Whole blood glucose analysis based on smartphone camera module

Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations

BACKGROUND

There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device.

OBJECTIVE

In this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone.

METHODS

We explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner.

RESULTS

The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot.

CONCLUSIONS

We addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone's camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal's features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.

Human Body-Related Disease Diagnosis Systems Using CMOS Image Sensors: A Systematic Review

According to the Center for Disease Control and Prevention (CDC), the average human life expectancy is 78.8 years. Specifically, 3.2 million deaths are reported yearly due to heart disease, cancer, Alzheimer’s disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS image sensor (CIS) became a prominent technology in assisting the monitoring and clinical diagnosis devices to treat diseases in the medical domain. To address the significance of CMOS image ‘sensors’ usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones, skin, blood, and bacteria cells causing diseases. This literature survey’s main objective is to evaluate the ‘systems’ capabilities and highlight the most potent ones with advantages, disadvantages, and accuracy, that are used in disease diagnosis. This systematic review used PRISMA workflow for study selection methodology, and the parameter-based evaluation is performed on disease diagnosis systems related to the human body’s organs. The corresponding CIS models used in systems are mapped organ-wise, and the data collected over the last decade are tabulated.

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.

Design, fabrication, and feasibility analysis of a colorimetric detection system with a smartphone for self-monitoring blood glucose

Maintaining appropriate insulin levels is very important for diabetes patients. Effective monitoring of blood glucose can aid in maintaining the body's insulin level, and thus reduce disease severities, secondary complications, and related mortalities. However, existing blood glucose measurement devices are inconvenient to carry and involve complex procedures, reducing the willingness of diabetes patients to regularly measure blood glucose. We aim to provide a rapid, convenient, and portable meter for diabetes patients. We introduce an integrated blood glucose detection device (IBGDD) that has no electronic component and uses the optical sensing module of a smartphone to inspect colorimetric blood strips. To demonstrate accuracy conformance of the developed device to the ISO 15197:2013 standard for blood glucose measurement, 20 diabetes mellitus patients used the IBGDD with smartphones to measure their blood glucose level. The measurement results revealed an accuracy of 100%, completely satisfying the requirements of the ISO 15197:2013 standard. Overall, our specially designed IBGDD with a smartphone could achieve high accuracy and convenient usage for the measurement of blood glucose concentration. Furthermore, the device is highly portable and simple to operate. This contributes toward achieving self-monitoring of blood glucose by diabetes patients and improved mobile health in the future.

Smartphone-based clinical diagnostics: towards democratization of evidence-based health care

Recent advancements in bioanalytical techniques have led to the development of novel and robust diagnostic approaches that hold promise for providing optimal patient treatment, guiding prevention programs and widening the scope of personalized medicine. However, these advanced diagnostic techniques are still complex, expensive and limited to centralized healthcare facilities or research laboratories. This significantly hinders the use of evidence-based diagnostics for resource-limited settings and the primary care, thus creating a gap between healthcare providers and patients, leaving these populations without access to precision and quality medicine. Smartphone-based imaging and sensing platforms are emerging as promising alternatives for bridging this gap and decentralizing diagnostic tests offering practical features such as portability, cost-effectiveness and connectivity. Moreover, towards simplifying and automating bioanalytical techniques, biosensors and lab-on-a-chip technologies have become essential to interface and integrate these assays, bringing together the high precision and sensitivity of diagnostic techniques with the connectivity and computational power of smartphones. Here, we provide an overview of the emerging field of clinical smartphone diagnostics and its contributing technologies, as well as their wide range of areas of application, which span from haematology to digital pathology and rapid infectious disease diagnostics.

Smartphone coupled handheld array reader for real-time toxic gas detection

Smartphones and related accessories are rapidly expanding their applications in various fields. Herein we developed a smartphone coupled handheld array reader with the integration of complementary metal oxide (CMOS) image sensor for detecting various toxic gases by colorimetric monitoring approach. In this study, toxic gases such as hydrogen fluoride (HF), chlorine (Cl₂), ammonia (NH₃), and formaldehyde (CH₂O) were detected using titanium nanoparticles (TiO₂ NPs) blended poly (vinyl alcohol) (PVA) hydrogel test strips, which were patterned with chemically responsive dyes. The dye colors changed based on acid-base reactions, and the colorimetric reader monitored the array strips and mapped them in the form of chrominance data. The observed signals transferred to a smartphone that displays the detected toxic gases and their exposure levels with the aid of the smartphone app "Toxic Gas Detection". The colorimetric array reader was precisely constructed with an auto-calibration system to minimize potential errors. Various concentration of toxic gases (0.5-10 ppm) were analyzed and achieved the detection limit of 1 ppm for each gas. Also, various analytical performance including sensitivity, stability, selectivity, and reproducibility analysis proved the reliability and accuracy of the detection system. Additionally, the smartphone app can be installed on any type of tablet, laptop, or other portable device. This study proved that smartphone coupled colorimetric array reader is a suitable system for detecting various toxic gases, chemicals and biochemical analysis with real-time monitoring approach.