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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1. Mulyanti B, Nugroho HS, Wulandari C, Rahmawati Y, Hasanah L, Hamidah I, et al. SPR-based sensor for the early detection or monitoring of kidney problems. Int J Biomater. 2022;2022(1):9135172.
2. Sansone F, Tonacci A. Non-invasive diagnostic approaches for kidney disease: The role of electronic nose systems. Sensors (Basel). 2024 Oct 8;24(19):6475.
3. Murad O, Orjuela DF, Goldman A, Stern T, Vernon P. Improving awareness of kidney function through electronic urine output monitoring: A comparative study. BMC Nephrol. 2022 Dec 26;23(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36572867/.
4. Liyakat KS. e-Skin applications in healthcare and robotics: A study. J Adv Robot. 2025;12(1):13–21.
5. Liyakat KK. AI in e-skin: A study. Int J Image Process Pattern Recognit. 2025;11(1):8–16.
6. Mulla NR, Liyakat KS. Pipeline pressure and flow rate monitoring using IoT sensors and ML algorithms to detect leakages. Int J Artif Intell Mech Eng. 2025 Jun;1(1):20–30.
7. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N, et al. HEECCNB: An efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In: 2023 Seventh International Conference on Image Information Processing (ICIIP). IEEE; 2023 Nov 22. p. 407–10.

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.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Mulyanti B, Nugroho HS, Wulandari C, Rahmawati Y, Hasanah L, Hamidah I, et al. SPR-based sensor for the early detection or monitoring of kidney problems. Int J Biomater. 2022;2022(1):9135172.
2. Sansone F, Tonacci A. Non-invasive diagnostic approaches for kidney disease: The role of electronic nose systems. Sensors (Basel). 2024 Oct 8;24(19):6475.
3. Murad O, Orjuela DF, Goldman A, Stern T, Vernon P. Improving awareness of kidney function through electronic urine output monitoring: A comparative study. BMC Nephrol. 2022 Dec 26;23(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36572867/.
4. Liyakat KS. e-Skin applications in healthcare and robotics: A study. J Adv Robot. 2025;12(1):13–21.
5. Liyakat KK. AI in e-skin: A study. Int J Image Process Pattern Recognit. 2025;11(1):8–16.
6. Mulla NR, Liyakat KS. Pipeline pressure and flow rate monitoring using IoT sensors and ML algorithms to detect leakages. Int J Artif Intell Mech Eng. 2025 Jun;1(1):20–30.
7. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N, et al. HEECCNB: An efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In: 2023 Seventh International Conference on Image Information Processing (ICIIP). IEEE; 2023 Nov 22. p. 407–10.