Crispr biosensing and Ai driven tools for detection and prediction of Covid-19

Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Electrochemical lateral-flow device for rapid COVID-19 antigen-diagnostic testing.

Antigen test kits (ATK) are extensively utilized for screening and diagnosing COVID-19 because they are easy to operate. However, ATKs exhibit poor sensitivity and cannot detect low concentrations of SARS-CoV-2. Herein, we present a new, highly sensitive, and selective device obtained by combining the principle of ATKs with electrochemical detection for COVID-19 diagnosis, which can be quantitatively assessed using a smartphone. An electrochemical test strip (E-test strip) was constructed by attaching a screen-printed electrode inside a lateral-flow device to exploit the remarkable binding affinity of SARS-CoV-2 antigen to ACE2. The ferrocene carboxylic acid attached to SARS-CoV-2 antibody acts as an electroactive species when it binds to SARS-CoV-2 antigen in the sample before it flows continuously to the ACE2-immobilization region on the electrode. Electrochemical-assay signal intensity on smartphones increased proportionally to the concentration of SARS-CoV-2 antigen (LOD = 2.98 pg/mL, under 12 min). Additionally, the application of the single-step E-test strip for COVID-19 screening was demonstrated using nasopharyngeal samples, and the results were consistent with those obtained using the gold standard (RT-PCR). Therefore, the sensor demonstrated excellent performance in assessing and screening COVID-19, and it can be used professionally to accurately verify diagnostic data while remaining rapid, simple, and inexpensive.

Diagnosis of Covid-19 from Chest X-Ray Images Using Wavelet-Based Depth Wise Convolution Network

There is a great need to create and put in place a method of automatic detection as a substitute for conventional diagnosis for COVID-19 detection that can be employed on a commercialscale because there aren't as many COVID-19 test kits availablein medical institutions. In particular, chest X-Ray scans can beexamined to assess whether a patient has COVID. Due to the availability of numerous big annotated picture datasets, convolutional neural networks have achieved remarkable success in image analysis and classification. Input is obtained in the form of chest x-rays images. Output results are acquired instantly in real-time which predicts if the person suffers from Covid or not. Modern technique use the RCNN algorithm, which makes them less precise and time-consuming. We suggest an automated deep learning-base method for extracting COVID-19 from chest X-ray pictures. For analysing the chest X-Ray pictures, suggested method offers enhanced depth-wise convolution neural network. Through wavelet decomposition, multiresolution analysis is incorporatedinto the network. In order to identify the condition, the network is given the frequency sub-bands that were recovered from the input pictures. The network's goal is to determine whether the input image belongs to the Covid-19 class or not. The Advantage of the proposed system are that it could be the very first-of its kind, cost-efficient, and highly accurate application that provide complete and accurate covid - 19 diagnosis. © 2022 IEEE.

Information Systems in Medical Settings: A Covid-19 Detection System Using X-Ray Scans

Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better. © 2022 IEEE.

A Framework for Bang Saen Safe Food Avenue Management System

The coronavirus (COVID-19) epidemic has had a significant impact on people's lives and businesses. After the epidemic situation, Saensuk Sub-district, Chonburi is one area that has a need to change its tourism management. Saen Suk Municipality launched a campaign to promote Bang Saen as a "Safe Food Avenue". The pilot project began with production of an application to support a special test for formalin (FA) in sea foods which shows the result to food shops and tourists online. The FA test kit was produced and approved with accurate test results verified by a team of specialists from Burapha university. The application was developed using a responsive website technique. Specialists used the system to upload images of FA test results. Then a machine learning in image processing technique was used to analyze the test results. The Server sends the result through a RESTFUL API in JSON format to the application so that users can see the results online. The experiment using Circular Hough Transform (CHT) algorithm to detect circular shape in two-dimensional space by voting in Hough parameter with 400 data records for training. Based on the FA test result dataset, training accuracy are 100%. The SafeFoodAvenue mobile application using responsive technology FA test result's dataset accuracy is 100%. © 2022 IEEE.

Accelerating COVID-19 testing: An experimental approach.

The COVID-19 pandemic, ever since its global outbreak in 2020, has continued to wreak havoc. Governments across the world were compelled to enforce strict nation-wide lockdowns, while emphasising on social distancing and quarantining suspected people in order to slow down the spread of the virus. During this time, there was a massive increase in demand for COVID-19 test kits. However, given the limited supply, countries were finding it hard to test enough people. This study proposes an approach called Encoded Blending (EB) to increase the number of tests drastically, without increasing the number of test kits. EB modifies the pooled testing method; this has been followed by countries like Germany, Israel and South Korea for mass testing their citizens. EB has the potential to reduce test kits requirement by up to 85% and 80% in a population with 5% and 10% affected cases, respectively.

Hybrid Transfer Learning with ECOC Ensemble Configuration for COVID-19 CXR Detection

Coronavirus Disease 2019 (COVID-19) is a viral pneumonia that causes symptoms in the lungs of those infected. The presence of the symptoms must be diagnosed as soon as possible. If no test kits are available, the next best alternative is a computer-aided diagnostic of a patient's chest X-ray scan for a quick and accurate diagnosis. This paper proposes a hybrid transfer learning method with Error-Correction Output Codes (ECOC) by combining networks including GoogLeNet, ResNet-18, and ShuffleNet for feature extraction. X-ray input data are collected from open-source repositories. In this implementations, Support Vector Machine (SVM) as the base classifier. The proposed network attempts to categorize the input data into one of three categories: COVID-19, healthy, and non-COVID-19 pneumonia. The mean accuracy of our method is 96.21%, compared fine tuning existing pre-trained model which yielded 89.1% for GoogLeNet, 88.95% for ResNet-18, and 89.31% for ShuffleNet. © 2022 IEEE.

Deep Learning Methods for Early Detection of Monkeypox Skin Lesion

As the world has not fully recovered from the aftermath of COVID-19, a new pandemic appears on the horizon. Monkey Pox is emerging as a new threat to the health of the world population. With the recorded spread over 40 countries worldwide it might be soon declared a pandemic. Monkey Pox shares common features with chickenpox and measles making it difficult to diagnose. Developing a new test kit at this early stage is a challenging task for the medical fraternity. This paper proposes the use of deep learning models that can be used to make the process of diagnosis automated. This paper tries to come up with a performance comparison of ResNet50, EfficientNetB3 and EfficientNetB7 algorithms. This study suggests a method for early detection of Monkey Pox Skin Lesion. Though an extensive study with other models on a larger dataset containing more images from various countries of the world needs to be carried out but this study gives some promising results on this limited dataset. © 2022 IEEE.

The Development of Mobile Application for Assisting COVID-19 Antigen Test Kit Results Reading

The antigen test kits or ATKs have been widely used for screening COVID-19 infections because they can detect and give the results quickly and can be done easily by untrained patients. However, reading ATK test results could be difficult for some people and may lead to misinterpretations of the test results. This paper presents a preliminary study for developing a mobile application for helping in reading the results of the COVID-19 ATKs from an image using algorithms based on the YOLO object detection. The results are classified into 3 classes, negative, positive, and invalid. The negative and the invalid results are further refined by using the distances between the visible line and the letters on the test cassette. Experiments were conducted to test the efficiency and accuracy of the developed model with a mean of average precision or mAP of 0.986 and an F1 score of 0.970. The model was developed and put into a prototype mobile application using tools that support cross-platform technology. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).

Knowledge, Attitude and Perspective of community pharmacists in the sale of COVID-19 self-test kits in Ipoh, Perak

Introduction: Malaysia experienced a surge in the number of active COVID-19 cases. As a result, the government came out with several measures and standard operating procedures to manage the COVID-19 pandemic. One of the most significant measures is by allowing the sale of COVID-19 self-test kits. This enables the public to do a self-test when they are close contacts or exhibiting symptoms. It enables immediate self-quarantine when found positive. This will restrict the spread of the COVID- 19 virus. Community pharmacies around the country have been in the forefront in selling the COVID-19 self-test kits. Their accessibility and role in counselling has made community pharmacists as an important figure in selling and counselling the public on the sale and use of COVID-19 self-test kits. Objective(s): The objective of the study is to evaluate the knowledge, attitude and perspectives of community pharmacists in the sale of COVID-19 self-test kits in Ipoh, Perak. Material(s) and Method(s): A cross-sectional survey study design was used to conduct this study. It was carried out via an online structured questionnaire distributed among the community pharmacists in Ipoh, Perak. 62 community pharmacists in Ipoh responded to this survey. Result(s) and Conclusion(s): It was found that 88.71% of the respondents have a good knowledge about the COVID-19 self-test kits. Around 58% of them portrayed a moderate attitude while selling the COVID-19 self-test kits, which included the demonstration and counselling. Whereas 58.2% of the community pharmacists showed moderate level of perspective while selling COVID-19 self-test kits sales. There is a need for the community pharmacists to undergo more training on COVID-19 self-test kits to improve their level of attitude and perspective when they sell the kits to the public. This will improve the management of the COVID-19 pandemic in the country.

COVID-19 Detection Method Based on Attentional Mechanism and LSTM

Since 2020, the Novel Coronavirus virus, which can cause upper respiratory and lung infections and even kill in severe cases, has been ravaging the globe. Rapid diagnostic tests have become one of the main challenges due to the severe shortage of test kits. This article proposes a model combining Long short-term Memory (LSTM) and Convolutional Block Attention Module for detection of COVID-19 from chest X-ray images. In this article, chest X-ray images from the COVID-19 radiology standard data set in the Kaggle repository were used to extract features by MobileNet, VGG19, VGG16 and ResNet50. CBAM and LSTM were used for classifcation detection. The simulation results showed that the experimental results showed that VGG16–CBAM–LSTM combination was the best combination to detect and classify COVID-19 from chest X-ray images. The classification accuracy of VGG-16-CBAM-LSTM combination was 95.80% for COVID-19, pneumonia and normal. The sensitivity and specificity of the combination were 96.54% and 98.21%. The F1 score was 94.11%. The CNN model proposed in this article contributes to automated screening of COVID-19 patients and reduces the burden on the healthcare delivery framework. © 2022, Springer Nature Switzerland AG.

Screening Covid-19 Infection from Chest CT Images using Deep Learning Models based on Transfer Learning

As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies. © 2022 IEEE.

Fusion of Hand-Crafted and Automatically Generated features for improving the performance of COVID-19 X- ray image classification

In December 2019, the new disease COVID-19 was initially discovered in Wuhan, China and with a fast pace, it took over the whole world. It has impacted everyone's health, as well as the global economy and people's daily lives. It has become crucial that all the positive cases be detected quickly so it is possible to save other lives. Still, the lack of doctors and the lower availability of the test kits made it an arduous task. Recent research shows that radiological imaging techniques have played a valuable role in the detection of COVID-19. The use of artificial intelligence technology with radiological images can help identify the disease very accurately. Even in remote areas, it can be beneficial to overcome the shortage of doctors. This study proposed a method based on the aggregation of the extracted hand-crafted features with the automated ones. We used a his-togram of oriented gradients (HOG) for the hand-crafted features extraction. In addition, several techniques are investigated to get the deep learned features such as "DenseNet201","Inception ResnetV2", "VGG16","VGG19", "Inception_V3", "Resnet50", "MobileNet_V2"and "Xception"out of which "VGG19"gives optimal performance. Furthermore, for dimensionality reduction and to maintain the consistency of features, "principal component analysis (PCA)"is used. Our experiments on COVID-19 image datasets revealed that the proposed method achieves 99% classification accuracy in classifying normal and pneumonia X-ray images. © 2021 IEEE.

Detection of COVID-19 from X-Ray Images using Deep Neural Networks

In Dec 2019, Coronavirus has first shown in China and since then there is a big rise in the number of these cases. On March 28, 2020, WHO (World Health Organization) tweeted and proclaimed it's an epidemic. The test kits are relatively less likely to be checked by collecting blood samples because they are easily infectious, and collecting blood samples are very time taking. But it's important to get a quick and simpler way to validate the covid-19. Lungs are getting very badly affected by the Coronavirus and it increases in lungs gradually so we have to come up with the Convolutional Neural Network (CNN). This detects Corona virus utilising X-Rays of the Chest within about few seconds. To diagnose Coronavirus from X-ray Image dataset using different Convolutional Neural Network methodologies like Mobile Net, Inception, Exception, VGG. However, the findings obtained are based on the VGG16, VGG19 model. Apply the models to the X-ray dataset this was obtained from the Kaggle source. This dataset included 100 X-ray images of the lungs(chest) of the Patients with CORONA VIRUS, and 100 X-ray images of the lungs(chest) of People who are healthy. Python language is being used to execute the COVID19 dataset and Google Collaboratory is used for coding purposes. The focus of this research is to see how successful automatically detecting COVID-19 from chest X-rays using Convolutional Neural Networks. This study shows that to detecting COVID-19, VGG16 performs better than other method. The accuracy is 96.15% using VGG16 method. The excellent achievement of these models has the potential to rapidly better the COVID-19 diagnosis performance and speed. Although, A bigger dataset of chest X-ray pictures (COVID-19 positive) are necessary while using deep transfer learning to achieve consistent, accurate and better results to detecting COVID-19 diseases. © 2021 IEEE.

COVID-19 test-kit trade and trade policy: Implications for developing countries.

At the centre of the multi-dimensional impact of the COVID-19 pandemic, the shortage of medical supplies in countries with weaker healthcare systems significantly reduced the effectiveness of national and international public health interventions. Using a database of test-kit trade flows and barriers, we estimate the price responsiveness of test-kit demand in a global sample of countries. These estimates allow us to investigate the degree to which import tariffs by leading producers could result in a disruption in global supply chains, price increases, and welfare loss. Simulation experiments indicate that the combination of rising demand for test kits and import dependence magnifies the impact of trade barriers on consumer welfare and this impact is more profound for developing countries.

Optimize Path Planning for UAV COVID-19 Test Kits Delivery System by Hybrid Reinforcement Learning

This paper aims to solve an optimization problem in the UAV-enabled COVID-19 test kits delivery system. The UAV intends to find the optimal path to deliver the COVID-19 test kits to people with a high probability of COVID-19 infection in the shortest time. The traditional Deep Reinforcement Learning doesn't perform well in solving the optimization problem because of the slow converging speed and difficult parameters-tuning. In order to solve this problem efficiently, a low-complexity Hybrid Reinforcement Learning is proposed. The algorithm consists of a heuristic algorithm and a Q Learning algorithm. At first, a heuristic algorithm is utilized to calculate the optimal path between any two users. Next, Q learning is applied to determine the sequence of the users to deliver the COVID-19 test kits. As a result, both the delivery sequence and the specific path from one user to another can be generated. The simulation results prove the superiority of the proposed Hybrid Reinforcement Learning in solving the proposed optimization problem compared with the state-of-arts. © 2022 IEEE.

Detection of COVID-19 and Classification of Pneumonia Using Deep Neural Networks Algorithms

COVID-19 which is a subclass of severe acute respiratory syndrome (SARS) is a viral disease which emerged from China in 2019. At first, there are shorthand of test kits available to diagnose the COVID-19 disease. The tests available to diagnose the COVID-19 are RT-PCR (real-time polymerase chain reaction), Rapid Antigen test and Antibody test. But in these, only RT-PCR has the high accuracy, and it is a time-taking process. It takes nearly from 4 to 48 h. Here, AI plays an important role in diagnosing the disease. In the recent years, AI becomes a part of medical field and is widely used in classification. The chest X-Rays are used to detect the COVID-19 using deep learning and the model used to detect the COVID-19 is ResNet18 which is a residual network containing 18 layers. In this work, we classified four types of classes to make sure that our model performance is better and classify accurately. The data set contain a total of 5365 images. In this, we used 80% of data for the training and 20% for validation. The accuracy obtained in classification of three classes is 96.67% and for four classes, the accuracy is 91%. We have also used another model for comparison which ResNet50 and achieved an accuracy of 75%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

PANDIT: An AI Twin-Based Radiography Image-Assisted nCOVID-19 Identification and Isolation

The unprecedented rise and spread of the pandemic in form of nCOVID-19 has really raised high concerns in the socioeconomic front. The usual diagnosis is made by an RT-PCR test, which is highly specific can incorrectly identify some nCOVID-19 individuals to cause a serious compromise in overall accuracy. Since the drug application in its full swing is still some months away, hence, the need of the hour is to find a more accurate technique which can be used by health care centers having basic point of care facilities. The increase in the number of cases in India and lack of test kits in some of the less known diagnostic centers has added more concerns to the increasing problems. Additionally, the test kits incur a significant cost making it less affordable to some of the diagnostic centers. Hence, this research group in this article has proposed an algorithm centered around the concept of Internet of Things, a dual deep learning based algorithm, and collating the decision by a strong decision fusion technique. The objective of the algorithm is to detect and isolate the nCOVID-19 subjects in a cost-effective way to keep a check on the spread. This pandemic detection and isolation technique (PANDIT) is based on two different radiography image technology and uses a state-of-the-art deep learning algorithm for the purpose. The radiography technique has long been the most acceptable technique for cases related to pneumonia. The group has developed the algorithm based on X-ray and CT scan as its training data. The novelty of this paper is best described by a multi-fold methodology. Firstly, the significance of radiography imaging for detecting and identification of COVID-19 subjects. A simple connected value chain driven by Internet of Things (IoT) would enable the isolation process in an efficient and accelerated manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Predicting NCOVID-19 Probability Factor with Severity Index

Hospitals worldwide are struggling to cope up with patient’s admission issues related with the increasing number of COVID-19 patients’ cases mainly driven by Delta variant, as severely ill nCOVID patients are found waiting for hospital beds, which are occupied by non-critical COVID patients. To make the situation worse, people who are partially or fully vaccinated against COVID-19 are also getting re-infected. Due to the absence of prior knowledge of an index of severity for COVID-19 patients, hospitals, with limited number of ventilators and medical equipment, fail to admit patients on any priority basis. With multiple tests kit available in market till now, there is none with an instantaneous index for severity prediction for COVID. This research develops a free and user-friendly algorithm titled “SAHAYATA 1427” (renamed herein Sahayata) which predicts a factor for a patient having the probability of disease nCOVID-19 termed as “probability factor” of COVID-19 for each patient. Concurrently, the algorithm also provides an index for severity by which the patient is affected by nCOVID, termed as “severity index.” The input data is both demographic and patient provided. The severity index is determined using artificial intelligence. Using a logistic regression model with data set of existing COVID patients, Sahayata predicts the probability factor for an nCOVID-19 patient with an accuracy, precision and recall of 88.17%, 100% and 87.3%, respectively. Results indicate that it can be used effectively both at hospitals by trained medical personnel and at home by the general population. Sahayata helps the COVID-19 patients living in rural communities with smaller patients care facilities with limited equipment by providing a way for efficient treatment care. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of COVID-19 Using Segmented Chest X-ray

COVID-19 is quickly gaining popularity across the globe. By April 14, 2020, 128,000 individuals had been killed by COVID-19, and 1.99 million incidents had been recorded in 210 countries and regions, totaling 219.747 cases. The rapid spread of the virus throughout the globe has resulted in a severe shortage of medical test kits in many parts of the world, particularly in Africa. A chest X-ray may prove to be a more successful screening method in certain situations than thermal screening of the whole body, due to the fact that the respiratory system is the most susceptible area in a human’s body to infection. Lung segmentation is the initial stage in identifying diseases using a chest x-ray picture. We describe a method for segmenting the lung region from CXR images that is based on the Euler number thresholding approach, i. When compared to current state-of-the-art methods, the suggested method demonstrates superior accuracy and performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.