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1.
6th IFIP TC 5 International Conference on Computer, Communication, and Signal Processing, ICCSP 2022 ; 651 IFIP:36-45, 2022.
Article in English | Scopus | ID: covidwho-1971576

ABSTRACT

The ongoing Coronavirus disease (COVID-19) pandemic still necessitates emphasis on diagnosis and management of the outbreaks due to the emergence of new variants. This paper is an extensive survey on the implementation of Deep Learning (DL) models used for diagnosing COVID-19 from chest imaging, enriched with quantitative measures and regulatory aspects. The authors have searched, collated and categorised various models and techniques that reported different architectures with respect to COVID-19 diagnosis in the literature. This survey also briefs about quantifying metrics and the reported results are enumerated, also regulatory frameworks for public use of Artificial Intelligence (AI) in medical devices are comprehended. © 2022, IFIP International Federation for Information Processing.

2.
Neural Comput Appl ; 34(16): 14003-14012, 2022.
Article in English | MEDLINE | ID: covidwho-1941735

ABSTRACT

COVID-19 has taken a toll on the entire world, rendering serious illness and high mortality rate. In the present day, when the globe is hit by a pandemic, those suspected to be infected by the virus need to confirm its presence to seek immediate medical attention to avoid adverse outcomes and also to prevent further transmission of the virus in their close contacts by ensuring timely isolation. The most reliable laboratory testing currently available is the reverse transcription-polymerase chain reaction (RT-PCR) test. Although the test is considered gold standard, 20-25% of results can still be false negatives, which has lately led physicians to recommend medical imaging in specific cases. Our research examines the aspect of chest imaging as a method to diagnose COVID-19. This work is not directed to establish an alternative to RT-PCR, but to aid physicians in determining the presence of virus in medical images. As the disease presents lung involvement, it provides a basis to explore computer vision for classification in radiographic images. In this paper, authors compare the performance of various models, namely ResNet-50, EfficientNetB0, VGG-16 and a custom convolutional neural network (CNN) for detecting the presence of virus in chest computed tomography (CT) scan and chest X-ray images. The most promising results have been derived by using ResNet-50 on CT scans with an accuracy of 98.9% and ResNet-50 on X-rays with an accuracy of 98.7%, which offer an opportunity to further explore these methods for prospective use.

3.
Medical Imaging 2022: Computer-Aided Diagnosis ; 12033, 2022.
Article in English | Scopus | ID: covidwho-1923076

ABSTRACT

Automated analysis of chest imaging in coronavirus disease (COVID-19) has mostly been performed on smaller datasets leading to overfitting and poor generalizability. Training of deep neural networks on large datasets requires data labels. This is not always available and can be expensive to obtain. Self-supervision is being increasingly used in various medical imaging tasks to leverage large amount of unlabeled data during pretraining. Our proposed approach pretrains a vision transformer to perform two self-supervision tasks - image reconstruction and contrastive learning on a Chest Xray (CXR) dataset. In the process, we generate more robust image embeddings. The reconstruction module models visual semantics within the lung fields by reconstructing the input image through a mechanism which mimics denoising and autoencoding. On the other hand, the constrastive learning module learns the concept of similarity between two texture representations. After pretraining, the vision transformer is used as a feature extractor towards a clinical outcome prediction task on our target dataset. The pretraining multi-kaggle dataset comprises 27499 CXR scans while our target dataset contains 530 images. Specifically, our framework predicts ventilation and mortality outcomes for COVID-19 positive patients using baseline CXR. We compare our method against a baseline approach using pretrained ResNet50 features. Experimental results demonstrate that our proposed approach outperforms the supervised method. © 2022 SPIE.

4.
J Med Imaging (Bellingham) ; 9(3): 034003, 2022 May.
Article in English | MEDLINE | ID: covidwho-1901880

ABSTRACT

Purpose: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. Approach: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). Results: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ( AUC = 0.67 [0.58 to 0.76]; accuracy = 77 % ). Model II performed the best ( AUC = 0.78 [0.71 to 0.86]; accuracy = 81 % ). Model III was equivalent ( AUC = 0.75 [0.67 to 0.84]; accuracy = 80 % ). Both models II and III outperformed model I ( AUC difference = 0.11 [0.02 to 0.19], p = 0.01 ; AUC difference = 0.08 [0.01 to 0.15], p = 0.04 , respectively). Model II and III results did not change significantly when POv was replaced by POa. Conclusions: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.

5.
BMJ Open ; 12(6): e059110, 2022 06 13.
Article in English | MEDLINE | ID: covidwho-1891837

ABSTRACT

OBJECTIVE: This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection. DESIGN: This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT. SETTING: A tertiary hospital in Sao Paulo, Brazil. PARTICIPANTS: 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years. PRIMARY OUTCOME MEASURE: A predictive clinical model for lung lesion detection on chest CT. RESULTS: There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07). CONCLUSION: A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.


Subject(s)
COVID-19 , Adolescent , Adult , Brazil/epidemiology , COVID-19/diagnosis , Humans , Lung/diagnostic imaging , Prospective Studies , SARS-CoV-2 , Survivors
6.
World J Radiol ; 14(1): 13-18, 2022 Jan 28.
Article in English | MEDLINE | ID: covidwho-1884585

ABSTRACT

The pandemic of novel coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diabetes mellitus is a risk factor for developing severe illness and a leading cause of death in patients with COVID-19. Diabetes can precipitate hyperglycaemic emergencies and cause prolonged hospital admissions. Insulin resistance is thought to cause endothelial dysfunction, alveolar capillary micro-angiopathy and interstitial lung fibrosis through pro-inflammatory pathways. Autopsy studies have also demonstrated the presence of microvascular thrombi in affected sections of lung, which may be associated with diabetes. Chest imaging using x-ray (CXR) and computed tomography (CT) of chest is used to diagnose, assess disease progression and severity in COVID-19. This article reviews current literature regarding chest imaging findings in patients with diabetes affected by COVID-19. A literature search was performed on PubMed. Patients with diabetes infected with SARS-CoV-2 are likely to have more severe infective changes on CXR and CT chest imaging. Severity of airspace consolidation on CXR is associated with higher mortality, particularly in the presence of co-morbidities such as ischaemic heart disease. Poorly controlled diabetes is associated with more severe acute lung injury on CT. However, no association has been identified between poorly-controlled diabetes and the incidence of pulmonary thromboembolism in patients with COVID-19.

7.
Comput Biol Med ; 145: 105466, 2022 06.
Article in English | MEDLINE | ID: covidwho-1763670

ABSTRACT

Fast and accurate diagnosis is critical for the triage and management of pneumonia, particularly in the current scenario of a COVID-19 pandemic, where this pathology is a major symptom of the infection. With the objective of providing tools for that purpose, this study assesses the potential of three textural image characterisation methods: radiomics, fractal dimension and the recently developed superpixel-based histon, as biomarkers to be used for training Artificial Intelligence (AI) models in order to detect pneumonia in chest X-ray images. Models generated from three different AI algorithms have been studied: K-Nearest Neighbors, Support Vector Machine and Random Forest. Two open-access image datasets were used in this study. In the first one, a dataset composed of paediatric chest X-ray, the best performing generated models achieved an 83.3% accuracy with 89% sensitivity for radiomics, 89.9% accuracy with 93.6% sensitivity for fractal dimension and 91.3% accuracy with 90.5% sensitivity for superpixels based histon. Second, a dataset derived from an image repository developed primarily as a tool for studying COVID-19 was used. For this dataset, the best performing generated models resulted in a 95.3% accuracy with 99.2% sensitivity for radiomics, 99% accuracy with 100% sensitivity for fractal dimension and 99% accuracy with 98.6% sensitivity for superpixel-based histons. The results confirm the validity of the tested methods as reliable and easy-to-implement automatic diagnostic tools for pneumonia.


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , COVID-19/diagnostic imaging , Child , Humans , Pandemics , SARS-CoV-2 , X-Rays
8.
J Am Coll Radiol ; 19(3): 415-422, 2022 03.
Article in English | MEDLINE | ID: covidwho-1637460

ABSTRACT

PURPOSE: The aim of this study was to evaluate radiology imaging volumes at distinct time periods throughout the coronavirus disease 2019 (COVID-19) pandemic as a function of regional COVID-19 hospitalizations. METHODS: Radiology imaging volumes and statewide COVID-19 hospitalizations were collected, and four 28-day time periods throughout the COVID-19 pandemic of 2020 were analyzed: pre-COVID-19 in January, the "first wave" of COVID-19 hospitalizations in April, the "recovery" time period in the summer of 2020 with a relative nadir of COVID-19 hospitalizations, and the "third wave" of COVID-19 hospitalizations in November. Imaging studies were categorized as inpatient, outpatient, or emergency department on the basis of patient location at the time of acquisition. A Mann-Whitney U test was performed to compare daily imaging volumes during each discrete 28-day time period. RESULTS: Imaging volumes overall during the first wave of COVID-19 infections were 55% (11,098/20,011; P < .001) of pre-COVID-19 imaging volumes. Overall imaging volumes returned during the recovery time period to 99% (19,915/20,011; P = .725), and third-wave imaging volumes compared with the pre-COVID-19 period were significantly lower in the emergency department at 88.8% (7,951/8,955; P < .001), significantly higher for outpatients at 115.7% (8,818/7,621; P = .008), not significantly different for inpatients at 106% (3,650/3,435; P = .053), and overall unchanged when aggregated together at 102% (20,419/20,011; P = .629). CONCLUSIONS: Medical imaging rebounded after the first wave of COVID-19 hospitalizations, with relative stability of utilization over the ensuing phases of the pandemic. As widespread COVID-19 vaccination continues to occur, future surges in COVID-19 hospitalizations will likely have a negligible impact on imaging utilization.


Subject(s)
COVID-19 , Radiology , COVID-19/epidemiology , COVID-19 Vaccines , Humans , Pandemics , Retrospective Studies , SARS-CoV-2
9.
Emerg Radiol ; 29(2): 235-241, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1611418

ABSTRACT

BACKGROUND: The necessity to identify and isolate COVID-19 patients to avoid intrahospital cross infections is particularly felt as a challenge. Clinically occult SARS-CoV-2 infection among patients admitted to the hospital is always considered a risk during the pandemic. The aim of our study is to describe the application of CT scan to reveal unexpected COVID-19 in patients needing hospital admission. METHOD: In our emergency department, we prospectively enrolled adult patients needing hospital admission, without symptoms suspected of COVID-19, and showing negative reverse transcriptase-polymerase chain reaction (RT-PCR) swab test. CT scan was performed to diagnose clinically occult COVID-19 pneumonia. All the exams were read and discussed retrospectively by two expert radiologists and assigned to one of 4 exclusive diagnoses: typical (typCT), indeterminate (indCT), atypical (atyCT), negative (negCT). The clinical characteristics and final diagnoses were described and compared with the results of CT scans. RESULTS: From May 25 to August 18, 2020, we prospectively enrolled 197 patients. They showed 122 negCT, 52 atyCT, 22 indCT, and 1 typCT. Based on the CT imaging, the prevalence of suspected clinically occult COVID-19 pneumonia was 11.6% (23 patients). None had confirmation of SARS-CoV-2 infection after the hospital stay. Nineteen patients had negative serial RT-PCR while in 4 cases, the infection was excluded by clinical follow-up or appearance of positivity of RT-PCR after months. CONCLUSION: Our descriptive analysis confirms that CT scan cannot be considered a valid tool to screen clinically occult COVID-19, when the asymptomatic patients need hospitalization for other conditions. Application of personnel protections and distancing among patients remains the best strategies to limit the possibility of intrahospital cross-infections.


Subject(s)
COVID-19 , Adult , Emergency Service, Hospital , Hospitalization , Hospitals , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
10.
Medicina (Kaunas) ; 57(11)2021 Oct 22.
Article in English | MEDLINE | ID: covidwho-1480868

ABSTRACT

Background and Objectives: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: Out of 117 CT scans of 75 patients with COVID-19 admitted to our hospital between April and June 2020, we retrospectively analyzed 79 CT scans that had a definite time of onset and were performed prior to any medication intervention. Patients were grouped according to the presence or absence of increased oxygen demand after CT scan. Quantitative volume data of lung opacity were measured automatically using a deep learning-based image analysis system. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Results: All 79 CT scans were included (median age, 62 years (interquartile range, 46-77 years); 56 (70.9%) were male. The volume of opacity was significantly higher for the increased oxygen demand group than for the nonincreased oxygen demand group (585.3 vs. 132.8 mL, p < 0.001). The sensitivity, specificity, and AUC were 76.5%, 68.2%, and 0.737, respectively, in the prediction of increased oxygen demand. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management.


Subject(s)
COVID-19 , Deep Learning , Pneumonia , Humans , Lung/diagnostic imaging , Male , Middle Aged , Oxygen , Pneumonia/diagnostic imaging , Retrospective Studies , SARS-CoV-2
11.
J Clin Med ; 9(12)2020 Dec 21.
Article in English | MEDLINE | ID: covidwho-1463718

ABSTRACT

Patients receiving mechanical ventilation for coronavirus disease 2019 (COVID-19) related, moderate-to-severe acute respiratory distress syndrome (CARDS) have mortality rates between 76-98%. The objective of this retrospective cohort study was to identify differences in prone ventilation effects on oxygenation, pulmonary infiltrates (as observed on chest X-ray (CXR)), and systemic inflammation in CARDS patients by survivorship and to identify baseline characteristics associated with survival after prone ventilation. The study cohort included 23 patients with moderate-to-severe CARDS who received prone ventilation for ≥16 h/day and was segmented by living status: living (n = 6) and deceased (n = 17). Immediately after prone ventilation, PaO2/FiO2 improved by 108% (p < 0.03) for the living and 150% (p < 3 × 10-4) for the deceased. However, the 48 h change in lung infiltrate severity in gravity-dependent lung zones was significantly better for the living than for the deceased (p < 0.02). In CXRs of the lower lungs before prone ventilation, we observed 5 patients with confluent infiltrates bilaterally, 12 patients with ground-glass opacities (GGOs) bilaterally, and 6 patients with mixed infiltrate patterns; 80% of patients with confluent infiltrates were alive vs. 8% of patients with GGOs. In conclusion, our small study indicates that CXRs may offer clinical utility in selecting patients with moderate-to-severe CARDS who will benefit from prone ventilation. Additionally, our study suggests that lung infiltrate severity may be a better indicator of patient disposition after prone ventilation than PaO2/FiO2.

12.
J Med Imaging Radiat Oncol ; 2021 Oct 05.
Article in English | MEDLINE | ID: covidwho-1455484

ABSTRACT

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has infected over 215 million individuals worldwide. Chest radiographs (CXR) and computed tomography (CT) have assisted with diagnosis and assessment of COVID-19. Previous reports have described peripheral and lower zone predominant opacities on chest radiographs. Whilst the most common patterns on CT are bilateral, peripheral basal predominant ground glass opacities (Wong et al., Radiology, 296, 2020, E72; Karimian and Azami, Pol J Radiol, 86, 2021, e31). This study describes the imaging findings in an Australian tertiary hospital population. METHODS: COVID-PCR-positive patients who had chest imaging (CXR, CT and ventilation perfusion (V/Q) scans) from January 2020 to August 2020 were included. Distribution, location and pattern of involvement was recorded. Evaluation of the assessors was performed using Fleiss Kappa calculations for review of radiographic findings and qualitative analysis of CT findings. RESULTS: A total of 681 studies (616 CXRs, 59 CTs, 6 V/Q) from 181 patients were reviewed. The most common chest radiograph finding was bilateral lower lobe predominant diffuse opacification and most common CT pattern being ground glass opacities. Of the CT imaging, 33 were CT Pulmonary Angiograms of which five demonstrated acute pulmonary emboli. There was good inter-rater agreement between radiologists in assessment of imaging appearances on CXR (kappa 0.29-0.73) and CT studies. CONCLUSION: A review of imaging in an Australian tertiary hospital demonstrates similar patterns of COVID-19 infection on chest X-ray and CT imaging when compared to the international population.

13.
Front Artif Intell ; 4: 612914, 2021.
Article in English | MEDLINE | ID: covidwho-1348578

ABSTRACT

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

14.
Front Pediatr ; 9: 668484, 2021.
Article in English | MEDLINE | ID: covidwho-1268271

ABSTRACT

Since its appearance in Wuhan in mid-December 2019, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) related 19 coronavirus disease (COVID-19) has spread dramatically worldwide. It soon became apparent that the incidence of pediatric COVID-19 was much lower than the adult form. Morbidity in children is characterized by a variable clinical presentation and course. Symptoms are similar to those of other acute respiratory viral infections, the upper airways being more affected than the lower airways. Thus far, over 90% of children who tested positive for the virus presented mild or moderate symptoms and signs. Most children were asymptomatic, and only a few cases were severe, unlike in the adult population. Deaths have been rare and occurred mainly in children with underlying morbidity. Factors as reduced angiotensin-converting enzyme receptor expression, increased activation of the interferon-related innate immune response, and trained immunity have been implicated in the relative resistance to COVID-19 in children, however the underlying pathogenesis and mechanism of action remain to be established. While at the pandemic outbreak, mild respiratory manifestations were the most frequently described symptoms in children, subsequent reports suggested that the clinical course of COVID-19 is more complex than initially thought. Thanks to the experience acquired in adults, the diagnosis of pediatric SARS-CoV-2 infection has improved with time. Data on the treatment of children are sparse, however, several antiviral trials are ongoing. The purpose of this narrative review is to summarize current understanding of pediatric SARS-CoV-2 infection and provide more accurate information for healthcare workers and improve the care of patients.

15.
Biomed Eng Adv ; 1: 100003, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1157145

ABSTRACT

People suspected of having COVID-19 need to know quickly if they are infected, so they can receive appropriate treatment, self-isolate, and inform those with whom they have been in close contact. Currently, the formal diagnosis of COVID-19 requires a laboratory test (RT-PCR) on samples taken from the nose and throat. The RT-PCR test requires specialized equipment and takes at least 24 h to produce a result. Chest imaging has demonstrated its valuable role in the development of this lung disease. Fast and accurate diagnosis of COVID-19 is possible with the chest X-ray (CXR) and computed tomography (CT) scan images. Our manuscript aims to compare the performances of chest imaging techniques in the diagnosis of COVID-19 infection using different convolutional neural networks (CNN). To do so, we have tested Resnet-18, InceptionV3, and MobileNetV2, for CT scan and CXR images. We found that the ResNet-18 has the best overall precision and sensitivity of 98.5% and 98.6%, respectively, the InceptionV3 model has achieved the best overall specificity of 97.4%, and the MobileNetV2 has obtained a perfect sensitivity for COVID-19 cases. All these performances have occurred with CT scan images.

16.
BMJ Open ; 11(3): e045120, 2021 03 05.
Article in English | MEDLINE | ID: covidwho-1119316

ABSTRACT

OBJECTIVES: Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images. DESIGN: A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians. SETTING: Two tertiary Canadian hospitals. PARTICIPANTS: 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE). RESULTS: The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01. CONCLUSIONS: A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Lung/diagnostic imaging , Neural Networks, Computer , Pulmonary Edema/diagnostic imaging , Respiratory Distress Syndrome/diagnostic imaging , Canada , Diagnosis, Differential , Humans
17.
World J Pediatr ; 17(1): 79-84, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1064617

ABSTRACT

BACKGROUND: This study aimed to reveal the differences between coronavirus disease 2019 (COVID-19) infections and non-COVID-19 respiratory tract infections in pediatric patients. METHODS: Sixty pediatric patients admitted to the hospital between March 11, 2020 and April 15, 2020 with respiratory tract infections were evaluated retrospectively. Among them, 20 patients with reverse transcription-polymerase chain reaction (RT-PCR) tests and chest computed tomography (CT) examinations were included in the study. According to the RT-PCR test results, the patients were divided into the COVID-19 and non-COVID-19 groups. The clinical observations, laboratory results, and radiological features from the two groups were then compared. RESULTS: According to the RT-PCR test results, 12 patients were assigned to the COVID-19 group and 8 to the non-COVID-19 group. There were no significant differences between the two groups in terms of clinical or laboratory features. In terms of radiological features, the presence of bronchiectasis and peribronchial thickening was statistically significantly higher in the non-COVID-19 group (P = 0.010 and P = 0.010, respectively). CONCLUSIONS: In pediatric cases, diagnosing COVID-19 using radiological imaging methods plays an important role in determining the correct treatment approach by eliminating the possibility of other infections.


Subject(s)
COVID-19/diagnostic imaging , Respiratory Tract Infections/diagnostic imaging , Tomography, X-Ray Computed , Child , Diagnosis, Differential , Female , Humans , Infant , Male , Retrospective Studies
18.
Front Med (Lausanne) ; 7: 571396, 2020.
Article in English | MEDLINE | ID: covidwho-1038611

ABSTRACT

Majority of patients with 2019 novel coronavirus infection (COVID-19) exhibit mild symptoms. Identification of COVID-19 patients with mild symptoms who might develop into severe or critical illness is essential to save lives. We conducted an observational study in a dedicated make-shift hospital for adult male COVID-19 patients with mild symptoms between February and March 2020. Baseline characteristics, medical history, and clinical presentation were recorded. Laboratory tests and chest computed tomography were performed. Patients were observed until they were either transferred to a hospital for advanced care owing to disease exacerbation or were discharged after improvement. Patients were grouped based on their chest imaging findings or short-term outcomes. A total of 125 COVID-19 patients with mild symptoms were enrolled. Of these, 7 patients were transferred for advanced care while 118 patients were discharged after improvement and showed no disease recurrence during an additional 28-day follow-up period. Eighty-five patients (68.0%) had abnormal chest imaging findings. Patients with abnormal chest imaging findings were more likely to have disease deterioration and require advanced care as compared to those with normal chest imaging findings. Patients with deteriorated outcomes were more likely to have low peripheral blood oxygen saturation and moderately-elevated body temperature. There were no significant differences between patients with deteriorated or improved outcomes with respect to age, comorbidities, or other clinical symptoms (including nasal congestion, sore throat, cough, hemoptysis, sputum production, shortness of breath, fatigue, headache, nausea or vomiting, diarrhea). Abnormal chest imaging findings, low peripheral blood oxygen saturation, and elevated temperature were associated with disease deterioration in adult male COVID-19 patients with mild clinical symptoms. Clinical Trial Registration: https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S0009RA3&selectaction=Edit&uid=U0003F4L&ts=2&cx=-ajpsbw, identifier NCT04346602.

19.
Am J Med Sci ; 361(4): 427-435, 2021 04.
Article in English | MEDLINE | ID: covidwho-1014310

ABSTRACT

The subpleural sparing pattern is a common finding on computed tomography (CT) of the lungs. It comprises of pulmonary opacities sparing the lung peripheries, typically 1cm and less from the pleural surface. This finding has a variety of causes, including idiopathic, inflammatory, infectious, inhalational, cardiac, traumatic, and bleeding disorders. Specific disorders that can cause subpleural sparing patterns include nonspecific interstitial pneumonia (NSIP), organizing pneumonia (OP), pulmonary alveolar proteinosis (PAP), diffuse alveolar hemorrhage (DAH), vaping-associated lung injury (VALI), cracked lung, pulmonary edema, pneumocystis jirovecii pneumonia (PJP), pulmonary contusion, and more recently, Coronavirus disease 2019 (COVID-19) pneumonia. Knowledge of the many etiologies of this pattern can be useful in preventing diagnostic errors. In addition, although the etiology of subpleural sparing pattern is frequently indistinguishable during an initial radiologic evaluation, the differences in location of opacities in the lungs, as well as the presence of additional radiologic findings, patient history, and clinical presentation, can often be useful to suggest the appropriate diagnosis. We did a comprehensive search on Pubmed and Google Scholar database using keywords of "subpleural sparing," "peripheral sparing," "sparing of peripheries," "CT chest," "chest imaging," and "pulmonary disease." This review aims to describe the primary differential diagnosis of subpleural sparing pattern seen on chest imaging with a strong emphasis on clinical and radiographic findings. We also discuss the pathogenesis and essential clues that are crucial to narrow the differential diagnosis.


Subject(s)
Pleura/diagnostic imaging , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Diagnosis, Differential , Humans , Lung Diseases/classification , Lung Diseases/diagnosis , Lung Diseases/diagnostic imaging
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