Acute-Onset Achalasia Following a Recent COVID-19 Infection: A Case Report.

Achalasia is a rare esophageal motility disorder that leads to dysphagia, regurgitation, and several other symptoms. While the etiology of achalasia is not completely understood, studies have suggested an immune reaction to viral infections, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as a potential cause. Here, we present a case report of a previously healthy 38-year-old male who presented to the emergency room with severe shortness of breath, recurrent vomiting, and dry cough, that had progressively worsened over five days. The patient was diagnosed with coronavirus disease 2019 (COVID-19), and a chest CT also revealed prominent features of achalasia with a markedly dilated esophagus and areas of narrowing at the distal esophagus. The initial management of the patient included IV fluids, antibiotics, anticholinergics, and corticosteroid inhalers which improved his symptoms. This case report highlights the importance of considering the acute-onset of achalasia in COVID-19 patients and the need for further research on the potential association between SARS-CoV-2 and achalasia.

Diagnostic des pneumonies aiguës communautaires aux urgences et distinction entre étiologie virale ou bactérienne

La pandémie actuelle liée à l'émergence du SARSCoV-2 en 2019 a considérablement modifié la perception des médecins de l'impact des virus respiratoires et de leur rôle dans les pneumonies aiguës communautaires (PAC). Alors que plus de 25 % des tableaux de PAC chez l'adulte étaient d'origine virale, les virus respiratoires étaient souvent perçus comme des agents pathogènes peu graves. Devant le défi que représente encore à nos jours la documentation microbiologique d'une PAC, l'instauration d'un traitement empirique par antibiotiques est souvent réalisée aux urgences. La pandémie de COVID-19 a surtout mis en exergue le rôle déterminant de la biologie moléculaire et du scanner thoracique dans l'algorithme diagnostique de la PAC. En effet, un diagnostic rapide et fiable est la clé pour améliorer les mesures de précaution et réduire la prescription inutile d'antibiotiques. Du fait de prises en charges très différentes, il est nécessaire de distinguer l'étiologie virale de la bactérienne d'une PAC.Alternate : The current pandemic linked to the emergence of SARS-CoV-2 in 2019 has considerably changed the perception of doctors of the impact of respiratory viruses and their role in community-acquired acute pneumonia (CAP). While more than 25% of CAP in adults were of viral origin, respiratory viruses were often perceived as harmless pathogens. Faced with the challenge that the microbiological documentation of a CAP still represents today, the establishment of empirical antibiotic treatment is often carried out in the emergency room. The COVID-19 pandemic has primarily highlighted the decisive role of molecular biology and chest CT in the diagnostic algorithm of CAP. Indeed, a rapid and reliable diagnosis is the key to improve isolation decisions and reducing the unnecessary prescription of antibiotics. Due to significantly different treatments, it is necessary to distinguish the viral etiology from the bacterial of a CAP.

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. © 2023 Association for Computing Machinery.

Symptomatic or asymptomatic SAR-CoV-2 positive divers should be medically evaluated before returning to scuba diving.

Introduction: In order to allow the resumption of diving activities after a COVID-19 infection, French military divers are required to undergo a medical fitness to dive (FTD) assessment. We present here the results of this medical evaluation performed 1 month after the infection. Methods: We retrospectively analyzed between April 2020 and February 2021 200 records of divers suspected of COVID-19 contamination. Data collected included physical examination, ECG, blood biochemistry, chest CT scan and spirometry. Results: 145 PCR-positive subjects were included, representing 8.5% of the total population of French military divers. Two divers were hospitalized, one for pericarditis and the other for non-hypoxemic pneumonia. For the other 143 divers, physical examination, electrocardiogram and blood biology showed no abnormalities. However 5 divers (3.4%) had persistent subjective symptoms including fatigability, exertional dyspnea, dysesthesias and anosmia. 41 subjects (29%) had significant decreases in forced expiratory flows at 25-75% and 50% on spirometry (n = 20) or bilateral ground-glass opacities on chest CT scan (n = 24). Only 3 subjects were affected on both spirometry and chest CT. 45% of these abnormalities were found in subjects who were initially asymptomatic or had non-respiratory symptoms. In case of abnormalities, normalization was obtained within 3 months. The median time to return to diving was 45 days (IQR 30, 64). Conclusion: Our study confirms the need for standardized follow-up in all divers after COVID-19 infection and for maintaining a rest period before resuming diving activities.

Diagnosis of community-acquired acute pneumonia in the emergency room and distinction between viral or bacterial etiology.

The current pandemic linked to the emergence of SARS-CoV-2 in 2019 has considerably changed the perception of doctors of the impact of respiratory viruses and their role in community-acquired acute pneumonia (CAP). While more than 25% of CAP in adults were of viral origin, respiratory viruses were often perceived as harmless pathogens. Faced with the challenge that the microbiological documentation of a CAP still represents today, the establishment of empirical antibiotic treatment is often carried out in the emergency room. The COVID-19 pandemic has primarily highlighted the decisive role of molecular biology and chest CT in the diagnostic algorithm of CAP. Indeed, a rapid and reliable diagnosis is the key to improve isolation decisions and reducing the unnecessary prescription of antibiotics. Due to significantly different treatments, it is necessary to distinguish the viral etiology from the bacterial of a CAP. Copyright © 2022 Lavoisier. All rights reserved.

Role of chest CT scan in patients with preexisting cancer and COVID-19 pneumonia.

BACKGROUND: Detection of COVID-19 in cancer patients is challenging due to probable preexisting pulmonary infiltration caused by many infectious and non-infectious etiologies. We evaluated chest CT scan findings of COVID-19 pneumonia in cancer patients and explored its prognostic role in mortality. METHODS: We studied 266 COVID-19 patients with a history of cancer diagnosis between 2020 and 2022. Chest CT images were reported based on Radiological Society of North America (RSNA) structural report and the CT score and pattern of involvement were noted. We used multivariate logistic regression models to determine the association between CT scan findings and mortality of the cancer COVID-19 patients. RESULTS: The mean age was 56.48 (± 18.59), and 53% were men. Gastrointestinal (29.3%), hematologic (26.3%), and breast (10.5%) cancers were the most frequent types of cancer. The prevalence of atypical or indeterminate findings in the chest CT was 42.8%. Most radiologic findings were consolidation mixed with ground-glass opacity (44.4%), pleural effusion (33.5%), and pure ground-glass opacity (19.5%). The risk of death was higher among those who had typical chest CT for COVID-19 (OR 3.47; 95% CI 1.14-8.98) and those who had a severity of score higher than 18 (OR 1.89; 95% CI 1.07-3.34). Also, presence of consolidation (P value 0.040), pleural effusion (P value 0.000), centrilobular nodules (P value 0.013), and architectural distortion (P value 0.005) were associated with a poorer prognosis. CONCLUSION: Less than half of COVID-19 patients with a history of cancer had typical imaging features of COVID-19. Radiologists should be aware of atypical, rare, or subtle chest CT findings in patients with pre-existing cancer.

An Ensemble Learning Model to Detect COVID-19 Pneumonia from Chest CT Scan

The novel COVID-19, initially found in Wuhan (China), reached quickly around the globe and turned into a worldwide pandemic situation. It has set off a significant impact on daily life, general well-being, and global finance. It is crucial to diagnose predisposed patients rapidly. There are no exact tests for COVID-19 except RT-PCR which is costly and needs a huge time. Recent research acquired applying radiology imaging approaches recommend that such images comprise features about the COVID-19 infection. The use of machine learning techniques combined with chest imaging can be helpful in the precise recognition of this infection, and can likewise be assistive to beat the issue of an absence of specific doctors. This investigation developed a model for automatic identification of COVID-19 infection utilizing chest CT images. A convolutional neural network has been applied to extract the features from the chest CT images and Principle Component Analysis has been applied for feature selection to reduce computational effort. The proposed model (the ensemble of ML classifiers) has been developed to provide accurate diagnostics by considering the five classes (Normal, Mycoplasma pneumonia, Bacterial pneumonia, Viral pneumonia, and COVID-19). The proposed model reached an accuracy of 99.3%, precision of 99.3%, and recall of 99.2%. This can help clinicians invalidate their primary checkups and can be utilized promptly to check the patients' infection rate. © 2022 IEEE.

Detection of Diseases in Pandemic: A Predictive Approach Using Stack Ensembling on Multi-Modal Imaging Data

Deep Learning (DL) in Medical Imaging is an emerging technology for diagnosing various diseases, i.e., pneumonia, lung cancer, brain stroke, breast cancer, etc. In Machine Learning (ML) and traditional data mining approaches, feature extraction is performed before building a predictive model, which is a cumbersome task. In the case of complex data, there are a lot of challenges, such as insufficient domain knowledge while performing feature engineering. With the advancement in the application of Artificial Neural Networks (ANNs) and DL, ensemble learning is an essential foundation for developing an automated diagnostic system. Medical Imaging with different modalities is effective for the detailed analysis of various chronic diseases, in which the healthy and infected scans of multiple organs are compared and analyzed. In this study, the transfer learning approach is applied to train 15 state-of-the-art DL models on three datasets (X-ray, CT-scan and Ultrasound) for predicting diseases. The performance of these models is evaluated and compared. Furthermore, a two-level stack ensembling of fine-tuned DL models is proposed. The DL models having the best performances among the 15 will be used for stacking in the first layer. Support Vector Machine (SVM) is used in Level 2 as a meta-classifier to predict the result as one of the following: pandemic positive (1) or negative (0). The proposed architecture has achieved 98.3%, 98.2% and 99% accuracy for D1, D2 and D3, respectively, which outperforms the performance of existing research. These experimental results and findings can be considered helpful tools for pandemic screening on chest X-rays, CT scan images and ultrasound images of infected patients. This architecture aims to provide clinicians with more accurate results.

Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases.

Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect. Therefore, in this study, alterations of bronchi and arteries are considered in the classification of lung diseases. Four approaches to highlight these are introduced: (a) a Hessian-based approach, (b) a region-growing algorithm, (c) a clustering-based approach, and (d) a color-coding-based approach. Prior to this, the lungs are segmented, employing several image preprocessing algorithms. The utilized COVID-19 Lung CT scan dataset contains three classes named Non-COVID, COVID, and community-acquired pneumonia, having 6983, 7593, and 2618 samples, respectively. To classify the CT scans into three classes, two deep learning architectures, (a) a convolutional neural network (CNN) and (b) a CNN with long short-term memory (LSTM) and an attention mechanism, are considered. Both these models are trained with the four datasets achieved from the four approaches. Results show that the CNN model achieved test accuracies of 88.52%, 87.14%, 92.36%, and 95.84% for the Hessian, the region-growing, the color-coding, and the clustering-based approaches, respectively. The CNN with LSTM and an attention mechanism model results in an increase in overall accuracy for all approaches with an 89.61%, 88.28%, 94.61%, and 97.12% test accuracy for the Hessian, region-growing, color-coding, and clustering-based approaches, respectively. To assess overfitting, the accuracy and loss curves and k-fold cross-validation technique are employed. The Hessian-based and region-growing algorithm-based approaches produced nearly equivalent outcomes. Our proposed method outperforms state-of-the-art studies, indicating that it may be worthwhile to pay more attention to BA features in lung disease classification based on CT images.

The feasibility of using acoustic measures for predicting the Total Opacity Scores of chest computed tomography scans in patients with COVID-19.

To study the possibility of using acoustic parameters, i.e., Acoustic Voice Quality Index (AVQI) and Maximum Phonation Time (MPT) for predicting the degree of lung involvement in COVID-19 patients. This cross-sectional case-control study was conducted on the voice samples collected from 163 healthy individuals and 181 patients with COVID-19. Each participant produced a sustained vowel/a/, and a phonetically balanced Persian text containing 36 syllables. AVQI and MPT were measured using Praat scripts. Each patient underwent a non-enhanced chest computed tomographic scan and the Total Opacity score was rated to assess the degree of lung involvement. The results revealed significant differences between patients with COVID-19 and healthy individuals in terms of AVQI and MPT. A significant difference was also observed between male and female participants in AVQI and MPT. The results from the receiver operating characteristic curve analysis and area under the curve indicated that MPT (0.909) had higher diagnostic accuracy than AVQI (0.771). A significant relationship was observed between AVQI and TO scores. In the case of MPT, however, no such relationship was observed. The findings indicated that MPT was a better classifier in differentiating patients from healthy individuals, in comparison with AVQI. The results also showed that AVQI can be used as a predictor of the degree of patients' and recovered individuals' lung involvement. A formula is suggested for calculating the degree of lung involvement using AVQI.

Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?

Introduction: This study intended to assess the dose length product (DLP), effective cumulative radiation dose (E.D.), and additional cancer risk (ACR) due to a chest CT scan to detect or follow up the Covid-19 disease in four university-affiliated hospitals that used different imaging protocols. Indeed, this study aimed to examine the differences in decision-making between different imaging centers in choosing chest CT imaging protocols during the pandemic, and to assess whether a new diagnostic reference level (DRL) is needed in pandemic situations. Methods: This retrospective study assessed the E.D. of all chest imagings for Covid-19 for six months in four different hospitals in our country. Imaging parameters and DLP (mGy.cm) were recorded. The E.D.s and ACRs from chest CT scans were calculated using an online calculator. Results: Thousand-six hundred patients were included in the study. The mean cumulative dose due to chest CT was 3.97 mSv which might cause 2.59 × 10-2 ACR. The mean cumulative E.D. in different hospitals was in the range of 1.96-9.51 mSv. Conclusions: The variety of mean E.D.s shows that different hospitals used different imaging protocols. Since there is no defined DRL in the pandemic, some centers use routine protocols, and others try to reduce the dose but insufficiently.In pandemics such as Covid-19, when CT scan is used for screening or follow-up, DLPs can be significantly lower than in normal situations. Therefore, international regularized organizations such as the international atomic energy agency (IAEA) or the international commission on radiological protection (IRCP) should provide new DRL ranges.

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Spontaneous Pneumomediastinum in a 16-Year-Old Patient with SARS-CoV-2 Infection: A North-East Romanian Case.

Spontaneous pneumomediastinum (SPM) associated with SARS-CoV-2 infection is a rare condition but can represent a medical emergency. It is probably related to alveolar damage secondary to SARS-CoV-2 infection, which allows air to escape in the surrounding lung tissue. Cough and airways' barotrauma are also mentioned as contributing mechanisms. Treatment is generally conservative, but surgery may be required in severe cases. This paper presents the case of a 16-year-old girl with COVID-19-associated SPM who was treated conservatively in our department. The clinical course was favorable with resolution of respiratory symptoms and radiological (chest CT scan) image of pneumomediastinum. The patient was discharged 7 days after the confirmation of the initial SP diagnosis with appropriate treatment and recommendations for isolation. The sudden occurrence of chest pain and dyspnea should raise the suspicion of SPM in COVID-19 patients. Close surveillance and proper radiological monitoring are required in such cases. Treatment should be strictly individualized based on clinical course and radiological appearance.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Diagnostic Value of Chest CT scan for COVID-19 Suspected Cases and Its Compatibility with RT-PCR Method: A Survey from Jiroft, Iran

Introduction: Accurate diagnosis of the COVID-19 disease is important. Currently, chest computed tomography (CT) and reverse polymerase chain reaction (RT-PCR) are being used for the diagnosis of the COVID-19 disease. This study was performed to evaluate the Chest computed tomography (CT) diagnostic value in comparison with the RT-PCR method among COVID-19 patients. Methods: This cross-sectional study was performed on suspected cases of COVID-19 in Imam Khomeini Hospital, Jiroft, Iran. Studied patients were evaluated via both a chest CT scan and nasopharyngeal swab for SARS-CoV-2 detection. Data was collected using a self-administered checklist, including demographic information, medical history, and symptoms of COVID-19, chest CT scan, and RT-PCR findings. Data were analyzed using SPSS-V21. Results: One thousand and ninety (1090) cases participated in the study;the mean age of the cases of COVID-19 was 48.20± 7.31 years old. The results of the RT-PCR test were 410 (37.6%) positive and 680 (62.4%) negative cases. According to the results of RT-PCR, which is the gold standard method, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive values of chest CT were 98.5%. (99.4-96.8 CI: 95%), 55.7% (59.5 – 51.9 CI: 95%), 71.5% (74.4-69.0 CI: 95%), 57.3% (60.9 – 53.5 CI: 95%), and 98.4% (99.4%-99.6 CI: 95%), respectively. Discussion: The results of the present study showed that a chest CT scan is highly sensitive for the diagnosis of the COVID-19 disease. Therefore, it can be used as a suitable method for screening and early detection, which requires knowledge of its common radiologic patterns. However, the results showed that the use of this method has low specificity, so it cannot be used for definitive diagnosis and should be used as a complementary method concomitant to the RT-PCR test. © 2022 Razzaghi et al.

Convolutional Neural Network (CNN) in COVID-19 Detection: A Case Study with Chest CT Scan Images

Deep Learning, especially Convolutional Neural Net-works (CNN) have been performing very well for the last decade in medical image classification. CNN has already shown a great prospect in detecting COVID-19 from chest X-ray images. However, due to its three dimensional data, chest CT scan images can provide better understanding of the affected area through segmentation in comparison to the chest X-ray images. But the chest CT scan images have not been explored enough to achieve sufficiently good results in comparison to the X-ray images. However, with proper image pre-processing, fine tuning, and optimization of the models better results can be achieved. This work aims in contributing to filling this void in the literature. On this aspect, this work explores and designs both custom CNN model and three other models based on transfer learning: InceptionV3, ResNet50, and VGG19. The best performing model is VGG19 with an accuracy of 98.39% and F-1 score of 98.52%. The main contribution of this work includes: (i) modeling a custom CNN model and three pre-trained models based on InceptionV3, ResNet50, and VGG19 (ii) training and validating the models with a comparatively larger dataset of 1252 COVID-19 and 1230 non-COVID CT images (iii) fine tune and optimize the designed models based on the parameters like number of dense layers, optimizer, learning rate, batch size, decay rate, and activation functions to achieve better results than the most of the state-of-the-art literature (iv) the designed models are made public in [1] for reproducibility by the research community for further developments and improvements. © 2022 IEEE.

A Large Imaging Database and Novel Deep Neural Architecture for Covid-19 Diagnosis

Deep learning methodologies constitute nowadays the main approach for medical image analysis and disease prediction. Large annotated databases are necessary for developing these methodologies;such databases are difficult to obtain and to make publicly available for use by researchers and medical experts. In this paper, we focus on diagnosis of Covid-19 based on chest 3-D CT scans and develop a dual knowledge framework, including a large imaging database and a novel deep neural architecture. We introduce COV19-CT-DB, a very large database annotated for COVID-19 that consists of 7,750 3-D CT scans, 1,650 of which refer to COVID-19 cases and 6,100 to non-COVID19 cases. We use this database to train and develop the RACNet architecture. This architecture performs 3-D analysis based on a CNN-RNN network and handles input CT scans of different lengths, through the introduction of dynamic routing, feature alignment and a mask layer. We conduct a large experimental study that illustrates that the RACNet network has the best performance compared to other deep neural networks i) when trained and tested on COV19-CT-DB;ii) when tested, or when applied, through transfer learning, to other public databases. © 2022 IEEE.

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features.

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.

Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.

Concurrent COVID-19 and pneumocystis carinii pneumonia in a patient subsequently found to have underlying hairy cell leukemia.

SARS-CoV-2 infection manifestation has great diversity and it becomes even greater while co-infection occurs or there is a serious underlying disease in an affected patient. In this case report, we present a case of a 71-year-old man who underwent a chest CT scan following the development of fever, weakness, and pulmonary symptoms. Chest CT scan showed segmental consolidation with centrilobular nodular infiltration, ground glass opacifications in the inferior segment of the left upper and lower lobes, and left lung pleural thickening which was atypical for either COVID-19 infection or pneumocystis carinii pneumonia but his SARS-CoV-2 PCR result was positive and he received COVID-19 treatment. His symptoms recurred after a few months with the same chest CT findings and subsequent bronchoalveolar lavage revealed the presence of pneumocystis carinii infection. Consequently, he received cotrimoxazole which caused improvement in symptoms, nonetheless splenomegaly and anemia remained in his clinical and laboratory investigation. Accordingly, bone marrow study and flow cytometry was done and confirmed the previously undiagnosed hairy cell leukemia. This case accentuates the fact that when we face atypical clinical or paraclinical features in a COVID-19 patient, we should explore for coinfection or unknown underlying diseases.