Abstract Background Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses. Content Through an extensive examination of relevant literature, this narrative review illustrates the significance and applicability of AI models across the diverse application area of urinalysis. It encompasses automated urine test strip and sediment analysis, urinary tract infection screening, and the interpretation of complex biochemical signatures in urine, including the utilization of cutting-edge techniques such as mass spectrometry and molecular-based profiles. Summary Retrospective studies consistently demonstrate good performance of AI models in urinalysis, showcasing their potential to revolutionize clinical practice. However, to comprehensively evaluate the real clinical value and efficacy of AI models, large-scale prospective studies are essential. Such studies hold the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize medical treatment strategies. By bridging the gap between research and clinical implementation, AI can reshape the landscape of urinalysis, paving the way for more personalized and effective patient care. 

Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.

Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Machine-learning enhancement of urine dipstick tests for chronic kidney disease detection

Point-of-care and bedside tests are analytical devices suitable for a growing role in the current healthcare system and provide the opportunity to achieve an exact diagnosis by an untrained person and in various conditions and sites where it is necessary. Using a digital camera integrated into a well-accessible device like a smartphone brings a new way in which a colorimetric point-of-care diagnostic test can provide unbiased data. This review summarizes basic facts about the colorimetric point-of-care tests, principles of how to use a portable device with a camera in the assay, applications of digital cameras for the current tests, and new devices described in the recent papers. An overview of the recent literature and a discussion of recent developments and future trends are provided. 

Current Trends in Digital Camera-Based Bioassays for Point-of-care Tests.

Urine analysis stands as a critical diagnostic tool, offering insights into health and disease. However, current techniques demand sophisticated equipment or significant sample processing for urine examination, reducing their suitability for regular point‐of‐care assessments. This study introduces a novel multi‐component sensing platform to address these constraints. The proposed sensor array can detect sodium (Na+), potassium (K+), ammonium (NH4+), calcium (Ca++), chloride (Cl−), and pH levels, thus, enabling real‐time urine analysis. This sensing platform utilizes an extended gate (EG)‐field effect transistor (FET) design employing EG electrodes made of LASER engraved graphene on flexible Kapton substrates. These experimental findings from individual sensors demonstrate consistent linear responses to ion levels, discrimination of specific ions among interferences, and operational stability over time. Additionally, the six‐channel sensor array exhibits notable sensitivity and selectivity in a urine environment, effectively discerning various ions and pH, illustrating its efficacy for urine analysis and validating its potential for reliable point‐of‐care diagnostics.

Extended Gate Transistor‐based Multi‐biomarker Sensing Platform for Real‐time Urine Analysis

Objective: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. Methods and procedures: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). Results: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. Conclusion: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. Clinical impact: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieved.

Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care

Cardiac parameters are important indicators for health assessment. Radar-based monitoring with microwave interferometric sensors (MIS) is a promising alternative to conventional measurement methods, as it enables completely contactless cardiac function diagnostics. In this study, we evaluated the effects of sensor positioning and movement on the accuracy of radar-based heart rate measurements with MIS. For this purpose, we recruited 29 participants which performed semi-standardized movements, a reading task, and a standardized laboratory stress test in a seated position. Furthermore, we compared three different sensor positions (dorsal, upper pectoral, and lower pectoral) to a gold standard 1-channel wearable ECG sensor node. The dorsal positioning achieved the best results with a mean error (ME) of 0.2±5.4 bpm and a mean absolute error (MAE) of 3.5±4.1 bpm for no movement and also turned out to be most robust against motion artifacts with an overall ME of 0.1±14.1 bpm (MAE: 9.5±10.4 bpm). No correlation was found between movement intensity and measurement error. Instead, movement type and direction were identified as primary impact factors. This study provides a valuable contribution towards the applicability of radar-based vital sign monitoring with MIS in real-world scenarios. However, further research is needed to sufficiently prevent and compensate for movement artifacts.

Influence of Sensor Position and Body Movements on Radar-Based Heart Rate Monitoring

Preterm births account for more than 10 % of all newborns. An adverse fetal presentation is a risk factor for intrapartum and neonatal mortality. To date, no technology enables a longitudinal, ubiquitous, and unobtrusive monitoring of fetal presentation. This study presents a first approach to fetal orientation detection based on non-invasive fetal electrocardiography (NI-fECG) using the non-invasive multi-modal foetal ECG-Doppler data set for antenatal cardiology research. The data set contains 60 recordings from 39 pregnant women (21–27 weeks), including NI-fECG and ultrasound position ground truth. We evaluated both handcrafted and generic features for five different classifiers (k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier, and Multilayer Perceptron) using cross-validation on subject splits on a cleaned subset. Best results for the distinction between vertex (head down) and breech (head up) were achieved using an AdaBoost classifier with a balanced accuracy of 86.5 ± 15.0 %. With this work, we take a first step towards longitudinal fetal presentation monitoring, which contributes to a better understanding of reduced fetal movements and extends the potential applications of NI-fECG in prenatal care. In future work, we will expand our classification system to detect more detailed fetal presentations using a newly created data set.

Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG

The Stroop test is one of the most widely used protocols to induce cognitive stress and reliably activates the sympathetic nervous system (SNS). However, it only moderately activates the hypothalamic-pituitary-adrenal (HPA) axis, the stress axis responsible for cortisol secretion. In other well-known stress protocols, such as the cold pressor test, adding social-evaluative elements to the regular procedure has proven to cause increased HPA axis activation. For this reason, we introduce the “Stroop Competition”, a novel stress protocol based on the established Stroop test that adds social-evaluative feedback by conducting the test against a fake opponent with subsequent performance evaluation. We investigated the stress response of 22 participants performing either the “Stroop Competition” (Competition group) or the regular Stroop test (Control group) three consecutive times. Stress responses were assessed using ECG recordings to extract heart rate (HR) and heart rate variability (HRV) and saliva samples to extract salivary alpha-amylase (sAA) and cortisol. In the Competition group, participants experienced higher SNS activation indicated by significantly higher HR and lower HRV levels as well as higher sAA response to the stressor compared to the Control group. Additionally, overall cortisol levels were significantly higher in the Competition group supporting higher HPA axis activity. The findings of our pilot study confirm our hypothesis that adding social-evaluative elements to the Stroop test causes a more effective activation of both the SNS and HPA axis. We are convinced that our novel “Stroop Competition” protocol will provide a valuable addition to the already existing stress protocols in biopsychological research.

The Stroop Competition: A Social-Evaluative Stroop Test for Acute Stress Induction

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Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care

Influence of Sensor Position and Body Movements on Radar-Based Heart Rate Monitoring

Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG

The Stroop Competition: A Social-Evaluative Stroop Test for Acute Stress Induction