scholarly journals Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients

2020 ◽  
Author(s):  
Dhurgham Al-Karawi ◽  
Shakir Al-Zaidi ◽  
Nisreen Polus ◽  
Sabah Jassim
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Real Time ◽  
False Negative ◽  
Chest Ct ◽  
Average Accuracy ◽  
Gabor Feature ◽  
And Performance ◽  
Learning Analysis ◽  
Chest Ct Scan

AbstractThis paper reports on the development and performance of machine learning schemes for the analysis of Chest CT Scan images of Coronavirus COVID-19 patients and demonstrates significant success in efficiently and automatically testing for COVID-19 infection. In particular, an innovative frequency domain algorithm, to be called FFT-Gabor scheme, will be shown to predict in almost real-time the state of the patient with an average accuracy of 95.37%, sensitivity 95.99% and specificity 94.76%. The FFT-Gabor scheme is adequately informative in that clinicians can visually examine the FFT-Gabor feature to support their final diagnostic.Key StrengthsThe proposed FFT-Gabor scheme is an automatic machine learning scheme that works in real time and achieves significantly high accuracy with very low false negative, and can provide supporting evidences of the predicted decision by visually displaying the final features upon which decision is made. This scheme will be most beneficial when used in addition to the RT-PCR swab test of non-symptomatic cases.

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features

2021 ◽  
pp. 139-147
Author(s):  
Asma Pourhoseingholi ◽  
Mohsen Vahedi ◽  
Samira Chaibakhsh ◽  
Mohamad Amin Pourhoseingholi ◽  
Amir Vahedian-Azimi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
Infection Status ◽  
Learning Analysis ◽  
Chest Ct Scan

Cancer surgery during the COVID‐19 pandemic: The experience of a comprehensive cancer center performing preoperative screening by RT‐PCR and chest CT scan

2021 ◽  
Vol 123 (4) ◽  
pp. 815-822
Author(s):  
Joanne Guerlain ◽  
Fabienne Haroun ◽  
Alexandra Voicu ◽  
Charles Honoré ◽  
Franck Griscelli ◽  
...  
Keyword(s):  
Ct Scan ◽  
Chest Ct ◽  
Cancer Surgery ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Preoperative Screening ◽  
Rt Pcr ◽  
Chest Ct Scan

scholarly journals A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fatemeh Khatami ◽  
Mohammad Saatchi ◽  
Seyed Saeed Tamehri Zadeh ◽  
Zahra Sadat Aghamir ◽  
Alireza Namazi Shabestari ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Reverse Transcriptase ◽  
Ct Scan ◽  
Predictive Value ◽  
Meta Analysis ◽  
Chest Ct ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Polymerase Chain ◽  
Chest Ct Scan

AbstractNowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85–90%), 46% (95% CI 29–63%), 69% (95% CI 56–72%), and 89% (95% CI 82–96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

scholarly journals The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vikram rao Bollineni ◽  
Koenraad Hans Nieboer ◽  
Seema Döring ◽  
Nico Buls ◽  
Johan de Mey
Keyword(s):  
Ct Scan ◽  
Predictive Value ◽  
Ct Imaging ◽  
Chest Ct ◽  
University Hospital ◽  
Rt Pcr ◽  
Ct Findings ◽  
Epidemic Area ◽  
Chest Ct Scan ◽  
Pcr Test

Abstract Background To evaluate the clinical value of the chest CT scan compared to the reference standard real-time polymerase chain reaction (RT-PCR) in COVID-19 patients. Methods From March 29th to April 15th of 2020, a total of 240 patients with respiratory distress underwent both a low-dose chest CT scan and RT-PCR tests. The performance of chest CT in diagnosing COVID-19 was assessed with reference to the RT-PCR result. Two board-certified radiologists (mean 24 years of experience chest CT), blinded for the RT-PCR result, reviewed all scans and decided positive or negative chest CT findings by consensus. Results Out of 240 patients, 60% (144/240) had positive RT-PCR results and 89% (213/240) had a positive chest CT scans. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of chest CT in suggesting COVID-19 were 100% (95% CI: 97–100%, 144/240), 28% (95% CI: 19–38%, 27/240), 68% (95% CI: 65–70%) and 100%, respectively. The diagnostic accuracy of the chest CT suggesting COVID-19 was 71% (95% CI: 65–77%). Thirty-three patients with positive chest CT scan and negative RT-PCR test at baseline underwent repeat RT-PCR assay. In this subgroup, 21.2% (7/33) cases became RT-PCR positive. Conclusion Chest CT imaging has high sensitivity and high NPV for diagnosing COVID-19 and can be considered as an alternative primary screening tool for COVID-19 in epidemic areas. In addition, a negative RT-PCR test, but positive CT findings can still be suggestive of COVID-19 infection.

A 71-Year-Old Man With Fever, Productive Cough, and Tree-in-Bud Pattern on Chest CT Scan

CHEST Journal ◽  
2013 ◽  
Vol 144 (2) ◽  
pp. 700-703 ◽  
Author(s):  
Sarah Bastawrous ◽  
Jan V. Hirschmann
Keyword(s):  
Ct Scan ◽  
Chest Ct ◽  
Productive Cough ◽  
Chest Ct Scan

Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis

2021 ◽  
pp. 2101344
Author(s):  
Alienor Campredon ◽  
Enzo Battistella ◽  
Clémence Martin ◽  
Isabelle Durieu ◽  
Laurent Mely ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Ct Scan ◽  
Structural Changes ◽  
Clinical Status ◽  
Chest Ct ◽  
Ct Scans ◽  
Wall Thickening ◽  
Unsupervised Machine Learning ◽  
One Year

ObjectivesLumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). This study aimed to assess lung structural changes after one year of lumacaftor-ivacaftor treatment, and to use unsupervised machine learning to identify morphological phenotypes of lung disease that are associated with response to lumacaftor-ivacaftor.MethodsAdolescents and adults with CF from the French multicenter real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pretherapeutic and follow-up chest computed tomography (CT)-scans available. CT scans were visually scored using a modified Bhalla score. A k-mean clustering method was performed based on 120 radiomics features extracted from unenhanced pretherapeutic chest CT scans.ResultsA total of 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (−1.40±1.53 points compared with pretherapeutic CT; p<0.001). This finding was related to a significant decrease in mucus plugging (−0.35±0.62 points; p<0.001), bronchial wall thickening (−0.24±0.52 points; p<0.001) and parenchymal consolidations (−0.23±0.51 points; p<0.001). Cluster analysis identified 3 morphological clusters. Patients from cluster C were more likely to experience an increase in percent predicted forced expiratory volume in 1 sec (ppFEV1) ≥5 under lumacaftor–ivacaftor than those in the other clusters (54% of responders versus 32% and 33%; p=0.01).ConclusionOne year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pretherapeutic CT scan may help in predicting lung function response under lumacaftor-ivacaftor.

PULMONARY BIRT-HOGG-DUBE SYNDROME: A GOOD REASON TO ROUTINELY PERFORM A LOW DOSE CHEST CT SCAN IN PATIENTS WITH PRIMARY SPONTANEOUS PNEUMOTHORAX?

CHEST Journal ◽  
2018 ◽  
Vol 154 (4) ◽  
pp. 576A
Author(s):  
JINCEY SRIRAM ◽  
IRMA VAN DE BEEK ◽  
PAUL JOHANNESMA ◽  
MICHIEL VAN WERKUM ◽  
TIJMEN VAN DER WEL ◽  
...  
Keyword(s):  
Ct Scan ◽  
Spontaneous Pneumothorax ◽  
Low Dose ◽  
Good Reason ◽  
Chest Ct ◽  
Primary Spontaneous Pneumothorax ◽  
Chest Ct Scan

scholarly journals Chest CT scan as an initial diagnostic method for tuberculosis infection detected by mass screening

2020 ◽  
Author(s):  
Dong-Hyun Joo ◽  
Hyun Woo Lee ◽  
Seo-Young Yoon ◽  
Tae Yun Park ◽  
Eun Young Heo ◽  
...  
Keyword(s):  
Ct Scan ◽  
Mass Screening ◽  
Diagnostic Method ◽  
Chest Ct ◽  
Tuberculosis Infection ◽  
Chest Ct Scan

scholarly journals Post-COVID-19 pulmonary fibrosis and its predictive factors: a prospective study

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mehrdad Nabahati ◽  
Soheil Ebrahimpour ◽  
Reza Khaleghnejad Tabari ◽  
Rahele Mehraeen
Keyword(s):  
Pulmonary Fibrosis ◽  
Ct Scan ◽  
Predictive Factors ◽  
Lung Fibrosis ◽  
Chest Ct ◽  
Clinical Severity ◽  
Secondary Outcome ◽  
Increased Risk ◽  
Chest Ct Scan

Abstract Background We aimed to prospectively assess the lung fibrotic-like changes, as well as to explore their predictive factors, in the patients who survived Coronavirus Disease 2019 (COVID-19) infection. In this prospective cross-sectional study, we recruited patients who had been treated for moderate or severe COVID-19 pneumonia as inpatients and discharged from Rohani hospital in Babol, northern Iran, during March 2020. The clinical severity of COVID-19 pneumonia was classified as per the definition by World Health Organization. We also calculated the CT severity score (CSS) for all patients at admission. Within the 3 months of follow-up, the next chest CT scan was performed. As the secondary outcome, the patients with fibrotic abnormalities in their second CT scan were followed up in the next 3 months. Results Totally, 173 COVID-19 patients were finally included in the study, of whom 57 (32.9%) were male and others were female. The mean age was 53.62 ± 13.67 years old. At 3-month CT follow-up, evidence of pulmonary fibrosis was observed in 90 patients (52.0%). Consolidation (odds ratio [OR] = 2.84), severe disease (OR 2.40), and a higher CSS (OR 1.10) at admission were associated with increased risk of fibrotic abnormalities found at 3-month CT follow-up. Of 62 patients who underwent chest CT scan again at 6 months of follow-up, 41 patients (66.1%) showed no considerable changes in the fibrotic findings, while the rest of 21 patients (33.9%) showed relatively diminished lung fibrosis. Conclusion Post-COVID-19 lung fibrosis was observed in about half of the survivors. Also, patients with severe COVID-19 pneumonia were at a higher risk of pulmonary fibrosis. Moreover, consolidation, as well as a higher CSS, in the initial chest CT scan, was associated with increased risk of post-COVID-19 lung fibrosis. In addition, some patients experienced diminished fibrotic abnormalities in their chest CT on 6-month follow-up, while some others did not.

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