Assistive technology using integrated flexible sensor and virtual alarm unit for blood leakage detection during dialysis therapy

Flexible Ring Sensor Array and Machine Learning Model for the Early Blood Leakage Detection during Dialysis

Severe blood leakage resulting from the detachment of dialysis tubing is often difficult to detect by nurses in busy clinics. This paper presents a flexible blood leakage detection system featuring a ring-light sensor array with an operating wavelength of 500–700 nm, which is held in place by the gauze covering the dialysis puncture site. A ring-light sensor is connected to a bidirectional hetero-associative memory network, which interprets detected changes in signal strength, the output signal of which is transmitted via WiFi to a server at the nursing station where a machine learning algorithm determines whether blood leakage has occurred. The compact design of this early warning system greatly enhances the comfort and mobility of patients undergoing dialysis. The efficacy of the proposed system was demonstrated in experiments involving artificial blood.

Opportunities in the cloud or pie in the sky? Current status and future perspectives of telemedicine in nephrology

The use of telehealth to support, enhance or substitute traditional methods of delivering healthcare is becoming increasingly common in many specialties, such as stroke care, radiology and oncology. There is reason to believe that this approach remains underutilized within nephrology, which is somewhat surprising given the fact that nephrologists have always driven technological change in developing dialysis technology. Despite the obvious benefits that telehealth may provide, robust evidence remains lacking and many of the studies are anecdotal, limited to small numbers or without conclusive proof of benefit. More worryingly, quite a few studies report unexpected obstacles, pitfalls or patient dissatisfaction. However, with increasing global threats such as climate change and infectious disease, a change in approach to delivery of healthcare is needed. The current pandemic with coronavirus disease 2019 (COVID-19) has prompted the renal community to embrace telehealth to an unprecedented extent and at speed. In that sense the pandemic has already served as a disruptor, changed clinical practice and shown immense transformative potential. Here, we provide an update on current evidence and use of telehealth within various areas of nephrology globally, including the fields of dialysis, inpatient care, virtual consultation and patient empowerment. We also provide a brief primer on the use of artificial intelligence in this context and speculate about future implications. We also highlight legal aspects and pitfalls and discuss the 'digital divide' as a key concept that healthcare providers need to be mindful of when providing telemedicine-based approaches. Finally, we briefly discuss the immediate use of telenephrology at the onset of the COVID-19 pandemic. We hope to provide clinical nephrologists with an overview of what is currently available, as well as a glimpse into what may be expected in the future.

Blood leakage detection during dialysis therapy based on fog computing with array photocell sensors and heteroassociative memory model

Blood leakage and blood loss are serious life-threatening complications occurring during dialysis therapy. These events have been of concerns to both healthcare givers and patients. More than 40% of adult blood volume can be lost in just a few minutes, resulting in morbidities and mortality. The authors intend to propose the design of a warning tool for the detection of blood leakage/blood loss during dialysis therapy based on fog computing with an array of photocell sensors and heteroassociative memory (HAM) model. Photocell sensors are arranged in an array on a flexible substrate to detect blood leakage via the resistance changes with illumination in the visible spectrum of 500-700 nm. The HAM model is implemented to design a virtual alarm unit using electricity changes in an embedded system. The proposed warning tool can indicate the risk level in both end-sensing units and remote monitor devices via a wireless network and fog/cloud computing. The animal experimental results (pig blood) will demonstrate the feasibility.

Fabrication of a Textile-Based Wearable Blood Leakage Sensor Using Screen-Offset Printing

We fabricate a wearable blood leakage sensor on a cotton textile by combining two newly developed techniques. First, we employ a screen-offset printing technique that avoids blurring, short circuiting between adjacent conductive patterns, and electrode fracturing to form an interdigitated electrode structure for the sensor on a textile. Furthermore, we develop a scheme to distinguish blood from other substances by utilizing the specific dielectric dispersion of blood observed in the sub-megahertz frequency range. The sensor can detect blood volumes as low as 15 μL, which is significantly lower than those of commercially available products (which can detect approximately 1 mL of blood) and comparable to a recently reported value of approximately 10 μL. In this study, we merge two technologies to develop a more practical skin-friendly sensor that can be applied for safe, stress-free blood leakage monitoring during hemodialysis.