Smartphone-based Diagnostics Research Articles

Smartphone-Based Diagnostic Tools: Revolutionizing Healthcare Access

Smartphone-based diagnostic tools have emerged as transformative innovations in healthcare, offering accessible, cost-effective solutions for disease diagnosis, remote monitoring, and patient engagement. This narrative review examines the capabilities, benefits, and limitations of these tools in healthcare delivery. We discuss their role in addressing disparities in healthcare access, particularly in resource-limited and remote areas, and their potential to support early detection and management of chronic diseases. Challenges such as data privacy, technological accessibility, and regulatory gaps are also analyzed. The review emphasizes the importance of multidisciplinary collaborations to optimize and scale these tools, addressing ethical concerns and ensuring equitable healthcare delivery. Future directions include advancements in sensor technology, artificial intelligence integration, and policy adaptations to harness the full potential of smartphone-based diagnostics. Keywords: Smartphone diagnostics, Mobile health (mHealth), Telemedicine, Remote monitoring, Early disease detection.

Smartphone-based diagnostics with coherent and infrared imaging for cultural heritage

Abstract Nondestructive optical techniques are crucial in heritage science for monitoring the condition of artworks in full field. Various imaging methods based on infrared and interferometry techniques have been proposed, but they often require specialized training and expensive equipment. This paper explores the emerging field of smartphone science and its potential to revolutionize artwork diagnostics, especially for cultural institutions with limited budgets. The smartphone science approach is divided into using the device ‘as is’ or enhancing it with add-on sensors. After a concise overview of smartphone sensing in different fields, the paper demonstrates smartphone-based optical diagnostics on traditional wooden painting models, employing coherent techniques like laser speckle imaging and moiré fringe technique, and infrared techniques like reflectography and thermography. The comparison of obtained results with established instrumentation in the field clearly shows that smartphone-based diagnostics have the potential to greatly contribute to cultural heritage preservation and conservation, transforming the field’s accessibility and cost-effectiveness.

Smartphone-based diagnostics for biosensing infectious human pathogens

The widespread usage of smartphones has made accessing vast troves of data easier for everyone. Smartphones are powerful, handy, and easy to operate, making them a valuable tool for improving public health through diagnostics. When combined with other devices and sensors, smartphones have shown potential for detecting, visualizing, collecting, and transferring data, enabling rapid disease diagnosis. In resource-limited settings, the user-friendly operating system of smartphones allows them to function as a point-of-care platform for healthcare and disease diagnosis. Herein, we critically reviewed the smartphone-based biosensors for the diagnosis and detection of diseases caused by infectious human pathogens, such as deadly viruses, bacteria, and fungi. These biosensors use several analytical sensing methods, including microscopic imaging, instrumental interface, colorimetric, fluorescence, and electrochemical biosensors. We have discussed the diverse diagnosis strategies and analytical performances of smartphone-based detection systems in identifying infectious human pathogens, along with future perspectives.

Smartphone-based fluorescent ELISA with simple fluorescent enhancement strategy for Opisthorchis viverrini (Ov) antigen detection in urine samples

Enzyme-linked immunosorbent assay (ELISA) is a new approach for the diagnosis of Opisthorchis viverrini antigen (OvAg) in urine samples. Herein, we demonstrate a portable fluorometer based on the smartphone to use as the point-of-care device for the diagnosis of OvAg in urine samples by fluorescence ELISA. In addition, the detection sensitivity of the fluorescent assay has been improved by a simple strategy using aqueous micelles of the non-ionic surfactant. The combination of a smartphone-based device with fluorescent enhancement provides an excellence tool for the diagnosis of the OvAg in urine samples. The smartphone-based device shows excellent potential for measuring the fluorescence signal comparable with a fluorescence microplate reader. The signal in the green channel (G) of the smartphone representing the emission intensity from the fluorescence ELISA exhibits a good linear relationship with OvAg concentration with an LOD of 33 ng mL−1. The proposed device achieved high diagnostic sensitivity, specificity, and accuracy of 84.88%, 89.66%, and 86.14%, respectively, compared to the measurement by conventional instruments (colorimetric ELISA). The smartphone-based device has been verified for the diagnosis of OvAg in urine samples from an endemic area with excellent results. Therefore, smartphone-based diagnostics is an effective point-of-care device for on-site assay of OvAg detection in urine samples.

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.

Photoplethysmography using a smartphone application for assessment of ulnar artery patency: a randomized clinical trial.

Radial artery access is commonly performed for coronary angiography and invasive hemodynamic monitoring. Despite limitations in diagnostic accuracy, the modified Allen test (manual occlusion of radial and ulnar arteries followed by release of the latter and assessment of palmar blush) is used routinely to evaluate the collateral circulation to the hand and, therefore, to determine patient eligibility for radial artery access. We sought to evaluate whether a smartphone application may provide a superior alternative to the modified Allen test. We compared the modified Allen test with a smartphone heart rate-monitoring application (photoplethysmography readings detected using a smartphone camera lens placed on the patient's index finger) in patients undergoing a planned cardiac catheterization. Test order was randomly assigned in a 1:1 fashion. All patients then underwent conventional plethysmography of the index finger, followed by Doppler ultrasonography of the radial and ulnar arteries (the diagnostic standard). The primary outcome was diagnostic accuracy of the heart rate-monitoring application. Among 438 patients who were included in the study, we found that the heart rate-monitoring application had a superior diagnostic accuracy compared with the modified Allen test (91.8% v. 81.7%, p = 0.002), attributable to its greater specificity (93.0% v. 82.8%, p = 0.001). We also found that this application had greater diagnostic accuracy for assessment of radial or ulnar artery patency in the ipsilateral and contralateral wrist (94.0% v. 84.0%, p < 0.001). A smartphone application used at the bedside was diagnostically superior to traditional physical examination for confirming ulnar patency before radial artery access. This study highlights the potential for smartphone-based diagnostics to aid in clinical decision-making at the patient's bedside. Trial registration: Clinicaltrials.gov, no. NCT02519491.

Hands-free smartphone-based diagnostics for simultaneous detection of Zika, Chikungunya, and Dengue at point-of-care

Infectious diseases remain the world's top contributors to death and disability, and, with recent outbreaks of Zika virus infections there has been an urgency for simple, sensitive and easily translatable point-of-care tests. Here we demonstrate a novel point-of-care platform to diagnose infectious diseases from whole blood samples. A microfluidic platform performs minimal sample processing in a user-friendly diagnostics card followed by real-time reverse-transcription loop-mediated isothermal amplification (RT-LAMP) on the same card with pre-dried primers specific to viral targets. Our point-of-care platform uses a commercial smartphone to acquire real-time images of the amplification reaction and displays a visual read-out of the assay. We apply this system to detect closely related Zika, Dengue (types 1 and 3) and Chikungunya virus infections from whole blood on the same pre-printed chip with high specificity and clinically relevant sensitivity. Limit of detection of 1.56e5 PFU/mL of Zika virus from whole blood was achieved through our platform. With the ability to quantitate the target nucleic acid, this platform can also perform point-of-care patient surveillance for pathogen load or select biomarkers in whole blood.