Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience

Greffier, Joël; Hoballah, Adel; Sadate, Alexandre; Oliveira, Fabien de; Claret, Pierre‐Géraud; Forges, Hélène de; Loubet, Paul; Mauboussin, Jean-Marc; Hamard, Aymeric; Beregi, Jean‐Paul; Frandon, Julien

doi:10.21037/qims-20-1176

Cited by 22 publications

(24 citation statements)

References 25 publications

(45 reference statements)

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“…At present, there exist three styles of CI approaches: (i) chest X-ray, (ii) chest CT, and (iii) chest ultrasound. Among all three styles of CI approaches, CCT is capable of providing finer resolution than the other two styles (chest X-ray and chest ultrasound), granting visualization of exceptionally small nodules in the lung, and displaying the realistic three-dimensional imaging of the chest ( 5 ). Some COVID-19 lesions are clearly observed in CCT, while they appear opaque in the other two CI approaches (chest X-ray and chest ultrasound) ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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“…According to the Radiological Society of North America Expert Consensus Statement, GGO with peripheral distribution accompanied by consolidation is considered a typical appearance of COVID-19 pneumonia [ 13 ]. In a similar study to describe the diagnostic value of ultra-low-dose chest CT compared to standard dose CT, Greffier et al [ 25 ] found that 97 patients out of 380 cases suspected to have viral pneumonia had CT patterns compatible with a viral pneumonia. Ultra-low-dose CT had a sensitivity of 98.9% and a specificity of 99% compared to standard dose CT as the reference standard to diagnose viral pneumonia pattern.…”

Section: Discussionmentioning

confidence: 99%

Agreement between low-dose and ultra-low-dose chest CT for the diagnosis of viral pneumonia imaging patterns during the COVID-19 pandemic

Bahrami‐Motlagh

Moharamzad

Amoli

et al. 2022

Egypt J Radiol Nucl Med

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Background Chest CT scan has an important role in the diagnosis and management of COVID-19 infection. A major concern in radiologic assessment of the patients is the radiation dose. Research has been done to evaluate low-dose chest CT in the diagnosis of pulmonary lesions with promising findings. We decided to determine diagnostic performance of ultra-low-dose chest CT in comparison to low-dose CT for viral pneumonia during the COVID-19 pandemic. Results 167 patients underwent both low-dose and ultra-low-dose chest CT scans. Two radiologists blinded to the diagnosis independently examined ultra-low-dose chest CT scans for findings consistent with COVID-19 pneumonia. In case of any disagreement, a third senior radiologist made the final diagnosis. Agreement between two CT protocols regarding ground-glass opacity, consolidation, reticulation, and nodular infiltration were recorded. On low-dose chest CT, 44 patients had findings consistent with COVID-19 infection. Ultra-low-dose chest CT had sensitivity and specificity values of 100% and 98.4%, respectively for diagnosis of viral pneumonia. Two patients were falsely categorized to have pneumonia on ultra-low-dose CT scan. Positive predictive value and negative predictive value of ultra-low-dose CT scan were respectively 95.7% and 100%. There was good agreement between low-dose and ultra-low-dose methods (kappa = 0.97; P < 0.001). Perfect agreement between low-dose and ultra-low-dose scans was found regarding diagnosis of ground-glass opacity (kappa = 0.83, P < 0.001), consolidation (kappa = 0.88, P < 0.001), reticulation (kappa = 0.82, P < 0.001), and nodular infiltration (kappa = 0.87, P < 0.001). Conclusion Ultra-low-dose chest CT scan is comparable to low-dose chest CT for detection of lung infiltration during the COVID-19 outbreak while maintaining less radiation dose. It can also be used instead of low-dose chest CT scan for patient triage in circumstances where rapid-abundant PCR tests are not available.

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“…Furthermore, higher BMI may result in lower detectability of emphysema 20 . Interestingly, an ULD CT protocol may also be accurate for evaluation of cystic fibrosis (CF), follow up imaging and detection of viral pneumonia, including COVID‐19 pneumonia 6,22 . However, if the primary indication is interstitial lung disease/abnormality, then an ULD CT protocol is not recommended 6…”

Section: Computed Tomographymentioning

confidence: 99%

Innovations in thoracic imaging: CT, radiomics, AI and x‐ray velocimetry

Vliegenthart

Fouras²,

Jacobs

et al. 2022

Respirology

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In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

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Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience

Cited by 22 publications

References 25 publications

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Agreement between low-dose and ultra-low-dose chest CT for the diagnosis of viral pneumonia imaging patterns during the COVID-19 pandemic

Innovations in thoracic imaging: CT, radiomics, AI and x‐ray velocimetry

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