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.

Two billion people are affected by hemoglobin (Hgb) related diseases. Usual clinical assessments of Hgb are conducted by analyzing venipuncture-obtained blood samples in laboratories. A non-invasive, cheap, point-of-care and accurate Hgb test is needed everywhere. Our group has developed a noninvasive Hgb measurement system using 10-second Smartphone videos of the index fingertips. Custom hardware sets were used to illuminate the fingers. We tested four lighting conditions with wavelengths in the near-infrared spectrum suggested by the absorption properties of two primary components of bloodoxygenated Hgb and plasma. We found a strong linear correlation between our measured and laboratory-measured Hgb levels in 167 patients with a mean absolute percentage error (MAPE) of 5%. In our initial analysis, critical tasks were performed manually. Now, using the same data, we have automated or modified all the steps. For all, male, and female subjects we found a MAPE of 6.43%, 5.34%, and 4.85 and mean squared error (MSE) of 0.84, 0.5, and 0.49 respectively. The new analyses however, have suggested inexplicable inconsistencies in our results, which we attribute to laboratory measurement errors reflected in a non-normative distribution of Hgb levels in our studied patients, as well as excess noise in the specific signals we measured in the videos. Based on these encouraging results, and the promise of greater accuracy with our revised hardware and software tools, we now propose a rigorous validation study to demonstrate that this approach to hemoglobin measurement is appropriate for general clinical application.

BACKGROUND In the USA, 5.6% of the population is anemic; 1.5% has moderate-severe anemia. Globally, 1.62 billion people are affected by Hb diseases. Clinical assessment of hemoglobin levels, using the cyan-methemoglobin method is reliable, but the process is not portable, results are not immediately available, and this test is unaffordable, and cost-ineffective for most patients in low- and middle-income countries who might benefit. When medical facilities and financial resources are available, frequent repeated testing is less than convenient under this method. In the presence of serious illness, with demands for repeated testing, the delays in obtaining results and the associated blood loss are particular drawbacks of this testing method. In these multiple circumstances, the potential advantages of a non-invasive, point-of-care (POC) method for hemoglobin measurement are clear. There are currently commercial non-invasive POC tools available for non-invasive Hb measurements. Most of these tools have one or more of the following limitations:1) challenging data collection methods; 2) complex data analysis and feature extraction processes; 3) affordability and portability; and 4) lack of user-friendliness and costly external modules. OBJECTIVE Investigate several hemoglobin measurement techniques based on smartphone devices, which have encountered significant challenges in theoretical foundation, data collection methods and sensors, data-signal analysis processes, and machine-learning algorithms. Our objective is to We identify these issue, define specific recommendations for practical solution development, and offer a conceptual framework for a noninvasive hemoglobin level estimation system using different types of smartphones and cloud computing paradigm. METHODS Growing interest and potential low cost of non-invasive hemoglobin measurement solutions has encouraged their development for low-resource settings, where the use of smartphones has increased rapidly. In such settings, the smartphone offers the possibility of an affordable, portable, and reliable point-of-care tool with leveraging its camera capacity, computing power, and lighting sources. However, several hemoglobin measurement techniques based on smartphone devices have encountered significant challenges in theoretical foundation, data collection methods and sensors, data-signal analysis processes, and machine-learning algorithms. We address these issues to define specific recommendations for practical solution development. Finally, we offer a conceptual framework for a noninvasive hemoglobin level estimation system using different types of smartphones and cloud computing paradigm. RESULTS Based on the foregoing review and other considerations, we suggested methods for body site selection for signal acquisition, response or signal, signal processing, theoretical foundations, feature generation, and machine learning algorithm selection. We also propose a conceptual framework for a noninvasive hemoglobin level estimation system using different types of smartphones and cloud computing paradigm. CONCLUSIONS As a growing widely available computing platform, the smartphone offers an alternative, non-invasive point-of-care tool to traditional measurements of blood hemoglobin. We recommend fingertip as a data collection site due to easy access, use of three different NIR lighting sources, specific signal processing techniques and feature selection methods, and region of interest selection methods, for the optimal development of an accurate hemoglobin prediction model. We point out the theoretical foundation, which can be applied for the identification of several blood constituent levels non-invasively. We suggest a conceptual framework for a non-invasive Hb level estimation system using different types of smartphones and a cloud computing paradigm. Investigators need to consider the following issues before developing such a Smartphone-based POC tool: (1) Cost of the smartphone, the external device, reagents if needed, and training, internet, and cloud implementation. (2) Patient’s other physiological features. (3) Allowing the user to do multiple checks and be challenged with a minimal cognitive load. (4) Including the user’s location, sex, and age in the stored record. (5) Keeping the external device as optional so that a user can run a diagnostic without the device. (6) Creating an external device that is cost-effective, easily attachable, properly fit with the finger, and user-friendly.