Design and Implementation of Intelligent Truck Based on Azure Kinect

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography Images

This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal Processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature. © 2022 ACM.

Metodo LAMP como alternativa diagnostica para la deteccion del virus SARS-CoV-2

Summary Introduction: The SARS-CoV-2 coronavirus, that causes the COVID-19 disease, has become a global public health problem that requires the implementation of rapid and sensitive diagnostic tests. Aim(s): To evaluate and compare the sensitivity of LAMP assay to a standard method and use RT-LAMP for the diagnosis of SARS-CoV-2 in clinical samples from Colombian patients. Method(s): A descriptive and cross-sectional study was conducted. A total of 25 nasopharyngeal swab samples including negative and positive samples for SARS-CoV-2 were analyzed, through the RT-LAMP method compared to the RT-qPCR assay. Result(s): LAMP method detected ~18 copies of the N gene, in 30 min, evidenced a detection limit similar to the standard method, in a shorter time and a concordance in RT-LAMP of 100% with the results. Conclusion(s): RT-LAMP is a sensitive, specific, and rapid method that can be used as a diagnostic aid of COVID-19 disease.Copyright © 2021. All Rights Reserved.

Improved mask detection based on YOLOv5s

COVID-19 epidemic is not over. The correct wearing of masks can effectively prevent the spread of the virus. Aiming at a series of problems of existing mask-wearing detection algorithms, such as only detecting whether to wear or not, being unable to detect whether to wear correctly, difficulty in detecting small targets in dense scenes, and low detection accuracy, It is suggested to use a better algorithm based on YOLOv5s. It improves the generalization and transmission performance of the model by changing the ACON activation function. Then Bifpn is used to replace PAN to effectively integrate the target features of different sizes extracted by the network. Finally, To enable the network to pay attention to a wide area, CA is introduced to the backbone. This embeds the location information into the channel attention. © 2023 SPIE.

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Masked Face Recognition Model with Explainable AI

Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model's accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

Advantages of Total Bilirubin for Predicting Malignant Obstructive Jaundice, A Combination of the Pandemic Era and Limited Resources Settings

BACKGROUND: Hepatopancreatobiliary (HPB) cancer incidence and mortality are increasing worldwide. An initial diagnostic predictor is needed for recommending further diagnostic modalities, referral, and curative or palliative decisions. There were no studies conducted in area with limited accessibility setting of the COVID-19 pandemic, coupled with limited human resources and facilities. AIM: We aimed to investigate the advantages of total bilirubin for predicting malignant obstructive jaundice, a combination of the pandemic era and limited resources settings. METHOD(S): Data from all cholestasis jaundice patients at M. Djamil Hospital in Pandemic COVID-19 period from July 2020 to May 2022 were retrospectively collected. The data included demographics, bilirubin fraction results, and final diagnosis. Bivariate analysis for obtain demographic risk factor, and Receiver Operating Characteristics (ROC) analysis for getting bilirubin value. RESULT(S): Of a total 132 patients included, 35.6% were malignant obstructive jaundice, and Pancreatic adeno ca was the most malignant etiology (34.4%). Bivariate analysis showed a significant correlation between age and malignant etiology (p = 0,024). Direct and total Bilirubin reach the same level of Area Under Curve (AUC). Total bilirubin at the cutoff point level of 10.7 mg/dl had the most optimal results on all elements of ROC output, AUC 0.88, sensitivity 76.6%, specificity 90.1%, +LR 8.14, and-LR 0.26. CONCLUSION(S): The bilirubin fraction is a good initial indicator for differentiating benign and malignant etiology (AUC 0.8-0.9) in pandemic era and resource-limited areas to improve diagnostic effectiveness and reduce referral duration.Copyright © 2023 Avit Suchitra, M. Iqbal Rivai, Juni Mitra, Irwan Abdul Rachman, Rini Suswita, Rizqy Tansa.

Graphene-Based Electrochemical Nano-Biosensors for Detection of SARS-CoV-2

COVID-19, a viral respiratory illness, is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which was first identified in Wuhan, China, in 2019 and rapidly spread worldwide. Testing and isolation were essential to control the virus's transmission due to the severity of the disease. In this context, there is a global interest in the feasibility of employing nano-biosensors, especially those using graphene as a key material, for the real-time detection of the virus. The exceptional properties of graphene and the outstanding performance of nano-biosensors in identifying various viruses prompted a feasibility check on this technology. This paper focuses on the recent advances in using graphene-based electrochemical biosensors for sensing the SARS-CoV-2 virus. Specifically, it reviews various types of electrochemical biosensors, including amperometric, potentiometric, and impedimetric biosensors, and discusses the current challenges associated with biosensors for SARS-CoV-2 detection. The conclusion of this review discusses future directions in the field of electrochemical biosensors for SARS-CoV-2 detection, underscoring the importance of continued research and development in this domain.

Procalcitonin as a Prognostic Marker of Complicated Appendicitis

Introduction: Acute appendicitis is the most common cause of acute abdominal pain as well as one of the most frequently performed procedures in general surgery. Different prognostic laboratory markers have been studied to identify patients with complicated appendicitis and it is unknown whether the level of procalcitonin in adults could be used as a predictive marker. From a cut-off point, Does procalcitonin have predictive value for complicated appendicitis? Methods: Prospective, observational study. Patients from the Civil Hospital of Guadalajara with a diagnosis of Appendicitis, presurgical laboratory studies and Procalcitonin, and undergo appendectomy in this institution. A calculated sample was obtained based on the surgeries performed annually. Result(s): 80 appendicectomies were performed in the 12-month period (2021;COVID pandemic) obtaining: 37 patients with uncomplicated appendicitis (Phase I and II) 43 patients with complicated appendicitis (Phase III and IV) The procalcitonin levels of both groups were analyzed to demonstrate differences between them, Mann-Whitney U test gives us as a result a p value <0.05. For the cut-off point at the most suitable procalcitonin level for this sample we decided to use the Yauden index method in the analysis of the ROC curve: it is observed that the cut-off point with a sensitivity of 72.1% and a specificity of 81.1% for the sample is 0.305. Conclusion(s): Procalcitonin has been shown to be a useful marker for discriminating the severity of appendicitis and that the best cutoff point for this sample is 0.3 ng/dl.

Direct bilirubin and the neutrophil-to-monocyte ratio timely predict intensive care unit admission in patients with severe acute respiratory syndrome coronavirus-2 infection (COVID-19)

Objective: Intensive care units (ICUs) collapsed under the global wave of coronavirus disease 2019 (COVID-19). Thus, we designed a clinical decision-making model that can help predict at hospital admission what patients with COVID-19 are at higher risk of requiring critical care. Method(s): This was a cross-sectional study in 119 patients that met hospitalization criteria for COVID-19 including less than 30 breaths per minute, peripheral oxygen saturation < 93%, and/or >= 50% lung involvement on imaging. Depending on the need for critical care, patients were retrospectively assigned to ICU and non-ICU groups. Demographic, clinical, and laboratory parameters were collected at admission and analyzed by classification and regression tree (CRT). Result(s): Forty-five patients were admitted to ICU and 80% of them were men older than 57.13 +/- 12.80 years on average. The leading comorbidity in ICU patients was hypertension. The CRT revealed that direct bilirubin (DB) > 0.315 mg/dl together with the neutrophil-to-monocyte ratio (NMR) > 15.90 predicted up to correctly in 92% of the patients the requirement of intensive care management, with sensitivity of 93.2%. Preexisting comorbidities did not influence on the tree growing. Conclusion(s): At hospital admission, DB and NMR can help identify nine in 10 patients with COVID-19 at higher risk of ICU admission.Copyright © 2022 Sociedad Medica del Hospital General de Mexico.

Predictors of severity in COVID-19

Background: In the context of home monitoring of severe acute respiratory syndrome coronavirus-2 disease (COVID-19) patients, it is imperative to evaluate the accuracy of finger pulse oximetry oxygen saturation (SpO2) in the assessment of hypoxia. Methods: Retrospective data analysis was performed on (n = 132) hospitalised COVID-19 patients with various levels of severity, in whom SpO2, haematological, biochemical and arterial blood gas (ABG) parameters were measured within 48 h after admission. Discrepancy between SpO2 and arterial blood oxygen saturation SaO2 was compared between mild, moderate and severe COVID-19 to assess the accuracy of finger pulse oximetry. Results: We found that total white blood cell count, neutrophil %, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, ferritin, C-reactive protein and lactate dehydrogenase (LDH) were significantly increased in severe COVID-19, while lymphocyte % was significantly less when compared to mild and moderate cases. Multivariable analysis suggested that red cell distribution width (RDW) and LDH together account for significant variance in the severity of disease. The SpO2 and SaO2 were significantly less in the severe group. The difference between SpO2 and SaO2 has a clinically meaningful albeit statistically nonsignificant trend with the discrepancy greater in severe COVID-19 cases when compared to mild and moderate cases. Conclusions: Finger pulse oximetry has the potential to underestimate the severity of hypoxia in severe COVID-19 and this has implications in the decision to start oxygen therapy. RDW and LDH constitute the best parsimonious set of variables to predict severity.

Disease X Testing: The results of an international external quality assessment exercise

The United Nations Secretary-General Mechanism (UNSGM) for investigation of the alleged use of chemical and biological weapons is the only established international mechanism of this type under the UN. The UNGSM may launch an international investigation, relying on a roster of expert consultants, qualified experts, and analytical laboratories nominated by the member states. Under the framework of the UNSGM, we organized an external quality assurance exercise for nominated laboratories, named the Disease X Test, to improve the ability to discover and identify new pathogens that may cause possible epidemics and to determine their animal origin. The "what-if" scenario was to identify the etiological agent responsible for an outbreak that has tested negative for many known pathogens, including viruses and bacteria. Three microbes were added to the samples, Dabie bandavirus, Mammarenavirus, and Gemella spp., of which the last two have not been taxonomically named or published. The animal samples were from Rattus norvegicus, Marmota himalayana, New Zealand white rabbit, and the tick Haemaphysalis longicornis. Of the 11 international laboratories that participated in this activity, six accurately identified pathogen X as a new Mammarenavirus, and five correctly identified the animal origin as R. norvegicus. These results showed that many laboratories under the UNSGM have the capacity and ability to identify a new virus during a possible international investigation of a suspected biological event. The technical details are discussed in this report.Copyright © 2022

Detection and Classification of Pneumonia Using Deep Learning by the Dense Net-121 Model

A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.

Design of a cost-effective diagnosis tool for SARSCov-2 variant detection through a next generation sequencing (NGS) based strategy

Background/Objectives: Current pandemic situation, together with the continuous emergence of new SARS-CoV-2 variants reveal the need to develop a more versatile tool than PCR-based methods that allows both high throughput COVID-19 diagnostic and specific variant detection at reduced cost and fast turnaround times. Thus, with the aim of overcoming current test limitations and providing a strategy with these characteristics arises our novel next generation sequencing based approach. Method(s): The developed strategy works with RNA samples obtained from nasopharyngeal swabs. RNA samples are processed with our custom laboratory protocol and can be sequenced with any Illumina platform to generate results within a 24h timeframe. A tailored bioinformatic pipeline analyzes the data and generates a clinical-level report. Result(s): Clinical validation results have shown that the designed solution, sensitively and specifically identifies negative and positive samples that display a broad range in viral loads and readily identifies the following major SARS-CoV-2 variants of concern (VoC): Alpha, Beta, Gamma, Delta, Lambda and Omicron (BA.1 and BA.2). Conclusion(s): The versatility of our solution allows the capability of identifying the presence of other common respiratory viruses as well as identifying patients at risk through the identification of susceptibility human variants in the host. This, together with the possibility of easily adding new VoC as they emerge, will make VoC monitoring in entire populations feasible, providing a new perspective on the application of NGS methods in the field of clinical microbiology.

Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

Evaluation of Lung Ultrasound in Assessment of Critically Ill patients in Respiratory Intensive Care Unit

Reaching a proper diagnosis for critically ill patients is like collecting pieces of puzzle and bed side lung ultrasound (LUS) becomes a crucial piece complementary to clinical and laboratory pieces. It is a bed side, real time tool for diagnosis of patients in ICU who are critical to be transferred to radiology unit especially in Covid-19 pandemic with risk of infection transmission. The aim was to evaluate the accuracy of lung ultrasound in assessment of critically ill patients admitted to Respiratory Intensive Care Unit (RICU), moreover to assess its diagnostic performance in different pulmonary diseases as compared to the gold standard approach accordingly. This observational prospective (cross sectional) study with a total 183 patients who met the inclusion criteria,were selected from patients admitted at the RICU;Chest Department, Zagazig University Hospitals, during the period from September 2019 to September 2021. LUS examination was performed to diagnose the different pulmonary diseases causing RF. All cases were examined by LUS on admission. From a total 183 patients, 111 patients 60.7% were males and 72 patients 39.3% were females, with a mean age of 56+/-12.77 years, 130 patients were breathing spontaneously received conservative management with O2 therapy, 32 patients needed NIV while 21 patients needed IMV with ETT. Exacerbated COPD was the most common disease finally diagnosed followed by bacterial pneumonia, exacerbated ILD, post Covid-19 fibrosis and pulmonary embolism in32, 29,27, 19 and 11 patients respectively with corresponding diagnostic accuracy of LUS 97.3%, AUC=0.943, 93.9% (AUC=0.922), 96.7%(AUC=0.920), 97.8%, AUC=0.895, and 97.8% respectively, while Covid-19 pneumonia was the final diagnosis in 8 patients with LUS diagnostic accuracy of 97.8% (AUC=0.869) with no statistical significant difference p-value=0.818 with bacterial pneumonia in distribution of US profiles. A profile was the commonest detected US profile among the studied patients followed by B profile, C profile, A/B profile and A' profile in 37.2%, 24.6%, 15.8% 4.9%, and 3.8% of cases respectively. Bed side LUS has a reliable, valuable diagnostic performance when integrated with clinical and laboratory data for the diagnosis of most pulmonary diseases in RICU.Copyright © 2023, Colegio de Farmaceuticos de la Provincia de Buenos Aires. All rights reserved.

Covid19 Disease Assessment Using CNN Architecture

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not. © 2023 IEEE.

SARS-CoV-2 Detection by Extraction-free qRT-PCR for Massive and Rapid COVID-19 Diagnosis During a Pandemic

Introduction/Objective COVID-19 pandemic severely impacted the healthcare and economy on a global scale. It is widely recognized that mass testing is an efficient way to contain the spread of SARS-CoV-2 infection as well as aid in the development of informed policies for disease management. Here we optimized two different protocols for qRT- PCR with direct samples and systematically compared them with the laboratory standard qRT-PCR detection assay. Methods/Case Report RNA samples from 270 subjects collected in two phases at 2020-2021. The groups consisted from positive (n = 240) and negative (n = 30) samples. We compared the performance of qRT-PCR in direct heat- inactivated (95 °C for 5 min, H), heat-inactivated and pelleted (95 °C for 5 min and centrifuged for 10 min at 12,000 g, HC) against standard laboratory protocol for SARS-CoV-2 qRT-PCR (targeting ORF1ab and N genes). Accuracy, sensitivity, and specificity for PCR assays were calculated using caret and epiR packages available in the R software environment for statistical computing. The Wilcoxon matched rank test was used to compare differences in Ct values. Results (if a Case Study enter NA) Our study suggests that HC samples show higher accuracy for SARS-CoV-2 detection PCR assay compared to direct H (89 % (95 % CI: 80–95 %) vs 83 % (95 % CI: 74–91 %) of the detection in RNA). The median ΔCt was lower by 1.55 and 2.29 cycles (Wilcoxon signed-rank test p = 0.0018 and < 0.0001 for ORF1ab and N genes, accordingly) in HC samples compared to H samples. Conclusion Our results suggest that purified RNA provides more accurate results;heat-inactivated and pelleted sample testing with qRT-PCR showed a slight drop in accuracy. However, the latter could also help to significantly increase testing capacity. Switching to the direct sample testing is justified if the number of tests is doubled at least.

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.