Non-invasive Self-Care Anemia Detection during Pregnancy Using a Smartphone Camera

Anggraeni, Mekar Dwi; Fatoni, Amin

doi:10.1088/1757-899x/172/1/012030

Cited by 23 publications

(7 citation statements)

References 13 publications

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“…Anggraeni and Fatoni [31] introduced a non-invasive anemia detection system based on a digital image of palpebral conjunctiva captured by a smartphone camera. Digital image of the inferior palpebral conjunctiva was captured with an Asus Zenphone-2 Laser smartphone, ambient lighting without flash, and then color-corrected with white paper.…”

Section: Related Workmentioning

confidence: 99%

A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels From Fingertip Video Using DNN Based Models

et al. 2021

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Blood components such as hemoglobin, glucose, creatinine measuring are essential for monitoring one's health condition. The current blood component measurement approaches still depend on invasive techniques that are painful, and uncomfortable for the patients. To facilitate measurement at home, we proposed a novel non-invasive technique to measure blood hemoglobin, glucose, and creatinine level based on PPG signal using Deep Neural Networks (DNN). Fingertip videos from 93 subjects have been collected using a smartphone. The PPG signal is generated from each video, and 46 characteristic features are then extracted from the PPG signal, its derivatives (1 st and 2 nd ) and from Fourier analysis. Additionally, age and gender are also included to feature because of the significant effects on hemoglobin, glucose, and creatinine. A correlation-based feature selection (CFS) using genetic algorithms (GA) has been used to select the optimal features to avoid redundancy and over-fitting. Finally, DNN based models have been developed to estimate the blood Hemoglobin (Hb), Glucose (Gl), and Creatinine (Cr) levels from the selected features. The approach provides the best-estimated accuracy of R 2 = 0.922 for Hb, R 2 = 0.902 for Gl, and R 2 = 0.969 for Cr. Experimental aftermaths show that the proposed method is a suitable technique to be used clinically to measure human blood component levels without taking blood samples. This paper also reveals that smartphone-based PPG signal has a great potential to measure the different blood components.

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Section: Related Workmentioning

confidence: 99%

A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels From Fingertip Video Using DNN Based Models

et al. 2021

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“…The most frequently used smartphones are developed by Apple, Samsung, Motorola, Google, HTC, Sony, and Asus, where the camera sensor is used to capture videos or images. The accuracy level was deemed to be reliable in each of the studies listed in Multimedia Appendix 1 [50,51,54,55,[57][58][59][60][61][62][63][64]. Data commonly captured by a smartphone were obtained from two main body sites: the fingertip and eyelid.…”

Section: Smartphone As a Poc Toolmentioning

confidence: 99%

“…Digital images of the palpebral conjunctiva can provide information to measure the level of Hb in a noninvasive manner [80]. Anggraeni et al [57] built a regression model using the digital images of 20 participants' eyelids along with white paper images captured at the same time by Asus ZenFone 2, and estimated the Hb concentration, which correlated highly with clinically measured Hb levels (r=0.92). Among the three color pixels of a palpebral conjunctiva image, the red color intensity showed better performance than the green and blue pixel intensities in this study.…”

Section: Palpebral Conjunctivamentioning

confidence: 99%

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

Hasan¹,

Aziz²,

Zarif³

et al. 2021

JMIR Mhealth Uhealth

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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.

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“…In [42], a study was presented that has the ambitious goal of providing health resources to developing countries, where it is difficult to diagnose anemia with traditional methods; in [43] the estimate of the concentration of hemoglobin in the blood is discussed to limit the problem of postpartum hemorrhage caused by anemia, which occurs mostly in Africa and Asia. Both studies are based on the analysis of the color of the conjunctiva, in the second case taking the photos in ambient lighting, without flash, with a white paper positioned next to the eye to standardize white.…”

Section: Estimation Of Anemia By Analyzing the Eyelid Conjunctivamentioning

confidence: 99%

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

et al. 2020

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Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined.

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Non-invasive Self-Care Anemia Detection during Pregnancy Using a Smartphone Camera

Cited by 23 publications

References 13 publications

A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels From Fingertip Video Using DNN Based Models

A Novel Technique for Non-Invasive Measurement of Human Blood Component Levels From Fingertip Video Using DNN Based Models

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

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

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