Identification of Clinical Features Associated with Mortality in COVID-19 Patients

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.

Haptically enabled robotic US

Introduction: In this research project we aim to assess the feasibility and practicability of using a robot to perform ultrasound. Especially during the current COVID-19 pandemic, robotic ultrasound when developed successfully can help us to perform ultrasound imaging of infectious patients while minimising the risk to our sonographers. Furthermore, robotic ultrasound system can reduce the musculoskeletal burden of sonographers while potentially obtaining better more even ultrasound pictures. Method: We are recruiting 60 patients to perform focused scans of the abdomen. This includes: liver, kidneys, ascites, gallbladder. Our study robot is uniquely a haptically enhanced robot, meaning it provides the sonographer (person who performs ultrasound) with force feedbacks throughout the scan making it easier to operate and safer. The research study will be conducted over a period of 3 months starting in February 2022. Results will be included at the time of presentation as we have only started the study. Conclusion: An innovative research project in which we reduce sonographer/ patient interaction in the cases of infections such as the COVID-19 pandemic.

SpinalXNet: Transfer Learning with Modified Fully Connected Layer for X-Ray Image Classification

Over the past year, COVID-19 has become a global pandemic and people across the globe have suffered a lot from this pandemic. The rate of transmitting the coronavirus in people is very quick. A rapid diagnosis can potentially help governments in identifying the pattern of transmission. There are some tests available but those tests take a long time to give the report. So, in this work, we have proposed a model that will distinguish between normal people, COVID affected people, and pneumonia affected people with the help of an X-ray. X-ray images are considered because taking an X-ray image is very little time-consuming. In this work, we have trained the X-ray images with a novel Deep Learning approach with SpinalNet architecture, and that distinguishes normal people, COVID affected people, and pneumonia affected people. After training the model we have achieved a very good accuracy for the SpinalNet architecture that is 96.12% while the traditional model provides 95.50% accuracy. We present precision, recall, and Fl scores of COVID and Pneumonia classes. We also present our results and codes with execution details. This paper contains the link to Kaggle notebooks with execution details. The applied Spinalnet transfer learning code is available in our GitHub repository: https://github.com/dipuk0506/SpinalNet

Challenges of Internet of Medical Things for Electronic Healthcare

The internet of medical things is one of the greatest marvels of the 21st century. Research indicates that after the covid-19 pandemic IoMT has gained a lot of popularity due to its demand in E-health and more particularly in telehealth and telemedicine. However, all the existing IoMT initiatives are at their early stage of development and require a more advanced approach within their domain. More significant, concerning the use of IoT in both the software and hardware arena, the lack of knowledge and experience to manufacture IoMT devices is observed. Thus, the health system is aware that there are substantial challenges to implementing IoMT software and hardware. In this paper, we aim to provide a high-level review of existing IoMT data interoperability, product design, product's market adoption, data challenges. Also, we are providing practical suggestions through implementing semi-automated systems using cloud computing, and artificial intelligence via digital health platforms. Knowing these provided high-level suggestions will enhance the process of IoMT production and provide better and more reliable healthcare and remote monitoring system. © 2021 IEEE.

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an auto-maticmethod that can cope with scenarios inwhich preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data (SCLLD) relying on Sobel edge detection and Generative Adversarial Networks (GANs) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled datawhere supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 +/- 0.20%, 99.88 +/- 0.24%, and 99.40 +/- 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 +/- 4.11%, 91.2 +/- 6.15%, and 46.40 +/- 5.21%.

BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings. (c) 2021 Elsevier Inc. All rights reserved.

A Deep Bayesian Ensembling Framework for COVID-19 Detection using Chest CT Images

The chest computed tomography (CT) images have been used for COVID-19 detection. Automating the process of analyzing can save great amount of time and energy. In this paper a deep bayesian ensembling framework is proposed for automatic detection of COVID-19 cases using the chest CT scans. Data augmentation is applied to increase the size and quality of training data available. Transfer learning is utilized to extract informative features. The extracted features are used to train the three different bayesian classifiers. The uncertainty of the neural network predictions is estimated by anchored, unconstrained and regularized bayesian ensembling methods. The reliability of predictions is then delineated. The epistemic and aleatoric uncertainties are estimated and different bayesian classifiers are compared from different perspectives. We use a small dataset containing only 275 CT images of positive COVID-19 cases. The results sounds promising and they can be improved in the future, as the performance of deep neural networks is reliant to big datasets. Prediction accuracy and predictive uncertainty estimates for unseen chest CT images indicate that the deep bayesian ensembling is a promising framework for COVID-19 detection.

Late stent thrombosis and acute ST-elevation myocardial infarction in a case affected with COVID-19: A rare manifestation

A 65-year-old male was introduced with a history of percutaneous coronary intervention 2 years ago who received Aspirin and Plavix. He was referred for coronary angiography after receiving thrombolytic therapy for ST-elevation myocardial infarction in precordial leads. On admission, he had dyspnea with low oxygen saturation, leukocytosis, lymphopenia, elevated C-reactive protein, and cardiac troponin levels. Transthoracic echocardiography demonstrated left ventricular ejection fraction (LVEF) of 25% and pulmonary artery pressure of 45 mmHg. A small thrombus at the site of the previously deployed stent was noticeable at coronary angiography. The chest computed tomography depicted significant involvement of the lungs manifested by peripheral ground-glass opacifications. A positive polymerase chain reaction confirmed coronavirus infection. He was oxygen dependent for 1 week. Gradually, his respiratory distress improved and his LVEF reached to 30% after discharge.