A simple diagnostic scoring system for COVID-19 screening

Background: The COVID-19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID-19 quickly and accurately. Early identification of COVID-19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection. Methodology: This study used a retrospective cohort design that involved 705 adults (>= 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power. Results: Two scores were generated;score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764-0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826-0. 891);the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19. Conclusions: A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection.

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review.

Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at-home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone-tethered optical platform through portable, automated, and deep-learning-enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone-based microscopes and lens-free holographic microscopy are introduced, and deep learning-based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID-19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability.

A simple diagnostic scoring system for COVID-19 screening

Background: The COVID-19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID-19 quickly and accurately. Early identification of COVID-19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection. Methodology: This study used a retrospective cohort design that involved 705 adults (>= 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power. Result(s): Two scores were generated;score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764-0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826-0. 891);the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19. Conclusion(s): A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection. Copyright © 2022 Faculty of Anaesthesia, Pain and Intensive Care, AFMS. All rights reserved.

AI-based Screening System for COVID-19

The outbreak of the COVID-19 pandemic caused by the novel coronavirus has disrupted global health systems and changed the way of life. Leveraging the potential of technology has become indispensable to ensure safety as new strains emerge. In this paper, we propose a low-cost AI-based screening system that can be installed at various locations. The objective of our solution is to automate the task of face mask detection, checking for social distancing, and body temperature scanning. The dataset used for our AI model consisted of 2314 images combining those without a mask and those with an artificial mask attached using computer vision techniques. These three functionalities were carried out by combining AI and IoT technologies. Specifically, we employed the SingleShot-Multibox detector (SSD) and ResNet-10 architecture as the first part of our model for face detection. MobileNetV2 architecture was used as the second part of the model for binary classification (with or without mask). Various IoT components were integrated to achieve a real-time screening process and subsequently simulate entry access or denial. Our proposed model outperformed other existing solutions by achieving an accuracy of 99% and an F1 score of 0.99. © 2022 IEEE.

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation.

Background: To conduct a rapid preliminary COVID-19 screening prior to polymerase chain reaction (PCR) test under clinical settings, including patient's body moving conditions in a non-contact manner, we developed a mobile and vital-signs-based infection screening composite-type camera (VISC-Camera) with truncus motion removal algorithm (TMRA) to screen for possibly infected patients. Methods: The VISC-Camera incorporates a stereo depth camera for respiratory rate (RR) determination, a red-green-blue (RGB) camera for heart rate (HR) estimation, and a thermal camera for body temperature (BT) measurement. In addition to the body motion removal algorithm based on the region of interest (ROI) tracking for RR, HR, and BT determination, we adopted TMRA for RR estimation. TMRA is a reduction algorithm of RR count error induced by truncus non-respiratory front-back motion measured using depth-camera-determined neck movement. The VISC-Camera is designed for mobile use and is compact (22 cm × 14 cm × 4 cm), light (800 g), and can be used in continuous operation for over 100 patients with a single battery charge. The VISC-Camera discriminates infected patients from healthy people using a logistic regression algorithm using RR, HR, and BT as explanatory variables. Results are available within 10 s, including imaging and processing time. Clinical testing was conducted on 154 PCR positive COVID-19 inpatients (aged 18-81 years; M/F = 87/67) within the initial 48 h of hospitalization at the First Central Hospital of Mongolia and 147 healthy volunteers (aged 18-85 years, M/F = 70/77). All patients were on treatment with antivirals and had body temperatures <37.5°C. RR measured by visual counting, pulsimeter-determined HR, and BT determined by thermometer were used for references. Result: 10-fold cross-validation revealed 91% sensitivity and 90% specificity with an area under receiver operating characteristic curve of 0.97. The VISC-Camera-determined HR, RR, and BT correlated significantly with those measured using references (RR: r = 0.93, p < 0.001; HR: r = 0.97, p < 0.001; BT: r = 0.72, p < 0.001). Conclusion: Under clinical settings with body motion, the VISC-Camera with TMRA appears promising for the preliminary screening of potential COVID-19 infection for afebrile patients with the possibility of misdiagnosis as asymptomatic.

Iranian National COVID-19 Electronic Screening System: A Mixed-Method Usability Evaluation

This study was conducted to determine the mixed-method usability evaluation of the Iranian national covid-19 electronic screening system. The cross-sectional study was carried out in partnership with 116 users of the Iranian national covid-19 electronic screening system and five experts. As a result of the experts' assessment, the Iranian national covid-19 electronic screening system scored 0-2 out of the 10 principles of Nielsen Jacob, which indicates a good approach to the design of this system. To evaluate, the questionnaire for user interaction satisfaction (QUIS) version 7 was used. Data were analyzed by spss version 19. A total of 112 out of 116 questionnaires were obtained. In the Iranian national covid-19 electronic screening system, nine (33.3%) of the 27 sections scored higher than seven. More than half scored over five. There were no factors in the terminology and system information and learning section between 7 and 9;the highest rankings in the section overall responses to the software were 1) terrible-wonderful 2) difficult-easy;in the section overall reactions to the software, all of the factors were highest;also, the highest rankings were in the section 'system capability' for 1) system speed 2) system reliability 3) designed for all levels of users. © 2022 IEEE.

An IoT Based Intelligent Screening System for Preventing COVID 19 Spread in Premises

The outbreak of COVID-19 led to huge number of security issues and casualties. In order to effectively prevent the spread of COVID-19, wearing mask and contactless thermal scanning has become mandatory. The conventional methods for visitor screening and temperature measurement have become inappropriate in this situation to avoid any contact. The traditional face recognition techniques are also ineffective as wearing mask hides some parts of the face. This paper presents an Intelligent IoT based screening system capable of contactless thermal scanning, mask detection and masked face recognition. The person not following the Covid-19 guidelines will be denied the access inside the premise. The system uses deep-learning based techniques for masked face recognition by discarding masked region. The data collected has been sent to the cloud using custom protocol stack which is lightweight, reliable and efficient for data communication. Using web application and mobile application, the data corresponding to each user can be monitored remotely using internet. The data collected can be shared to healthcare agencies for further analysis. © 2021 IEEE.

A Yeast-Based Screening System for Differential Identification of Poisons and Suppressors of Human Topoisomerase I.

BACKGROUND: Inhibition of human topoisomerase I (TOP1) by camptothecin and topotecan has been shown to reduce excessive transcription of PAMP (Pathogen-Associated Molecular Pattern)-induced genes in prior studies, preventing death from sepsis in animal models of bacterial and SARS-CoV-2 infections. The TOP1 catalytic activity likely resolves the topological constraints on DNA that encodes these genes to facilitate the transcription induction that leads to excess inflammation. The increased accumulation of TOP1-DNA covalent complex (TOP1cc) following DNA cleavage is the basis for the anticancer efficacy of the TOP1 poisons developed for anticancer treatment. The potential cytotoxicity and mutagenicity of TOP1 targeting cancer drugs pose serious concerns for employing them as therapies in sepsis prevention. METHODS: In this study we set up a novel yeast-based screening system that employs yeast strains expressing wild-type or a dominant lethal mutant recombinant human TOP1. The effect of test compounds on growth is monitored with and without overexpression of the recombinant human TOP1. RESULTS: This yeast-based screening system can identify human TOP1 poisons for anticancer efficacy as well as TOP1 suppressors that can inhibit TOP1 DNA binding or cleavage activity in steps prior to the formation of the TOP1cc. CONCLUSIONS: This yeast-based screening system can distinguish between TOP1 suppressors and TOP1 poisons. The assay can also identify compounds that are likely to be cytotoxic based on their effect on yeast cell growth that is independent of recombinant human TOP1 overexpression.

Design of an Automatic Temperature Screening System for Elevated Skin Temperature with Information Logging Capability

With the outbreak of the highly-contagious SARS-CoV-2 virus and its accompanying coronavirus disease 2019 (COVID-19), many government agencies adopted contact tracing to measure and mitigate the spread of the virus. Contact tracing aims to keep track of the individual's movements and activities and identify all those who they come in contact with. This study is focused on designing a cost-effective, efficient, and accurate system for information logging and temperature screening with a complementary contact tracing feature. The system provides an automated, safe, and physical-distance-aware alternative to manual temperature measurement and data logging practiced by most commercial establishments. The system uses an Arduino and a Raspberry Pi, along with infrared temperature sensors utilizing proper calibration methods to yield temperature reading difference of 0.1-0.3 degree-Celsius taken at 10 cm distance. User identification is done by reading either specifically-registered RFID tags or system-generated identity-QR code. Temperature is subsequently read, date and time stamped, and logged into the system. This allows for automated and exact association of the user logged information with their corresponding temperature. © 2021 IEEE.

A framework of multi-point infrared temperature screening system for COVID-19 pandemic

This paper describes a framework for COVID-19 pandemic screening that includes a multi-infrared temperature sensor. Due to the high risk of transmission of the COVID-19 epidemic in closed areas, it is important to secure these areas in terms of epidemics. Symptoms of COVID-19 disease include fever in patients. Thermal cameras or infrared temperature sensors are used to detect this anomaly in real-time. In this study, a study was carried out on which multiple uses of infrared sensors increase the measurement performance. Additionally, the general concept of an intelligent long-range temperature measurement system with facial recognition support is presented, which may be simply integrated with this approach. © 2021 IEEE.

Non-contact screening system based for COVID-19 on XGBoost and logistic regression.

BACKGROUND: The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. OBJECTIVE: We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. METHODS: We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. RESULTS: The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. CONCLUSION: The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

Yeungnam University type drive-through (YU-Thru) coronavirus disease 2019 (COVID-19) screening system: a rapid and safe screening system.

Active and prompt scale-up screening tests are essential to efficiently control the coronavirus disease 2019 (COVID-19) outbreak. The goal of this work was to identify shortcomings in the conventional screening system (CSS) implemented in the beginning of the outbreak. To overcome these shortcomings, we then introduced a novel, independently developed system called the Yeungnam University type drive-through (YU-Thru), and distributed it nationwide in Korea. This system is similar to the drive-throughs utilized by fast food restaurants. YU-Thru system has shortened the time taken to test a single person to 2-4 minutes, by completely eliminating the time required to clean and ventilate the specimen collection room. This time requirement was a major drawback of the CSS. YU-Thru system also reduced the risk of subjects and medical staff infecting one another by using a separate and closed examination system. On average, 50 to 60 tests were conducted per day when using the CSS, while now up to 350 tests per day are conducted with the YU-Thru system. We believe that the YU-Thru system has made an important contribution to the rapid detection of COVID-19 in Daegu, South Korea. Here, we will describe the YU-Thru system in detail so that other countries experiencing COVID-19 outbreaks can take advantage of this system.

Analysis of SARS-CoV-2 Screening Clinic (Including Drive-Through System) Data at a Single University Hospital in South Korea from 27 January 2020 to 31 March 2020 during the COVID-19 Outbreak.

In this study, we evaluated the efficiency of a drive-through (DT) screening system for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by comparing it with a conventional screening system. We reviewed and analyzed the SARS-CoV-2 screening data obtained at our university hospital. We compared the number of tests for SARS-CoV-2 (using real-time polymerase chain reaction) performed using two different specimen collection systems-DT and conventional-during the coronavirus disease 2019 (COVID-19) outbreak in Daegu. Based on the results, the DT screening system collected 5.8 times more specimens for testing than the conventional screening system. From January 27 to 31 March 2020, 6211 individuals were screened for SARS-CoV-2 infection using either the DT or conventional system. In total, 217 individuals tested positive for SARS-CoV-2 (positive rate: 3.50%). Of the 6211 individuals, 3368 were symptomatic or had a history of contact with COVID-19 patients, and 142 of them tested positive for SARS-CoV-2 (positive rate: 4.22%). Further, 2843 individuals were asymptomatic and had no history of contact with COVID-19 patients, and 75 of them tested positive for SARS-CoV-2 (positive rate: 2.64%). In conclusion, the DT system allowed clinicians to collect specimens for SARS-CoV-2 screening more efficiently than the conventional system. Furthermore, as there might be several COVID-19 patients who remain asymptomatic, expanding the screening test to asymptomatic individuals would be necessary.