sophisticated control algorithm that dynamically adjusts filtration parameters (e.g., dialysate composition, flow rates, ultrafiltration) in real-time. This adaptive approach promises to optimize waste product removal, maintain electrolyte balance, and minimize complications associated with conventional dialysis, ultimately leading to improved patient outcomes and a more personalized treatment experience. This abstract outlines the fundamental architecture of the EKS, highlights the critical role of sensor integration, and posits a future where artificial kidney technology offers a more intelligent and responsive solution for chronic kidney disease management. In addition to its core functionality, the EKS framework emphasizes patient-centric design, portability, and energy efficiency to support long-term use outside traditional clinical settings. The integration of machine learning techniques is also envisioned to further refine predictive accuracy and treatment customization by analyzing historical and real-time patient data. Overall, the EKS represents a forward-looking step towards intelligent renal replacement systems capable of delivering safer, more effective, and highly adaptive therapy for individuals requiring continuous kidney support.
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