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Aim: The process of biomarker discovery is being accelerated with the application of artificial intelligence (AI), including machine learning. Biomarkers of diseases are useful because they are indicators of pathogenesis or measures of responses to therapeutic treatments, and therefore, play a key role in new drug development. Proteins are among the candidates for biomarkers of rectal cancer, which need to be explored using state-of-the-art AI to be utilized for prediction, prognosis, and therapeutic treatment. This paper aims to investigate the predictive power of Ras homolog family member B (RhoB) protein in rectal cancer. Methods: This study introduces the integration of pretrained convolutional neural networks and support vector machines (SVMs) for classifying biopsy samples of immunohistochemical expression of protein RhoB in rectal-cancer patients to validate its biologic measure in biopsy. Features of the immunohistochemical expression images were extracted by the pretrained networks and used for binary classification by the SVMs into two groups of less and more than 5-year survival rates. Results: The fusion of neural search architecture network (NASNet)-Large for deep-layer feature extraction and classifier using SVMs provided the best average classification performance with a total accuracy = 85%, prediction of survival rate of more than 5 years = 90%, and prediction of survival rate of less than 5 years = 75%. Conclusions: The finding obtained from the use of AI reported in this study suggest that RhoB expression on rectal-cancer biopsy can be potentially used as a biomarker for predicting survival outcomes in rectal-cancer patients, which can be informative for clinical decision making if the patient would be recommended for preoperative therapy.

Quick, early, and precise detection is important for diagnosis to control the spread of COVID-19 infection. Artificial Intelligence (AI) technology could certainly be used as a modulating tool to ease the detection, and help with the preventive steps further. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many visual recognition tasks. Nevertheless, most of these state-of-the-art networks highly rely on the availability of a high amount of labeled data, being an essential step in supervised machine learning tasks. Conventionally, this manual, mundane, and time-consuming process of annotating images is done by humans. Learning to localize or detect COVID-19 infection masks in our specific case study typically requires the collection of CT scan data that has been labeled with bounding boxes or similar annotations, which generally is limited. A technique that could perform such learning with much less annotations, and transfer the learned proposals that are algorithm-driven to generate more synthetic annotated samples would be helpful & quite valuable. We present such a technique inspired by weakly trained mask region based convolutional neural networks (R-CNN) architecture for localization, in which the number of images with their pixel-level masks can be a small proportion of the total dataset, and then further improvise CNNs by inversely generating dense annotations on-the-go using an algorithmic-based computational approach. We focus on alleviating the bottleneck associated with deep learning models needing annotated data for training in an intuitive reverse engineering fashion through this work. Our proposed solution can certainly provide the prospect of automated labeling on-the-fly, thereby reducing much of the manual work. As a result, one can quickly train a precise COVID-19 infection detector with the leverage of autonomous frame-by-frame machine generated annotations. The model achieved mean precision accuracy (%) of 0.99, 0.931, and 0.8 for train, validation, and test set, respectively. The results demonstrate that the proposed method can be adopted in a clinical setting for assisting radiologists, and also our fully autonomous approach can be generalized to any detection/recognition tasks at ease.

Cancer biomarkers play a very important role for the disease diagnosis, prognosis, and prediction of treatment response. In this study, expression levels of RhoB and DNp73 proteins in rectal cancer captured on immunohistochemical images were analyzed for predicting the 5-year survival time of two patient cohorts with and without preoperative radiotherapy. Applications of deep convolutional neural networks in medicine have been increasingly attracting attention in clinical cancer research. The success is mainly due to the power of extracting deep features of images that are very useful for machine learning. Another successful convolution-based method for feature extraction at multiple levels is wavelet scattering networks. This study combines the potential of these two convolution-based methods for robust image feature extraction of protein expression. The proposed approach was tested with samples of various tissue types (tumor, biopsy, metastasis, and normal condition adjacent to tumor). Statistical performance measures show very high rates in terms of prediction, classification and model accuracies, precision, and area under the receiver operating characteristic curve. The results suggest the usefulness of the double convolutional learning for assisting clinical researchers in timely validating and discovering cancer biomarkers.

Background: Over a decade, tissues dissected adjacent to primary tumors have been considered "normal " or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. Methods: This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. Results: Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. Conclusion: Preliminary results not only add objective evidence to recent findings of NATs molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. Clinical impact: The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

We report the genome sequence of bacteriophage NathanVaag, an actinobacteriophage isolated from soil in El Paso, Texas, that infects

Aims: This study investigates an unsupervised deep learning-based feature fusion approach for the detection and analysis of COVID-19 using chest X-ray (CXR) and Computed tomography (CT) images. Background: The outbreak of COVID-19 has affected millions of people all around the world and the disease is diagnosed by the reverse transcription-polymerase chain reaction (RT-PCR) test which suffers from a lower viral load, and sampling error, etc. Computed tomography (CT) and chest X-ray (CXR) scans can be examined as most infected people suffer from lungs infection. Both CT and CXR imaging techniques are useful for the COVID-19 diagnosis at an early stage and it is an alternative to the RT-PCR test. Objective: The manual diagnosis of CT scans and CXR images are labour-intensive and consumes a lot of time. To handle this situation, many AI-based solutions are researched including deep learning-based detection models, which can be used to help the radiologist to make a better diagnosis. However, the availability of annotated data for COVID-19 detection is limited due to the need for domain expertise and expensive annotation cost. Also, most existing state-of-the-art deep learning-based detection models follow a supervised learning approach. Therefore, in this work, we have explored various unsupervised learning models for COVID-19 detection which does not need a labelled dataset. Methods: In this work, we propose an unsupervised deep learning-based COVID-19 detection approach that incorporates the feature fusion method for performance enhancement. Four different sets of experiments are run on both CT and CXR scan datasets where convolutional autoencoders, pre-trained CNNs, hybrid, and PCA-based models are used for feature extraction and K-means and GMM techniques are used for clustering. Results: The maximum accuracy of 84% is achieved by the model Autoencoder3-ResNet50 (GMM) on the CT dataset and for the CXR dataset, both Autoencoder1-VGG16 (KMeans and GMM) models achieved 70% accuracy. Conclusion: Our proposed deep unsupervised learning, feature fusion-based COVID-19 detection approach achieved promising results on both datasets. It also outperforms four well-known existing unsupervised approaches.

The complexity in the styles of 1200 Byzantine icons painted between 13th and 16th from Greece, Russia and Romania was investigated through the Kolmogorov algorithmic information theory. The aim was to identify specific quantitative patterns which define the key characteristics of the three different painting schools. Our novel approach using the artificial surface images generated with Inverse FFT and the Midpoint Displacement (MD) algorithms, was validated by comparison of results with eight fractal and non-fractal indices. From the analyzes performed, normalized Kolmogorov compression complexity (KC) proved to be the best solution because it had the best complexity pattern differentiations, is not sensitive to the image size and the least affected by noise. We conclude that normalized KC methodology does offer capability to differentiate the icons within a School and amongst the three Schools.

Personalised 3D face creation has always been a hot topic in the computer vision community. Many methods have been proposed including the statistic model, the non-rigid registration and high-end depth acquisition equipment. However, in practical applications, those existing methods still have their own limitations. For example, the performance of the statistic model-based methods highly depends on the generality of the pre-trained statistic model; the non-rigid registration based methods are sensitive to the quality of input data; the high-end equipment-based methods are less able to be popularised due to the expensive equipment costs; the deep learning-based methods can only perform well if proper training data provided for the target domain, and require GPU for better performance. To this end, this paper presents an adaptive template augmented method that can automatically obtain a personalised 4D facial modelling only using a consumer-grade device. The noisy data from such a cheap device are well handled. The whole process consists of a series of linear solutions and can be achieved in real-time for online processing only based on the CPU computation on a laptop. There is no constraint nor complex operation required by the proposed method. No additional time-consumptive pre- or post-processing for the personalisation is needed. Comparisons against several existing methods demonstrate the superiority of the proposed method.

Lung cancer causes the most cancer deaths worldwide and has one of the lowest five-year survival rates of all cancer types. It is reported that more than half of patients with lung cancer die within one year of being diagnosed. Because mediastinal lymph node status is the most important factor for the treatment and prognosis of lung cancer, the aim of this study is to improve the predictive value in assessing the computed tomography (CT) of mediastinal lymph-node malignancy in patients with primary lung cancer. This paper introduces a new method for creating pseudo-labeled images of CT regions of mediastinal lymph nodes by using the concept of recurrence analysis in nonlinear dynamics for the transfer learning. Pseudo-labeled images of original CT images are used as input into deep-learning models. Three popular pretrained convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201) were used for the implementation of the proposed concept for the classification of benign and malignant mediastinal lymph nodes using a public CT database. In comparison with the use of the original CT data, the results show the high performance of the transformed images for the task of classification. Three pretrained convolutional neural networks that are AlexNet, SqueezeNet, and DenseNet201 were trained and tested with the transformed images. Classification accuracies and areas under the receiver operating characteristic curve obtained from the ten-fold cross-validation are 93% and 0.97, 96% and 0.99, and 100% and 1 for the SqueezeNet, AlexNet, and DenseNet201, respectively. The proposed method has the potential for differentiating benign from malignant mediastinal lymph nodes on CT, and may provide a new way for studying lung cancer using radiology imaging.

The purpose of this study was to investigate and evaluate the effectiveness of phytotherapy on a severe and complicated Immune Thrombocytopenia (ITP) patient who had failed with conventional treatments. A male patient presented with clinical symptoms of ITP and had been treated with Corticosteroids, Azathioprine, Eltrombopag, and platelet transfusions for over three years. The patient had an initial response but later developed severe complications, including hydrothorax, gastric pain, hematuria, and digestive hemorrhage, and no further response to treatment. The patient then received Phytotherapy for 17 months which significantly improved the clinical symptoms, platelet counts, and laboratory tests. Despite his active lifestyle, the patient was symptom-free with platelet counts ranging from 109 to 132×10

In this article, we propose a parallel hierarchy convolutional neural network (PHCNN) combining a Long Short-Term Memory (LSTM) network structure to quantitatively assess the grading of facial nerve paralysis (FNP) by considering the region-based asymmetric facial features and temporal variation of the image sequences. FNP, such as Bell's palsy, is the most common facial symptom of neuromotor dysfunctions. It causes the weakness of facial muscles for the normal emotional expression and movements. The subjective judgement by clinicians completely depends on individual experience, which may not lead to a uniform evaluation. Existing computer-aided methods mainly rely on some complicated imaging equipment, which is complicated and expensive for facial functional rehabilitation. Compared with the subjective judgment and complex imaging processing, the objective and intelligent measurement can potentially avoid this issue. Considering dynamic variation in both global and regional facial areas, the proposed hierarchical network with LSTM structure can effectively improve the diagnostic accuracy and extract paralysis detail from the low-level shape, contour to sematic level features. By segmenting the facial area into two palsy regions, the proposed method can discriminate FNP from normal face accurately and significantly reduce the effect caused by age wrinkles and unrepresentative organs with shape and position variations on feature learning. Experiment on the YouTube Facial Palsy Database and Extended CohnKanade Database shows that the proposed method is superior to the state of the art deep learning methods.

RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. RhoB expression is known to have implications in pathological processes of diseases. Investigation in the regulation and communication of this protein detected by immunohistochemical staining on the microscope is worth exploring to gain insightful information that may lead to identifying optimal disease treatment options. In particular, the role of RhoB in rectal cancer is not well-discovered. Here, we report that methods of deep learning-based image analysis and the decomposition of multiway arrays discover the predictive factor of RhoB in two cohorts of rectal-cancer patients having survival rates of less and more than 5 years. The analysis results show distinctions between the tensor-decomposition factors of the two cohorts.

Malware distribution to the victim network is commonly performed through file attachments in phishing email or from the internet, when the victim interacts with the source of infection. To detect and prevent the malware distribution in the victim machine, the existing end device security applications may leverage techniques such as signature or anomaly-based, machine learning techniques. The well-known file formats Portable Executable (PE) for Windows and Executable and Linkable Format (ELF) for Linux based operating system are used for malware analysis, and the malware detection capabilities of these files has been well advanced for real-time detection. But the malware payload hiding in multimedia using steganography detection has been a challenge for enterprises, as these are rarely seen and usually act as a stager in sophisticated attacks. In this article, to our knowledge, we are the first to try to address the knowledge gap between the current progress in image steganography and steganalysis academic research focusing on data hiding and the review of the stegomalware (malware payload hiding in images) targeting enterprises with cyberattacks current status. We present the stegomalware history, generation tools, file format specification description. Based on our findings, we perform the detail review of the image steganography techniques including the recent Generative Adversarial Networks (GAN) based models and the image steganalysis methods including the Deep Learning(DL) models for hiding data detection. Additionally, the stegomalware detection framework for enterprise is proposed for anomaly based stegomalware detection emphasizing the architecture details for different network environments. Finally, the research opportunities and challenges in stegomalware generation and detection are also presented.

Studying the dynamics of nanostructures in the intracellular space is important because it allows gaining insights into the mechanism of complex biological functions of organelles. Understanding such dynamical processes can contribute to the development of nanomedicine for the diagnosis and treatment of many diseases caused by the interaction of multiple genes and environmental factors. Here a quantitative measure of spatial-temporal dynamics of nanostructures within a cell line in the context of nonlinear dynamics is introduced, where early endosomes, late endosomes, and lysosomes recorded by time-lapse confocal imaging are examined. The mathematical derivation of the proposed technique is based on the concept of recurrence dynamics and sequential rate of change over time. The quantification introduced as fuzzy recurrence exponents can be generalized for characterizing the dynamics of experimental evolutions in other nanostructures of living cells captured under the optical microscope.

Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.

The concept of networks in the context of graph theory delineates a wide variety of real-life complex systems. The theory of networks finds its applications very useful in many scientific and intellectual domains. Weighted networks can characterize complex statistical graph properties, particularly where node connections are heterogeneous. A framework of fuzzy weighted recurrence networks of time series is presented in this letter. Popular graph measures including the average clustering coefficient and characteristic path length of fuzzy weighted recurrence networks are shown to be more robust than those of unweighted recurrence networks derived from binary recurrence plots.

Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

The mechanism underlying the complex behavior of major or clinical depression has been hypothesized as a dynamical system. This study presents an analysis of mood self-assessment in major depression using the method of fuzzy cross recurrence plots and tensor decomposition. The relationships between positive and negative mental states can be visualized and quantified using the proposed approach. The discovery of cross recurrences or dynamic correlations of the two opposite mental states can be helpful for targeted therapy in the management of personalized depression either under or without antidepressant drugs.

This research utilizes social knowledge extracted from user-generated content from Twitter to identify the social media users’ concerns on different domains of innovation regarding Sustainable Development Goals (SDGs) during 4 years, whereby understanding the emotional expression of public opinion upon those SDGs innovation dimensions. Topic analysis with latent semantic approach is the most suitable approach for exploring topics of interest from large text corpus; while sentiment analysis using Python-based library Vader is effective to investigate the Twitter users’ sentiment underneath those extracted topics. The importance of gender equality and youth empowerment on innovation; and the innovation in sustainable agriculture, education, eco-friendly materials, green energy, and economic development are intensively discussed topics. The component-terms of corresponding topics in different years are further scrutinized to highlight the evolution of Twitter users’ concerns on a particular innovation dimension. The result of sentiment analysis suggests the predominance of “very positive” and “positive” sentiment in almost topics during 4 years. The “neutral” sentiment prevails in a certain topic that generally addresses many angles of SDGs innovation in its majority of tweets without focusing on any specific dimensions. The high of “negative” sentiment during 4 years is noticed in the agriculture innovation topic in 2018.

Tuberculosis (TB) is an infectious disease that remained as a major health threat in the world. The computer-aided diagnosis (CAD) system for TB is one of the automated methods in early diagnosis and treatment, particularly used in developing countries. Literature survey shows that many methods exist based on machine learning for TB classification using X-ray images. Recently, deep learning approaches have been used instead of machine learning in many applications. This is mainly due to the reason that deep learning can learn optimal features from the raw dataset implicitly and obtains better performances. Due to the lack of X-ray image TB datasets, there are a small number of works on deep learning addressing the image-based classification of TB. In addition, the existing works can only classify X-ray images of a patient as TB or Healthy. This work presents a detailed investigation and analysis of 26 pretrained convolutional neural network (CNN) models using a recently released and large public database of TB X-ray. The proposed models have the capability to classify X-ray of a patient as TB, Healthy, or Sick but non-TB. Various visualization methods are adopted to show the optimal features learnt by the pretrained CNN models. Most of the pretrained CNN models achieved above 99% accuracy and less than 0.005 loss with 15 epochs during the training. All 7 different types of EfficientNet (ENet)-based CNN models performed better in comparison to other models in terms of accuracy, average and macro precision, recall, F1 score. Moreover, the proposed ENet-based CNN models performed better than other existing methods such as VGG16 and ResNet-50 for TB classification tasks. These results demonstrate that ENet-based models can be effectively used as a useful tool for TB classification.

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.

Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

The method of fuzzy recurrence plots has been introduced as an extension of the method of recurrence plots to display recurrences of the reconstructed phase-space trajectories of dynamical systems as grayscale images. This paper presents some quantifiable measures of fuzzy recurrence plots in the context of the recurrence quantification analysis.

Major depressive disorder or clinical depression is a mental disorder characterized by daily low moods, which occur across many situations. Individuals suffering from depression are typically treated with counseling and antidepressant medication. This paper presents a computing approach for visualizing the dynamics of pairwise interactions of moods in personalized depression under and without medication. The methods of fuzzy cross recurrence plots of time series and their tensor decomposition offer a new way for gaining insight into the causality of the complex behavior of depression and its treatment.

This paper presents the development of a non-invasive image-analysis method for improving the diagnostic accuracy of mediastinal lymph node metastasis in patients with lung cancer. The approach adopts pretrained deep learning models incorporated with the geostatistical simulation of texture of benign and metastatic lung lymph nodes in computed tomography (CT) images for classification. Using 271 CT samples of mediastinal lymph nodes collected from 148 patients with lung cancer, deep-learning models coupled with the stochastic simulated data augmentation provide the best classification results. The simulation of texture in these medical images was able to discover rich radiomic features to ascertain subtle difference between benign and metastatic lymph nodes and enhance the performance of the deep-learning models for complex pattern classification. The proposed approach is very promising to be utilized as a computerized tool for medical image analysis.

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.