Assessing an AI-based Smart Imagery Framing and Truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest X-ray images for development of deep learning models

To assess a Smart Imagery Framing and Truthing (SIFT) system in automatically labeling and annotating chest X-ray (CXR) images with multiple diseases as an assist to radiologists on multi-disease CXRs. SIFT system was developed by integrating a convolutional neural network based-augmented MaskR-CNN and a multi-layer perceptron neural network. It is trained with images containing 307,415 ROIs representing 69 different abnormalities and 67,071 normal CXRs. SIFT automatically labels ROIs with a specific type of abnormality, annotates fine-grained boundary, gives confidence score, and recommends other possible types of abnormality. An independent set of 178 CXRs containing 272 ROIs depicting five different abnormalities including pulmonary tuberculosis, pulmonary nodule, pneumonia, COVID-19, and fibrogenesis was used to evaluate radiologists' performance based on three radiologists in a double-blinded study. The radiologist first manually annotated each ROI without SIFT. Two weeks later, the radiologist annotated the same ROIs with SIFT aid to generate final results. Evaluation of consistency, efficiency and accuracy for radiologists with and without SIFT was conducted. After using SIFT, radiologists accept 93% SIFT annotated area, and variation across annotated area reduce by 28.23%. Inter-observer variation improves by 25.27% on averaged IOU. The consensus true positive rate increases by 5.00% (p=0.16), and false positive rate decreases by 27.70% (p<0.001). The radiologist's time to annotate these cases decreases by 42.30%. Performance in labelling abnormalities statistically remains the same. Independent observer study showed that SIFT is a promising step toward improving the consistency and efficiency of annotation, which is important for improving clinical X-ray diagnostic and monitoring efficiency. © 2023 SPIE.

ASSOCIATION OF CHEST RADIOGRAPHIC GEOMETRIC CHANGES IN MEDIASTINUM WITH COVID-19 CONDITIONS

In this work, an attempt is made to investigate the association of geometric changes in mediastinum and lungs with Coronavirus Disease-2019 (COVID-19) using chest radiographic images. For this, the normal and COVID-19 images are considered from a public database. Reaction-diffusion level set is employed to segment the lung fields. Further, Chan Vese level set mechanism is used to delineate the mediastinum. Features, such as area, convex area, and bounding box area, are extracted from the mediastinum and lung masks. Then, mediastinum to lungs ratiometric features are derived, and statistical analysis is performed. The results demonstrate that the proposed methods are able to segment both regions by capturing significant anatomical landmarks. The ratiometric indices, along with mediastinum measures, are observed to be statistically significant for normal and COVID-19 conditions. Mediastinum convex area for COVID-19 conditions is found to be two times greater than normal subjects indicating the maximum difference in values between the classes. An AUC of 94% is obtained using SVM classifier for differentiating normal and COVID-19 conditions. Thus, the investigation of the mechanics of structural alterations of lungs and mediastinum is significant in COVID-19 diagnosis. As the proposed approach is able to detect COVID-19 conditions, it could act as a decision support system to assist clinicians in early detection. [ FROM AUTHOR] Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company 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.)

A unilateral whiteout lung in child with multisystem inflammatory syndrome associated with COVID-19 due to SARS-CoV-2: one case report of a boy.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a relatively new and rare complication of COVID-19. This complication seems to develop after the infection rather than during the acute phase of COVID-19. This report aims to describe a case of MIS-C in an 8-year-old Thai boy who presented with unilateral lung consolidation. Unilateral whiteout lung is not a common pediatric chest radiograph finding in MIS-C, but this is attributed to severe acute respiratory failure. CASE PRESENTATION: An 8-year-old boy presented with persistent fever for seven days, right cervical lymphadenopathy, and dyspnea for 12 h. The clinical and biochemical findings were compatible with MIS-C. Radiographic features included total opacity of the right lung and CT chest found consolidation and ground-glass opacities of the right lung. He was treated with intravenous immunoglobulin and methylprednisolone, and he dramatically responded to the treatment. He was discharged home in good condition after 8 days of treatment. CONCLUSION: Unilateral whiteout lung is not a common pediatric chest radiographic finding in MIS-C, but when it is encountered, a timely and accurate diagnosis is required to avoid delays and incorrect treatment. We describe a pediatric patient with unilateral lung consolidation from the inflammatory process.

A comparison of the horizontal and vertical bitewing images in detecting approximal caries and interdental bone loss in posterior teeth: A diagnostic accuracy randomized cross over clinical trial.

Background/purpose: Horizontal bitewing radiographs are widely and frequently used in dentistry and are very reliable in diagnosing proximal caries and interproximal alveolar bone level. However, it is challengeable in detecting interproximal root caries, horizontal and/or vertical alveolar bone loss, and furcation involvements. The aim of this article was to assess the accuracy of vertical bitewing images in the diagnosis of caries and alveolar bone level compared to the horizontal bitewing technique. Materials and methods: Each one of the 20 patients had eight bitewing radiographs to get four horizontal bitewing (control) and four vertical bitewing (experimental) images for the same posterior area; a steel wire (3 mm) was used on the sensor plate to help measure the magnification later on. The radiographs were processed digitally and were evaluated for caries by two expert restorative specialists and for bone loss by two experienced periodontists. They were also compared to the "gold standard," which is using of both clinical and radiographic examination for diagnosis. They were blinded to each other during images evaluation. Results: Of the 20-patient sample size, 70% were male and 30% were female, with a mean age of 29.9. The average number of radiographs taken to achieve four standard bitewing radiographs was 5.9 ± 1.7 for vertical bitewings and 5.3 ± 1.3 for horizontal bitewing radiograph. The measurements from the cementoenamel junction (CEJ) to the level of crestal bone didn't show a significant difference between the horizontal and vertical bitewing radiographs. The detection of furcation area in the molar teeth was much higher in the vertical bitewing (100%) compared to the horizontal bitewing (57.5%) (P < 0.0001). Conclusion: The vertical bitewing radiograph has the upper hand over the horizontal bitewing radiograph in the detection of furcation involvement, caries detection, and alveolar bone loss. Therefore, it is highly recommended to use vertical bitewing in caries and patients with periodontal disease rather than the conventional horizontal bitewing.

A Deep Net Model for Covid-19 Detection Based on ROC Analysis Using Radiographs

The European Centre of Disease Prevention & Control's analytical statistics show that the new corona virus (Covid-19) is rapidly spreading amongst millions of people & causing the deaths of thousands of them. Despite the daily increase in cases, there are still a finite quantity of Covid-19 test kits available. The use of an automatic recognition system is crucial for the diagnosis and control of Covid-19. Three important Inception-ResNetV2, InceptionV3, & ResNet50 models of convolutional neural networks are utilized to detect the Corona Virus in lung X-ray radiography. The ResNet50 version has the best result & accuracy rate of the present system. As compared to the current models, a novel procedures and ensuring on the CNN model delivers better specific, sensitivities, and precision. By using confusion matrix and ROC assessment, fivefold validation data is utilized to analyze the current models and compare them to the proposed system. © 2023 IEEE.

A Segmentation Method Based on PDNet for Chest X-rays with Targets in Different Positions and Directions

To assess the impact of the relative displacement between machines and subjects, the machine angle and the fine-tuning of the subject posture on the segmentation accuracy of chest X-rays, this paper proposes a Position and Direction Network (PDNet) for chest X-rays with different angles and positions that provides more comprehensive information for cardiac image diagnosis and guided surgery. The implementation of PDnet was as follows: First, the extended database image was sent to a traditional segmentation network for training to prove that the network does not have linear invariant characteristics. Then, we evaluated the performance of the mask in the middle layers of the network and added a weight mask that identifies the position and direction of the object in the middle layer, thus improving the accuracy of segmenting targets at different positions and angles. Finally, the active-shape model (ASM) was used to postprocess the network segmentation results, allowing the model to be effectively applied to 2014 × 2014 or higher definition chest X-rays. The experimental comparison of LinkNet, ResNet, U-Net, and DeepLap networks before and after the improvement shows that its segmentation accuracy (MIoU) are 5%, 6%, 20%, and 13% better. Their differences of losses are 11.24%, 21.96%, 18.53%, and 13.43% and F-scores also show the improved networks are more stable.

Imaging recommendations and algorithms for pediatric tuberculosis: part 1-thoracic tuberculosis.

Tuberculosis (TB) remains a global health problem and is the second leading cause of death from a single infectious agent, behind the novel coronavirus disease of 2019. Children are amongst the most vulnerable groups affected by TB, and imaging manifestations are different in children when compared to adults. TB primarily involves the lungs and mediastinal lymph nodes. Clinical history, physical examination, laboratory examinations and various medical imaging tools are combined to establish the diagnosis. Even though chest radiography is the accepted initial radiological imaging modality for the evaluation of children with TB, this paper, the first of two parts, aims to discuss the advantages and limitations of the various medical imaging modalities and to provide recommendations on which is most appropriate for the initial diagnosis and assessment of possible complications of pulmonary TB in children. Practical, evidence-based imaging algorithms are also presented.

Portable Chest Radiograph: A Boon for Critically Ill Patients With COVID-19 Pneumonia.

OBJECTIVE: In the present study, we evaluated the role of portable chest radiographs in critically ill patients with COVID-19 pneumonia in whom computed tomography (CT) of the chest was not feasible. METHODS: A retrospective chest X-ray study of patients under investigation for COVID-19 was performed in our dedicated COVID hospital (DCH) during the exponential growth phase of the COVID-19 outbreak (August-October, 2020). A total of 562 on-bed chest radiographs were examined comprising 289 patients (critically ill who couldn't be mobilized for CT) along with positive reverse transcription-polymerase chain reaction (RT-PCR) tests. We categorized each chest radiograph as progressive, with changes, or improvement in appearance for COVID-19, utilizing well-documented COVID-19 imaging patterns. RESULTS:  In our study, portable radiographs provided optimum image quality for diagnosing pneumonia, in critically ill patients. Although less informative than CT, nevertheless radiographs detected serious complications like pneumothorax or lung cavitation and estimated the evolution of pneumonia. CONCLUSION: A portable chest X-ray is a simple but reliable alternative for critically ill SARS-CoV-2 patients who could not undergo chest CT. With the help of portable chest radiographs, we could monitor the severity of the disease as well as different complications with minimal radiation exposure which would help in identifying the prognosis of the patient and thus help in medical management.

Incidence of Respiratory Pathogens in Naval Special Warfare SEAL Candidates with Swimming-Induced Pulmonary Edema.

BACKGROUND: Swimming-Induced Pulmonary Edema (SIPE) is a respiratory condition frequently seen amongst Naval Special Warfare (NSW) trainees. The incidence of positive respiratory panels (RPs) in trainees diagnosed with SIPE is currently unknown. RESEARCH QUESTION: Is there a significant difference in the incidence of respiratory pathogens in nasopharyngeal samples of NSW candidates with SIPE and a control group? STUDY DESIGN AND METHODS: Retrospective analysis of clinical information from NSW Sea Air and Land (SEAL) candidates diagnosed with SIPE over a 12-month period. Candidates who presented with the common signs and symptoms of SIPE received a nasopharyngeal swab and RP test for common respiratory pathogens. SIPE diagnoses were supported by two-view chest radiograph. RP tests were obtained for a selected control group of 1st phase trainees without SIPE. RESULTS: 45 of 1048 SEAL candidates were diagnosed with SIPE (4.3%). 5 had superimposed pneumonia. 36 of 45 tested positive for at least one microorganism on the RP (80%). In the study group, human rhinovirus/enterovirus (RV/EV) was the most frequently detected organism (37.8%), followed by coronavirus OC43 (17.8%), and parainfluenza virus 3 (17.8%). 16 of 68 candidates from the control group had positive RPs (24%). Patients with SIPE and positive RPs reported dyspnea (94%), pink-frothy sputum (44%), and hemoptysis (22%) more frequently than the controls with positive RPs. Those who reported respiratory infection symptoms in both the study and control groups had higher incidences of positive RPs (P=.046). INTERPRETATION: We observed that 80% of trainees diagnosed with SIPE tested positive on a point of care RP. This positivity rate was significantly higher than RP test results from the control cohort. These findings suggest an association between colonization with a respiratory pathogen and the development of SIPE in NSW candidates.

Identification and Localization COVID-19 Abnormalities on Chest Radiographs

Solutions to screen and diagnose positive patients for the SARS-CoV-2 promptly and efficiently are critical in the context of the COVID-19 pandemic's complex evolution. Recent researches have demonstrated the efficiency of deep learning and particularly convolutional neural networks (CNNs) in classifying and detecting lung disease-related lesions from radiographs. This paper presents a solution using ensemble learning techniques on advanced CNNs to classify as well as localize COVID-19-related abnormalities in radiographs. Two classifiers including EfficientNetV2 and NFNet are combined with three detectors, DETR, Yolov7 and EfficientDet. Along with gathering and training the model on a large number of datasets, image augmentation and cross validation are also addressed. Since then, this study has shown promising results and has received excellent marks in the Society for Imaging Informatics in Medicine's competition. The analysis in model selection for the trade-off between speed and accuracy is also given. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automation of COVID-19 Disease Diagnosis from Radiograph

The coronavirus disease (COVID-19) makes humans suffer from mild to moderate respiratory problems, with severe cases requiring special treatment. In many severe cases, elderly individuals and people with pre-existing medical issues like lung-related disease, insulin-dependent disease, and carcinoma, are more prone to difficulty breathing and developing a severe illness. To detect the coronavirus here, X-ray radiograph images are considered. The main motive for using X-ray radiograph images is their being cost-effective and being able to give considerable accuracy compared to its counterpart, computed tomography (CT) scans. In this study, the deep learning model Visual Geometry Group (VGG)16 using the transfer learning method and image augmentation techniques was employed for automatic COVID-19 diagnosis. These two techniques will assist the deep learning model to learn the target task by improving the baseline performance by using fewer X-ray radiograph images in the training phase and showing improvements in the model development time by utilising knowledge gained from a source model. Many deep learning methods have been published in the literature to solve the same cases, but the proposed method uses a simple VGG16 model with transfer learning, which takes less processing time and gives satisfactory results even by using fewer training samples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Role of chest radiograph in MERS-Cov pneumonia: a single tertiary referral center experience in the United Arab Emirates

BackgroundThe Middle East respiratory syndrome coronavirus (MERS-Cov) continues to be a source of concern due to intermittent outbreaks. Serial chest radiographic changes in MERS-Cov patients were analyzed for various variables that could be compared to the patients' final outcomes in a cluster of MERS-Cov patients and to identify a predictor of mortality in the United Arab Emirates.ResultsA total of 44 MERS-Cov cases were reviewed. The mean age of the patients was 43.7 ± 14.7 years. The chest radiograph was abnormal in 14/44 (31.8%). The commonest radiology features include ground-glass opacities (seven of 14, 50%), ground-glass and consolidation (seven of 14, 50%), pleural effusion (eight of 14, 57.1%), and air bronchogram (three of 14, 21.4%). The mortality rate was 13.6% (six of 44);the deceased group (6 of 44, 13.6%) was associated with significantly higher incidence of mechanical ventilation (p < 0.001), pleural effusion (p < 0.001), chest radiographic score (8.90 ± 6.31, p < 0.001), and type 4 radiographic progression of disease (p < 0.001). A chest radiographic score at presentation was seen to be an independent and strong predictor of mortality (OR [95% confidence interval] 3.20 [1.35, 7.61]). The Cohen κ coefficient for the interobserver agreement was k = 0.89 (p = 0.001).ConclusionThe chest radiographic score, associated with a higher degree of disease progression (type 4), particularly in patients with old age or with comorbidity, may indicate a poorer prognosis in MERS-Cov infection, necessitating intensive care unit management or predicting impending death.

The Impact of Data Management on the Achievable Dose and Efficiency of Mammography and Radiography During the COVID-19 Era: A Facility-Based Cohort Study.

Purpose: To evaluate the impact of using computational data management resources and analytical software on radiation doses in mammography and radiography during the COVID-19 pandemic, develop departmental diagnostic reference levels (DRLs), and describe achievable doses (ADs) for mammography and radiography based on measured dose parameters. Patients and Methods: This ambispective cohort study enrolled 795 and 12,115 patients who underwent mammography and radiography, respectively, at the King Fahd Hospital of the University, Al-Khobar City, Saudi Arabia between May 25 and November 4, 2021. Demographic data were acquired from patients' electronic medical charts. Data on mammographic and radiographic dose determinants were acquired from the data management software. Based on the time when the data management software was operational in the institute, the study was divided into the pre-implementation and post-implementation phases. Continuous and categorical variables were compared between the two phases using an unpaired t-test and the chi-square test. Results: The median accumulated average glandular dose (AGD; a mammographic dose determinant) in the post-implementation phase was three-fold higher than that in the pre-implementation phase. The average mammographic exposure time in the post-implementation phase was 16.3 ms shorter than that in the pre-implementation phase. Furthermore, the median values of the dose area product ([DAP], a radiographic dose determinant) were 9.72 and 19.4 cGycm2 in the pre-implementation and post-implementation phases, respectively. Conclusion: Although the data management software used in this study helped reduce the radiation exposure time by 16.3 ms in mammography, its impact on the mean accumulated AGD was unfavorable. Similarly, radiographic exposure indices, including DAP, tube voltage, tube current, and exposure time, were not significantly different after the data management software was implemented. Close monitoring of patient radiation doses in mammography and radiography, and dose reduction will become possible if imaging facilities use DRLs and ADs via automated systems.

GUI Enabled Optimized Approach of CNN for Automatic Diagnosis of COVID-19 Using Radiograph Images.

World Health Organization (WHO) proclaimed the Corona virus (COVID-19) as a pandemic, since it contaminated billions of individuals and killed lakhs. The spread along with the severity of the disease plays a key role in early detection and classification to reduce the rapid spread as the variants are changing. COVID-19 could be categorized as a pneumonia infection. Bacterial pneumonia, fungal pneumonia, viral pneumonia, etc., are the classifications of several forms of pneumonia, which are subcategorized into more than 20 forms and COVID-19 will come under viral pneumonia. The wrong prediction of any of these can mislead humans into improper treatment, which leads to a matter of life. From the radiograph that is X-ray images, diagnosis of all these forms can be possible. For detecting these disease classes, the proposed method will employ a deep learning (DL) technique. Early detection of the COVID-19 is possible with this model; hence, the spread of the disease is minimized by isolating the patients. For execution, a graphical user interface (GUI) provides more flexibility. The proposed model, which is a GUI approach, is trained with 21 types of pneumonia radiographs by a convolutional neural network (CNN) trained on Image Net and adjusts them to act as feature extractors for the Radiograph images. Next, the CNNs are combined with united AI strategies. For the classification of COVID-19 detection, several approaches are proposed in which those approaches are concerned with COVID-19, pneumonia, and healthy patients only. In classifying more than 20 types of pneumonia infections, the proposed model attained an accuracy of 92%. Likewise, COVID-19 images are effectively distinguished from the other pneumonia images of radiographs.

Chest radiograph findings in children with COVID-19-A retrospective analysis from a tertiary care paediatric hospital in South India.

OBJECTIVE: The primary aim of this study is to document the chest X-ray findings in children with COVID-19 pneumonia. The secondary aim is to correlate chest X-ray findings to patient outcome. METHODS: We performed a retrospective analysis of children (0-18 years) with SARS-CoV-2 admitted to our hospital from June 2020 to December 2021. The chest radiographs were assessed for: peribronchial cuffing, ground-glass opacities (GGOs), consolidation, pulmonary nodules and pleural effusion. The severity of the pulmonary findings was graded using a modification of the Brixia score. RESULTS: There were a total of 90 patients with SARS-CoV-2 infection; the mean age was 5.8 years (age range 7 days to 17 years). Abnormalities were seen on the CXR in 74 (82%) of the 90 patients. Bilateral peribronchial cuffing was seen in 68% (61/90), consolidation in 11% (10/90), bilateral central GGOs in 2% (2/90) and unilateral pleural effusion in 1% (1/90). Overall the average CXR score in our cohort of patients was 6. The average CXR score in patients with oxygen requirement was 10. The duration of hospital stay was significantly longer in those patients with CXR score >9. CONCLUSION: The CXR score has the potential to serve as tool to identify children at high risk and may aid planning of clinical management in such patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created a global pandemic in early March 2020. There are very few studies describing the lung changes in affected children. We performed a retrospective study in children, aged between 0 days and 18 years, who tested positive for this virus. This study was conducted in a paediatric tertiary care hospital in South India. Chest X-ray (CXR) was done in children with moderate and severe SARS-CoV-2 infection; these X-rays were reviewed and scoring was done to assess the degree of abnormality. It was seen that the duration of hospital stay was longer in children with a high CXR score. Amongst the children with score >9, 60% needed oxygen support during their treatment. Thus, CXR score can play a role in the prediction of disease outcome in SARS-CoV-2 infection.

Cross-Sectional Study on Lateral Skull Radiographs to Design a New Nasopharyngeal Swab for Simplified COVID-19 and Respiratory Infections Diagnostic Testing in Children.

Nasopharyngeal swab sample collection is the first-line testing method for diagnosing COVID-19 infection and other respiratory infections. Current information on how to properly perform nasopharyngeal swabbing in children is largely defective. This study aimed at collecting nostril to nasopharynx distance measurements on lateral skull radiographs of children and adolescents to design a nasopharyngeal swab meant to standardize and facilitate the sample collection procedure. A total of 323 cephalograms of 152 male and 171 female children aged 4-14 years taken for orthodontic reasons were selected. On each cephalogram, the shortest distance between the most anterosuperior point of the nostril contour and the nasopharynx outline was measured in mm parallel to the palatal plane. Descriptive statistics of the measurements were calculated for each age group. The lower limit of the 95% confidence intervals of the measurements was taken as a reference to design a swab shaft with marks that, at each age, delimitate a safety boundary for swab progression up to the posterior nasopharyngeal wall. The simplification of the procedure enabled by the newly designed nasopharyngeal swab is valuable to help healthcare providers perform specimen collection on children in a safe and effective way, perhaps under the less-than-ideal conditions possibly occurring in 'point-of-need' contexts.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Autoencoder-based Unsupervised Anomaly Detection for Covid-19 Screening on Chest X-Ray Images

The COVID-19 outbreak is a major global catastrophe of our time and the largest hurdle since World War II. According to WHO, as of July 2022, there are more than 571 million confirmed cases of COVID-19 and over six million deaths. The issue of identifying unexpected inputs based on trained examples of normal data is known as anomaly detection. In the case of diagnosing covid-19, Chest X-ray disorders that are hardly apparent are extremely challenging to identify. Although various well-known supervised classification methods are being applied for that purpose, however in the real scenario, healthy patients' data is tremendously available but contaminated samples are scarce. The process of gathering samples from ill patients is troublesome and takes a lengthy time. To address the issue of data imbalance in anomaly detection, this research demonstrates an unsupervised learning technique using a convolutional autoencoder in which the training phase does not include any infected sample. Being trained only with the healthy data, The patterns of the healthy samples are preserved in latent vector space and can differentiate ill samples by observing substantial divergence from the distribution of healthy data. Higher reconstruction error and lower KDE (Kernel Density Estimation) indicate affected data. By contrasting the reconstruction error and KDE of healthy data with anomalous data, the suggested technique is feasible for identifying anomalous samples. © 2022 IEEE.

Utility of chest radiograph severity scoring in emergency department for predicting outcomes in COVID-19: A study of 1275 patients.

OBJECTIVE: To measure the reliability and reproducibility of a chest radiograph severity score (CSS) in prognosticating patient's severity of disease and outcomes at the time of disease presentation in the emergency department (ED) with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: We retrospectively studied 1275 consecutive RT-PCR confirmed COVID-19 adult patients presenting to ED from March 2020 through June 2020. Chest radiograph severity score was assessed for each patient by two blinded radiologists. Clinical and laboratory parameters were collected. The rate of admission to intensive care unit, mechanical ventilation or death up to 60 days after the baseline chest radiograph were collected. Primary outcome was defined as occurrence of ICU admission or death. Multivariate logistic regression was performed to evaluate the relationship between clinical parameters, chest radiograph severity score, and primary outcome. RESULTS: CSS of 3 or more was associated with ICU admission (78 % sensitivity; 73.1 % specificity; area under curve 0.81). CSS and pre-existing diabetes were independent predictors of primary outcome (odds ratio, 7; 95 % CI: 3.87, 11.73; p < 0.001 & odds ratio, 2; 95 % CI: 1-3.4, p 0.02 respectively). No significant difference in primary outcome was observed for those with history of hypertension, asthma, chronic kidney disease or coronary artery disease. CONCLUSION: Semi-quantitative assessment of CSS at the time of disease presentation in the ED predicted outcomes in adults of all age with COVID-19.

Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters

As of 2019, COVID-19 is the most difficult issue that we are facing. Till now, it has reached over 30 million deaths. Since SARS-CoV-2 is the new virus, it took time to investigate and examine the influence of Coronavirus in human. After analyzing the spreading and infection of COVID-19, researchers applied Artificial Intelligence (AI) techniques to detect COVID-19 quickly to balance the rapid spreading of the virus. Image segmentation is a critical first step in clinical implementations, is a vital role in computer - aided diagnosis that relies heavily on image recognition. Image segmentation is used in medical MRI research to determine the proportions of different anatomical areas of the tissue, as well as how they change as the disease progresses. CT scans are often used to aid with diagnoses. Computer-assisted therapy (CAD) using AI is a particularly significant research area in intelligent healthcare. This paper presents the detection of COVID-19 at an early stage using autoencoders algorithm and Generative Adversarial Networks (GAN) using deep learning approach with more accurate results. The images of Chest Radiograph (CRG) and Chest Computed Tomography (CCT) are used as a trained dataset to detect since SARS-CoV-2 first affect the respiratory system in humans. We achieved a ratio of 1.0, 0.99, and 0.96, the combined dataset was randomly divided into the train, validation, and test sets. Although the early detection of Coronavirus is still a question since the accuracy of the deep learning approach is still under research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.