Research Paper.pdf
Creators: name: Nashat, Mina
Publisher: name: figshare
Year: 2023
Subjects: Bioinformatic methods development
Bioinformatics and computational biology not elsewhere classified
Biological network analysis

description: 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 choiceto buildthis solution. Consideration of a smartphonebased 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 challengeswith respectto data collectionmethods, sensor selection, signal analysisprocesses, andmachinelearning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device. The potential for visually detectable clinical signs of anemia and their correlation with the severity of the pathology has supported research on non-invasive prevention methods. Physical examination for a suspected diagnosis of anemia is a practice performed by a specialist to evaluate the pallor of the exposed tissues. The aim of the research presented herein is to quantify and minimize the subjective nature of the examination of the palpebral conjunctiva, suggesting a method of diagnostic support and autonomous monitoring. Here we describe the methodology and system for extracting key data from the digital image of the conjunctiva, which is also based on analysis of the dominant color classes. Effective features have been used herein to establish the inclusion of each image in a diagnosis probability class for anemia. The images of the conjunctiva were taken using a new low cost and easy to use device, designed to optimize the properties of independence from ambient light. The performance of the system was tested either by extracting manually the palpebral conjunctiva from images or by extracting them in a semi-automatic way based on the SLIC Superpixel algorithm. 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. 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, bio signal 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. 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 BlandAltman plot. 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. i II. Introduction Low Hemoglobin (Hb) level in the blood leads to many chronic health problems, including heart attack, stroke, and pregnancy complications [1]. When an adequate Hb blood level (men13 g/dL, women12 g/dL) is not maintained, the disorder complicates the function of the major organs (eg, kidney, brain, and heart) that require oxygen [2]. Anemia, a common Hb disorder, may be caused by blood loss , decreased red blood cell (RBC) production associated with iron and other nutritional deficiencies, and increased RBC destruction[3,4]. Approximately 5.6% of the US population is anemic and 1.5% of the population has moderate to severe anemia[5] . Sickle cell diseases (SCD) cost more than US $1.5 billion annually in the United States [6]. Globally, blood disorders and associated complications affect more than 5 million people. In Africa, approximately 250,000 babies are born with SCD every year[7] and 1.62 billion people are affected by Hb related abnormalities worldwide . A reliable, affordable, and user-typically involves the cyanmethemoglobin method, which is considered to be reliable. Clinical assessment of Hb typically involves the cyan-methemoglobin method, which is considered to be reliable. However, this invasive process has several limitations, including that the diagnostic devices are not portable, results are not immediately available, and the entire process is expensive, complete blood count (CBC) test in Bari, Puglia, Italy, with approximately 1,000,000 inhabitants, to be US $3.14, resulting in a total cost of US $560,000 in 2018. <br>