A Portable Smartphone-linked Device for Direct, Rapid and Chemical-Free Hemoglobin Assay

A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy

In this paper, we propose a smartphone-based biosensor for detecting human total hemoglobin concentration in vivo with high accuracy. Compared to the existing biosensors used to measure hemoglobin concentration, the smartphone-based sensor utilizes the camera, memory, and computing power of the phone. Thus, the cost is largely reduced. Compared to existing smartphone-based sensors, we developed a highly integrated multi-wavelength LED module and a specially designed phone fixture to reduce spatial errors and motion artifacts, respectively. In addition, we embedded a new algorithm into our smartphone-based sensor to improve the measurement accuracy; an L*a*b* color space transformation and the “a” parameter were used to perform the final quantification. We collected 24 blood samples from normal and anemic populations. The adjusted R² of the prediction results obtained from the multiple linear regression method reached 0.880, and the RMSE reached 9.04, which met the accuracy requirements of non-invasive detection of hemoglobin concentration.

Point-of-care microchip electrophoresis for integrated anemia and hemoglobin variant testing

Anemia affects over 25% of the world's population with the heaviest burden borne by women and children. Genetic hemoglobin (Hb) variants, such as sickle cell disease, are among the major causes of anemia. Anemia and Hb variant are pathologically interrelated and have an overlapping geographical distribution. We present the first point-of-care (POC) platform to perform both anemia detection and Hb variant identification, using a single paper-based electrophoresis test. Feasibility of this new integrated diagnostic approach is demonstrated via testing individuals with anemia and/or sickle cell disease. Hemoglobin level determination is performed by an artificial neural network (ANN) based machine learning algorithm, which achieves a mean absolute error of 0.55 g dL^-1 and a bias of -0.10 g dL^-1 against the gold standard (95% limits of agreement: 1.5 g dL^-1) from Bland-Altman analysis on the test set. Resultant anemia detection is achieved with 100% sensitivity and 92.3% specificity. With the same tests, subjects with sickle cell disease were identified with 100% sensitivity and specificity. Overall, the presented platform enabled, for the first time, integrated anemia detection and hemoglobin variant identification using a single point-of-care test.

The validity of mobile applications to facilitate patient care provided to cancer patients: opportunities and limitations

BACKGROUND

The use of health-related applications (apps) on smartphones has become widespread. This is especially of value during the ongoing SAR-COV-2 pandemic, where the accessibility for health care services has been greatly limited. Patients with free access to apps can obtain information to improve their understanding and management of health issues. Currently, there are cancer-related apps available on iPhones and androids. However, there are no guidelines to control these apps and ensure their quality. Furthermore, these apps may significantly modify the patients' perception and knowledge toward drug-related health services.

OBJECTIVE

The aim of this study was to assess the convenience, quality, safety and efficacy of apps for cancer patient care.

METHODS

The study was conducted by searching all apps related to cancer care on both Google Play Store and Apple iTunes Store. A detailed assessment was then performed using the mobile application rating scale (MARS) and risk assessment tools.

RESULTS

The results indicated that on a scale from 1-5, 47% of the apps were rated ≥ 4. The MARS assessment of the apps indicated an overall quality rating of 3.38 ± 0.9 (mean ± SD). The visual appeal of the app was found to have a significant effect on app functionality and user engagement. The potential benefits of these apps come with challenges and limitations. Patents related to smartphone applications targeting patients were also discussed.

CONCLUSION

We recommend a greater emphasis toward producing evidence-based apps. These apps should be rigorously tested, evaluated and updated by experts, particularly clinical pharmacists. Also, these may alter patient attitudes toward services provided by physicians and pharmacists. Finally, these apps should not replace in-person interactive health services.

Optical Investigation of Individual Red Blood Cells for Determining Cell Count and Cellular Hemoglobin Concentration in a Microfluidic Channel

We demonstrate a blood analysis routine by observing red blood cells through light and digital holographic microscopy in a microfluidic channel. With this setup a determination of red blood cell (RBC) concentration, the mean corpuscular volume (MCV), and corpuscular hemoglobin concentration mean (CHCM) is feasible. Cell count variations in between measurements differed by 2.47% with a deviation of −0.26×106 μL to the reference value obtained from the Siemens ADVIA 2120i. Measured MCV values varied by 2.25% and CHCM values by 3.78% compared to the reference ADVIA measurement. Our results suggest that the combination of optical analysis with microfluidics handling provides a promising new approach to red blood cell counts.

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.