Development of Low-Cost Point-of-Care Technologies for Cervical Cancer Prevention Based on a Single-Board Computer

A paper-based HPV E7 oncoprotein assay for cervical precancer detection at the point of care

Cervical cancer, while preventable through screening and treatment of cervical precancer, remains a global challenge with a disproportionately high burden of disease in resource-limited settings, especially in low- and middle-income countries (LMICs). Lack of affordable, easy-to-use screening and diagnostic tests contributes to this disparity. Most commercially available tests are not appropriate for use in LMICs due to resource constraints. Specifically, HPV mRNA and oncoprotein tests that have high specificity for cervical precancer and cancer require complex sample preparation protocols and expensive instrumentation. To address these limitations, an HPV E7 oncoprotein assay for HPV16, 18, and 45 was developed that is appropriate for use at the point of care. The assay is paper-based, involves only five simple steps, and does not require instrumentation. A clinically relevant limit of detection was demonstrated with cellular samples. Additionally, clinical performance was demonstrated with a small pilot study (n = 19), in which the HPV E7 paper-based assay was found to have 95% accuracy when compared to histopathologic diagnosis of cervical intraepithelial neoplasia grade 2 or more severe (CIN2+). With further clinical validation, this assay could enable highly specific point-of-care testing for cervical precancer and cancer that is instrumentation-free, affordable, and ideal for use in resource-limited settings.

Early detection of carcinoma: correlating quantifiable tumor biomarkers with High-Resolution Microscopy (HRME) findings

INTRODUCTION

Cancer ranks as the second most prevalent cause of death worldwide, responsible for approximately 9.6 million deaths annually. Approximately one out of every six deaths is caused by cancer. About 80% of cancer deals with epithelial tissues located on the outer lines of the body cavity.

AREAS COVERED

This review study selected and analyzed recent works in the field of High Resolution Microendoscopy (HRME) that have been used to diagnose cancer in various organs such as cervical, esophageal, head & neck, and gastrointestinal.

EXPERT OPINION

The HRME modality will play a vital role in improving the diagnostic accuracy of carcinoma. HRME has shown promising statistical outcomes for diagnosing carcinoma, enabling the clinician to gain additional information before performing conventional tissue biopsy. A multimodal probe consisting of a macroscopic investigation aided by HRME modality for microscopic investigation can significantly reduce the number of unnecessary biopsies leading to overall improvement in patient wellness. The new directions of the HRME research would be in the light source and detection configuration, increasing the number of optical fiber cores, which improves the resolution of the image, AI-assisted automatic quantification of the key HRME parameters, and clinical studies with newer near-infrared regime-based contrast agents.

Automated analysis of digital medical images in cervical cancer screening: A systematic review

Background

Cervical cancer screening programs are poorly implemented in LMICs due to a shortage of specialists and expensive diagnostic infrastructure. To address the barriers of implementation researchers have been developing low-cost portable devices and automating image analysis for decision support.However, as the knowledge base is growing rapidly, progress on the implementation status of novel imaging devices and algorithms in cervical cancer screening has become unclear. The aim of this project was to provide a systematic review summarizing the full range of automated technology systems used in cervical cancer screening.

Method

A search on academic databases was conducted and the search results were screened by two independent reviewers. Study selection was based on eligibility in meeting the terms of inclusion and exclusion criteria which were outlined using a Population, Intervention, Comparator and Outcome framework.

Results

17 studies reported algorithms developed with source images from mobile device, viz. Pocket Colposcope, MobileODT EVA Colpo, Smartphone Camera, Smartphone-based Endoscope System, Smartscope, mHRME, and PiHRME. While 56 studies reported algorithms with source images from conventional/commercial acquisition devices. Most interventions were in the feasibility stage of development, undergoing initial clinical validations.

Conclusion

Researchers have proven superior prediction performance of computer aided diagnostics (CAD) in colposcopy (>80% accuracies) versus manual analysis (<70.0% accuracies). Furthermore, this review summarized evidence of the algorithms which are being created utilizing portable devices, to circumvent constraints prohibiting wider implementation in LMICs (such as expensive diagnostic infrastructure). However clinical validation of novel devices with CAD is not yet implemented adequately in LMICs.

Real-timing processing of fiber bundle endomicroscopy images in Python using PyFibreBundle

Fiber imaging bundles allow the transfer of optical images from place-to-place along narrow and flexible conduits. Traditionally used extensively in medical endoscopy, bundles are now finding new applications in endoscopic microscopy and other emerging techniques. PyFibreBundle is an open-source Python package for fast processing of images acquired through imaging bundles. This includes detection and removal of the fiber core pattern by filtering or interpolation, and application of background and flat-field corrections. It also allows images to be stitched together to create mosaics and resolution to be improved by combining multiple shifted images. This paper describes the technical implementation of PyFibreBundle and provides example results from three endomicroscopy imaging systems: color transmission, monochrome transmission, and confocal fluorescence. This allows various processing options to be compared quantitatively and qualitatively, and benchmarking demonstrates that PyFibreBundle can achieve state-of-the-art performance in an open-source package. The paper demonstrates core removal by interpolation and mosaicing at over 100 fps, real-time multi-frame resolution enhancement and the first demonstration of real-time endomicroscopy image processing, including core removal, on a Raspberry Pi single board computer. This demonstrates that PyFibreBundle is potentially a valuable tool for the development of low-cost, high-performance fiber bundle imaging systems.

A portable Raspberry Pi-based spectrometer for on-site spectral testing

We designed a portable Raspberry Pi-based spectrometer, which mainly consists of a white LED acting as the wide-spectrum source, a reflection grating for light dispersion, and a CMOS imaging chip aiming at spectral recording. All the optical elements and Raspberry Pi were integrated using 3-D printing structures with a size of 118 mm × 92 mm × 84 mm, and home-built software was also designed for spectral recording, calibration, analysis, and display implemented with a touch LCD. Additionally, the portable Raspberry Pi-based spectrometer was equipped with an internal battery, thus supporting on-site applications. Tested by a series of verifications and applications, the portable Raspberry Pi-based spectrometer could reach a spectral resolution of 0.065 nm per pixel within the visible band and provide spectral detection with high accuracy. Therefore, it can be used for on-site spectral testing in various fields.

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies-the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

Improve the cervical cancer prevention behaviors through mobile-based educational intervention based on I-CHANGE model: study protocol for a randomized controlled trial

Background

Applications of mobile technologies (mHealth) have the potential to reduce health inequalities, give patients more control over their health, and improve health care’s cost-effectiveness. The widespread use of mobile phones offers us a new way to prevent cervical cancer. The objective of the study was to design and develop a mobile phone application (app) that aims to conduct a behavioral intervention for women and to evaluate the efficacy of the app-based intervention.

Methods

This study involves 5 phases. In the first phase, understanding women’s perspectives will be identified using a qualitative approach based on the I-Change model. In phase 2, the results from the qualitative approach and requirement prioritization through providing experts’ perspectives will be done. The main outputs of this phase will be resulted in prioritizing the main measurable effective variables of the I-Change model. Phase 3 will be processed for the development and psychometric of an assessment tool regarding selected constructs. In phase 4, the App framework and content development will be performed. In phase 5, a three-armed, parallel-design randomized controlled trial will be conducted on women. Two hundred ten women will be randomly assigned to three groups including two intervention groups and one control group. The intervention groups included the following: (1) a mobile application and (2) a digital book. The data will be evaluated using tools designed and constructed in phase 3 of the study at baseline in 3-month follow-up assessments. The impact of the two interventions on cervical cancer prevention behaviors through mobile-based educational intervention will then be evaluated.

Discussion

A theory-based health education program using a mobile app to improve cervical cancer-preventive behaviors will be implemented for the first time in Iran. With an effective health mobile-based educational design, it is very important to determine whether Iranian women will be motivated to adhere to preventive behavior related to CC.

Trial registration

Iranian Clinical Trial Register IRCT20181205041861N3. Registered V2.0 on 26 October 2021.

A Low-Cost, Open-Source Peer-to-Peer Energy Trading System for a Remote Community Using the Internet-of-Things, Blockchain, and Hypertext Transfer Protocol

A low-cost, open-source peer-to-peer (P2P) energy trading system for a remote community is presented in this paper. As a result of its geographic location, this community has never been able to access electricity and other modern amenities. This study aims to design and implement a P2P energy trading system for this remote community that allows residents to take advantage of distributed energy resources. A Raspberry Pi 4 Model B (Pi4B) hosts the main server of the trading system that includes the user interface and a local Ethereum blockchain server. The Ethereum blockchain is used to deploy smart contracts. The Internet-of-Things (IoT) servers run on ESP32 microcontrollers. Sensors and actuators connected to the ESP32 are field instrumentation devices that facilitate acquiring, monitoring, and transferring energy data in real-time. To perform trading activities, React.JS open-source library was used to develop the blockchain-enabled user interface. An immutable blockchain network keeps track of all transactions. The proposed system runs on a local Wi-Fi network with restricted authorization for system security. Other security measures such as login credentials, private key, firewall, and secret recovery phrases are also considered for information security and data integrity. A Hypertext Transfer Protocol is implemented for communication between the servers and the client. This explains the overall system design, implementation, testing, and results.

Use of some cost-effective technologies for a routine clinical pathology laboratory

Classically, the need for highly sophisticated instruments with important economic costs has been a major limiting factor for clinical pathology laboratories, especially in developing countries. With the aim of making clinical pathology more accessible, a wide variety of free or economical technologies have been developed worldwide in the last few years. 3D printing and Arduino approaches can provide up to 94% economical savings in hardware and instrumentation in comparison to commercial alternatives. The vast selection of point-of-care-tests (POCT) currently available also limits the need for specific instruments or personnel, as they can be used almost anywhere and by anyone. Lastly, there are dozens of free and libre digital tools available in health informatics. This review provides an overview of the state-of-the-art on cost-effective alternatives with applications in routine clinical pathology laboratories. In this context, a variety of technologies including 3D printing and Arduino, lateral flow assays, plasmonic biosensors, and microfluidics, as well as laboratory information systems, are discussed. This review aims to serve as an introduction to different technologies that can make clinical pathology more accessible and, therefore, contribute to achieve universal health coverage.

Ultracompact fluorescence smartphone attachment using built-in optics for protoporphyrin-IX quantification in skin

Smartphone-based fluorescence imaging systems have the potential to provide convenient quantitative image guidance at the point of care. However, common approaches have required the addition of complex optical attachments, which reduce translation potential. In this study, a simple clip-on attachment appropriate for fluorescence imaging of protoporphyrin-IX (PpIX) in skin was designed using the built-in light source and ultrawide camera sensor of a smartphone. Software control for image acquisition and quantitative analysis was developed using the 10-bit video capability of the phone. Optical performance was characterized using PpIX in liquid tissue phantoms and endogenously produced PpIX in mice and human skin. The proposed system achieves a very compact form factor (<30 cm3) and can be readily fabricated using widely available low-cost materials. The limit of detection of PpIX in optical phantoms was <10 nM, with good signal linearity from 10 to 1000 nM (R2 >0.99). Both murine and human skin imaging verified that in vivo PpIX fluorescence was detected within 1 hour of applying aminolevulinic acid (ALA) gel. This ultracompact handheld system for quantification of PpIX in skin is well-suited for dermatology clinical workflows. Due to its simplicity and form factor, the proposed system can be readily adapted for use with other smartphone devices and fluorescence imaging applications. Hardware design and software for the system is made freely available on GitHub (https://github.com/optmed/CompactFluorescenceCam).

PARP1: A Potential Molecular Marker to Identify Cancer During Colposcopy Procedures

Despite efforts in prevention, cervical cancer still presents with a high worldwide incidence and remains a great problem in public health, especially in low-income countries. Screening programs, such as colposcopy with Papanicolaou testing, have greatly improved mortality rates. However, the agents currently used to delineate those lesions (topical application of acetic acid or Lugol iodine) lack specificity and sometimes can lead to unnecessary biopsies or even cervical excisions. A tool to enable in vivo histology to quickly and quantitatively distinguish between tumor, dysplastic tissue, and healthy tissue would be of great clinical interest. Methods: Here, we describe the use of PARPi-FL, a fluorescent inhibitor of poly[adenosine diphosphate-ribose]polymerase 1 (PARP1), which is a nuclear enzyme that is overexpressed in cancer when compared with the normal surrounding tissues. We exploit its use as an optical imaging agent to specifically target PARP1 expression, which was demonstrated to be higher in cervical cancer than the normal surrounding tissue. Results: After topical application of PARPi-FL on freshly excised cone biopsy samples, the nuclei of tumor cells emitted a specific fluorescent signal that could be visualized using a handheld fluorescence confocal microscope. Conclusion: This approach has the potential to improve in vivo identification of tumor cells during colposcopy examination, allowing a rapid, noninvasive, and accurate histopathologic assessment.

Smartphone-based imaging systems for medical applications: a critical review

SIGNIFICANCE

Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed.

AIM

Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances.

APPROACH

First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed.

RESULTS

The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo, multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications.

CONCLUSIONS

A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forward.

A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study

Background

Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients.

Objective

The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope.

Methods

We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope.

Results

We were able to conveniently operate Auscul Pi and precisely record the patients’ auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001).

Conclusions

Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19.

Trial Registration

ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971.

Expression and Clinical Significance of KLRG1 and 2B4 on T Cells in the Peripheral Blood and Tumour of Patients with Cervical Cancer

Background: Killer cell lectin-like receptor G1 (KLRG1) and 2B4 play important roles in the immune regulation and immune tolerance to tumor cells by inhibiting T cell function. However, the clinical relevance of KLRG1 and 2B4 to cervical cancer remains to be understood.Methods: We measured the frequency of KLRG1+ or 2B4+ cells in CD4+ or CD8 + T cells derived from peripheral blood or tumour biopsies in cervical cancer patients by flow cytometry.Results: Compared with healthy controls, the level of KLRG1 and 2B4 on CD8 + T cells in the blood of the patients increased significantly (P = .0056 and .0441). KLRG1 level on CD8 + T cells and 2B4 level on CD4 + T cells in peripheral blood were significantly higher than in tumor tissues (P < .0001 and P = .0003). Higher KLRG1 level on blood-derived CD8 + T cells was observed in patients older than 54 years (P = .001) or tested to be HPV-negative (P = .026). Tumor-infiltrated CD8 + T cells demonstrated elevated KLRG1 level in patients having pelvic lymph node metastasis (P = .016). Increased 2B4 level on blood-derived CD8 + T cells was also observed in patients older than 54 years (P < .001). KLRG1 expression on both CD4 + T (P = .0158) and CD8 + T (P = .0187) cells in the peripheral blood increased after radiotherapy.Conclusion: KLRG1 level on T cells was related to age and HPV in patients with cervical cancer, while 2B4 level on T cells was related to age, underlying their roles in the host immune response to cervical cancer. Radiotherapy can improve the immune function of patients.