A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

Investigation of demographic implicit discrimination and disparate impact in chest radiography image-based AI for COVID-19 severity prediction

It is important to evaluate medical imaging artificial intelligence (AI) models for possible implicit discrimination (ability to distinguish between subgroups not related to the specific clinical task of the AI model) and disparate impact (difference in outcome rate between subgroups). We studied potential implicit discrimination and disparate impact of a published deep learning/AI model for the prediction of ICU admission for COVID-19 within 24 hours of imaging. The IRB-approved, HIPAA-compliant dataset contained 8,357 chest radiography exams from February 2020-January 2022 (12% ICU admission within 24 hours) and was separated by patient into training, validation, and test sets (64%, 16%, 20% split). The AI output was evaluated in two demographic categories: sex assigned at birth (subgroups male and female) and self-reported race (subgroups Black/African-American and White). We failed to show statistical evidence that the model could implicitly discriminate between members of subgroups categorized by race based on prediction scores (area under the receiver operating characteristic curve, AUC: median [95% confidence interval, CI]: 0.53 [0.48, 0.57]) but there was some marginal evidence of implicit discrimination between members of subgroups categorized by sex (AUC: 0.54 [0.51, 0.57]). No statistical evidence for disparate impact (DI) was observed between the race subgroups (i.e. the 95% CI of the ratio of the favorable outcome rate between two subgroups included one) for the example operating point of the maximized Youden index but some evidence of disparate impact to the male subgroup based on sex was observed. These results help develop evaluation of implicit discrimination and disparate impact of AI models in the context of decision thresholds © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

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.

IsoCore–An efficient model to aid rapid forecasting of SARS-CoV-2 infection from biomedical imagery

Combating the covid19 scourge is a prime concern for the human race today. Rapid diagnosis is critical to identify the infection accurately. Due to the prevalence of public health crisis, reaction-based blood tests are the customary approach for identifying covid19. As a result, scientists are analyzing screening methods like deep layered machine learning on chest radiographs. Despite their usefulness, these approaches have large computational costs, rendering them unworkable in practice. This study's main goal is to establish an accurate yet efficient method for predicting SARS-CoV-2 infection (Severe Acute Respiratory Syndrome CoronaVirus 2) using chest radiography pictures. We utilized and enhanced the graph-based family of neural networks to achieve the stated goal. The IsoCore algorithm is trained on a collection of X-ray images separated into four categories: healthy, Covid19, viral pneumonia, and bacterial pneumonia. The IsoCore model has 5 to 10 times fewer parameters than the other tested designs. It attains an overall accuracy of 99.79%. We believe the acquired results are the most ideal in the deep inference domain at this time. This proposed model might be employed by doctors via phones. © The Authors.

COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

The COVID 19 pandemic is highly contagious disease is wreaking havoc on people's health and well-being around the world. Radiological imaging with chest radiography is one among the key screening procedure. This disease contaminates the respiratory system and impacts the alveoli, which are small air sacs in the lungs. Several artificial intelligence (AI)-based method to detect COVID-19 have been introduced. The recognition of disease patients using features and variation in chest radiography images was demonstrated using this model. In proposed paper presents a model, a deep convolutional neural network (CNN) with ResNet50 configuration, that really is freely-available and accessible to the common people for detecting this infection from chest radiography scans. The introduced model is capable of recognizing coronavirus diseases from CT scan images that identifies the real time condition of covid-19 patients. Furthermore, the database is capable of tracking detected patients and maintaining their database for increasing accuracy of the training model. The proposed model gives approximately 97% accuracy in determining the above-mentioned results related to covid-19 disease by employing the combination of adopted-CNN and ResNet50 algorithms.

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

State-of-the-Art in Lung Ultrasound Processing - Brief Review

This paper explains the basic principles of ultrasound and its use in the medical examination mainly in chest ultrasound. We provide an overview of methods that address various aspects of classification and semantic segmentation of pathological symptoms in ultrasound videos. Also, we review the availability of the lung ultrasound data for the development of the machine learning models. Finally, we introduce our ongoing research in the field. This article serves as a theoretical basis for the introduction to lung ultrasound and the processing of ultrasonography data mainly with convolutional neural networks.

Radiographic findings of SARS-CoV-2 infection.

STUDY OBJECTIVE: The 2019-20 coronavirus pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). This study was undertaken to identify and compare findings of chest radiography and computed tomography among patients with SARS-CoV-2 infection. METHODS: This retrospective study was undertaken at a tertiary care center. Eligible subjects included consecutive patients age 18 and over with documented SARS-CoV-2 infection between March and July 2020. The primary outcome measures were results of chest radiography and computed tomography among patients with documented SARS-CoV-2 infection. RESULTS: Among 724 subjects, most were admitted to a medical floor (46.4%; N = 324) or admitted to an ICU (10.9%; N = 76). A substantial number of subjects were intubated during the emergency department visit or inpatient hospitalization (15.3%; N = 109). The majority of patients received a chest radiograph (80%; N = 579). The most common findings were normal, bilateral infiltrates, ground-glass opacities, or unilateral infiltrate. Among 128 patients who had both chest radiography and computed tomography, there was considerable disagreement between the 2 studies (52.3%; N = 67; 95% confidence interval: 43.7% to 61.0%).). The presence of bilateral infiltrates (infiltrates or ground-glass opacities) was associated with clinical factors including older age, ambulance arrivals, more urgent triage levels, higher heart rate, and lower oxygen saturation. Bilateral infiltrates were associated with poorer outcomes, including higher rate of intubation, greater number of inpatient days, and higher rate of death. CONCLUSIONS: Common radiographic findings of SARS-CoV-2 infection include infiltrates or ground-glass opacities. There was considerable disagreement between chest radiography and computed tomography. Computed tomography was more accurate in defining the extent of involved lung parenchyma. The presence of bilateral infiltrates was associated with morbidity and mortality.

Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model.

Coronavirus disease, also known as COVID-19, is an infectious disease caused by SARS-CoV-2. It has a direct impact on the upper and lower respiratory tract and threatened the health of many people around the world. The latest statistics show that the number of people diagnosed with COVID-19 is growing exponentially. Diagnosing positive cases of COVID-19 is important for preventing further spread of the disease. Currently, Coronavirus is a serious threat to scientists, medical experts and researchers around the world from its detection to its treatment. It is currently detected using reverse transcription polymerase chain reaction (RT-PCR) analysis at the most test centers around the world. Yet, knowing the reliability of a deep learning based medical diagnosis is important for doctors to build confidence in the technology and improve treatment. The goal of this study is to develop a model that automatically identifies COVID-19 by using chest X-ray images. To achieve this, we modified the DarkCovidNet model which is based on a convolutional neural network (CNN) and plotted the experimental results for two scenarios: binary classification (COVID-19 versus No-findings) and multi-class classification (COVID-19 versus pneumonia versus No-findings). The model is trained on more than 10 thousand X-ray images and achieved an average accuracy of 99.53% and 94.18% for binary and multi-class classification, respectively. Therefore, the proposed method demonstrates the effectiveness of COVID-19 detection using X-ray images. Our model can be used to test the patient via cloud and also be used in situations where RT-PCR tests and other options aren't available.

Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?

Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.

Chest radiography evaluation in patients admitted with confirmed COVID-19 infection, in a resource limited South African isolation hospital

AJOL : Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the subsequent global outbreak (coronavirus disease 2019 [COVID-19]) was declared a public health emergency in January 2020. Recent radiologic literature regarding COVID-19 has primarily focused on Computed Tomography (CT) chest findings, with chest radiography lacking in comparison.Objectives: To describe the demographic profile of adult patients with COVID-19 pneumonia requiring hospital admission. To describe and quantify the imaging spectrum on chest radiography using a severity index, and to correlate the severity of disease with prognosis.Method: Retrospective review of chest radiographs and laboratory records in patients admitted to a South African tertiary hospital with confirmed COVID-19 infection. The chest X-rays were systematically reviewed for several radiographic features, which were then quantified using the Brixia scoring system, and correlated to the patient's outcome.Results: A total of 175 patients (mean age: 53.34 years) admitted with COVID-19 were included. Ground glass opacification (98.9%), consolidation (86.3%), and pleural effusion (29.1%) was commonly found. Involvement of bilateral lung fields (96.6%) with no zonal predominance (61.7%), was most prevalent. Correlation between the Brixia score and outcome was found between severe disease and death (odds ratio [OR]: 12.86;95% confidence interval [CI]: 1.58-104.61). Many patients had unknown TB (71.4%) and HIV (72.6%) statuses.Conclusion: In this study population, ground glass opacification, consolidation, and pleural effusions, with bilateral lung involvement and no zonal predominance were the most prevalent findings in proven COVID-19 infection. Quantification using the Brixia scoring system may assist with timeous assessment of disease severity in COVID-19 positive patients, as an overall predicator of clinical outcome

A Transfer Learning Approach for Diagnosis of COVID-19 Cases from Chest Radiography Images

The new coronavirus disease 2019 (COVID-19) pandemic completely changed individuals' daily lives and created economic disruption across the world. Many countries are using movement restrictions and physical distancing as their measures to slow down this transmission. Effective screening of COVID-19 cases is needed to stop the spreading of these diseases. In the first phases of clinical assessment, it was seen that patients with deformities in chest X-ray images show the signs of COVID-19 infection. Inspired from this, in this study, a novel framework is designed to detect the COVID-19 cases from chest radiography images. Here, a pre-trained deep convolutional neural network VGG-16 is used to extract discriminating features from the radiography images. These extracted features are given as an input to the Logistic regression classifier for automatic detection of COVID-19 cases. The suggested framework obtained a remarkable accuracy of 99.1% with a 100% sensitivity rate in comparison with other state-of-the-art classifier. © 2022 IEEE.

COVID Detection Using Chest X-ray Images Using Ensembled Deep Learning

COVID-19 originated in Wuhan, China, in December 2019, and there have been over 464.5 million infected cases, and 6.08 million individuals have died worldwide. Effective detection of COVID-19 has been an essential task for stopping its quick spread and ultimately saving precious lives. This paper considers radiological examination using chest X-rays as patients with COVID-19 infections are likely to be adequately recognized using chest radiography pictures. Although many machine learning/deep learning techniques have been developed, their approach is likely to suffer problems like generalization error, high variance, overfitting, etc., due to limited dataset size. By producing predictions with numerous models rather than only one model, the ensemble model can overcome the disadvantages of deep learning. So, in this paper, we propose an ensemble deep learning method for detecting COVID-19 using chest X-ray images. On a combination of DenseNet, InceptionV3, and MobileNet, we got the best validation accuracy of 96.20% and testing accuracy of 92.45%. We hope this approach will help detect COVID-19 early and reduce further spread. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Classification of Chest Radiography Scans for COVID-19

Humanity has suffered as a result of the COVID-19 pandemic for more than two years. Testing kits were not widely accessible during the pandemic, which caused alarm. Any technical development that enables a quicker and more accurate identification of COVID-19 infection can be very beneficial for the medical field. X-rays can be used to examine a patient's lungs since COVID-19 targets the epithelial cells that line the respiratory system. It is challenging to determine COVID-19 from other Viral Pneumonia cases, though. The purpose of this paper is to examine the effectiveness of deep learning models in the quick and precise detection of COVID-19 in chest X-ray scans. © 2022 IEEE.

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

A phantom study of a protective trolley for neonatal radiographic imaging: new equipment to protect the operator from scatter radiation.

Chest radiography is commonly performed as a diagnostic tool of neonatal diseases. Contact-based radiation personal protective equipment (RPPE) has been widely used for radiation protection, but it does not provide full body protection and it is often shared between users, which has become a major concern during the coronavirus disease 2019 (COVID-19) pandemic. To address these issues, we developed a novel trolley to protect radiographers against X-ray radiation by reducing scatter radiation during neonatal radiographic examinations. We measured the scatter radiation doses from a standard neonatal chest radiograph to the radiosensitive organs using a phantom operator in three protection scenarios (trolley, radiation personal protective equipment [RPPE], no protection) and at three distances. The results showed that the scatter radiation surface doses were significantly reduced when using the trolley compared with RPPE and with no protection at a short distance (P<0.05 for both scenarios in all radiosensitive organs). The novel protective trolley provides a non-contact protective tool for radiographers against the hazard of scatter radiation during neonatal radiography examinations.

Artificial Intelligence in Paediatric Tuberculosis.

Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.

Covid-19 Chest Radiography Images Analysis Based on Integration of Image Preprocess and Ensemble Learning Model

COVID-19, a highly insidious infectious disease, is now widespread in the world and has caused hundreds of millions of dollars of damage. Rapid detection for COVID-19 is essential for outbreak control, yet existing RT-PCR assays require hours to obtain results and may be incorrect. To enable faster and more accurate detection of COVID-19 infected patients, training neural networks on chest X-ray scans and using the trained models to assist in the diagnosis of lung disease is worth considering. Our team constructed a model consisting of 5 CNN networks of AlexNet, VGG11, GoogleNet, ResNet18 and DenseNet121 with a fully connected neural network using transfer learning and ensemble learning. This model is able to combine the advantages of the 5 CNN networks to get better results. At the same time, we use CLAHE image enhancement algorithm with image augmentation to optimize the training set, which avoids overfitting problem and can further improve the results. With the above approach, we can train a highly accurate ensemble model in a short time to quickly detect COVID-19 infected patients with a small sample of chest X-ray images. Our ensemble model converges quickly and the final test accuracy is 96.48%, which is higher than the test accuracy of any of the five individual CNN networks. © 2022 IEEE.