A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

Pan, Feng; Liu, Bo; Ye, Tianhe; Li, Lingli; Liu, Dehan; Ding, Zezhen; Chen, Guangfeng; Liang, Bo; Yang, Lian; Zheng, Chuansheng

doi:10.1038/s41598-020-80261-w

Cited by 35 publications

(22 citation statements)

References 30 publications

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“…They reached a Dice similarity coefficient (DSC) of 0.798. Pan et al (2021) developed COVID-Lesion Net based on a combination of U-net and Fully convolutional networks.…”

Section: The Pandemic Begins: To the First Peakmentioning

confidence: 99%

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

et al. 2021

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The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

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“…They reached a Dice similarity coefficient (DSC) of 0.798. Pan et al (2021) developed COVID-Lesion Net based on a combination of U-net and Fully convolutional networks.…”

Section: The Pandemic Begins: To the First Peakmentioning

confidence: 99%

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

et al. 2021

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“…This system was constructed using a combination of U-net and fully convolutional networks (40,41), which consists of three different network components: (1) 12 convolutional segments, which included convolutional layer (Conv2d), batch normalization layer, and an activation layer; (2) three max-pooling layers for down-sampling; and (3) three transpose convolutional layer for up-sampling (Figure 2). The development of the COVID-Lesion Net has been described in a previous study (42).…”

Section: Quantitative Ct Analysismentioning

confidence: 99%

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

Pang¹,

Liu

et al. 2021

Front. Med.

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Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients.Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis.Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001).Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

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“…Approaches for lung nodule segmentation involved the detection of a volume of interest (VOI) covering the nodule area and segmentation inside this VOI. 3 , 4 These methods can be generally classified into machine-learning methods based on China big date, which covered more than 10 provinces and more than 50 hospitals), only identified 18 pulmonary nodules ( Figure 2 ), which are lower than 10% of the real data. Besides, all of these were judged as low-risk nodules.…”

Section: Case Reportmentioning

confidence: 99%

Artificial Intelligence System Application in Miliary Lung Metastasis: Experience from a Rare Case

Chen¹,

Li²,

Jiang³

et al. 2021

RMHP

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Introduction: Miliary intrapulmonary carcinomatosis (MIPC) is very rare in the existing literature. We reported a lung adenocarcinoma patient presented with over 200 uniform size pulmonary nodules in all lung lobes at the initial examination. The application of artificial intelligence (AI) in lung cancer has been gradually reported, but not yet reported in MIPC. The application of AI in this rare disease is worth exploring. Patient Information: A 57-year-old woman received chest computed tomography (CT) scan because of dry cough, intermittent chest wall and back pain for 3 weeks. CT imaging found over 200 uniform size pulmonary nodules in an evenly dispersed pattern at bilateral lungs with a 38×45mm new creature at the dorsal segment of the lower lobe of the left lung. However, as a very reliable diagnostic assistant system in CT imaging of lung cancer, AI can only identify 18 nodules in such classic metastatic lung cancer case. Conclusion:This case provides classical imaging figures as textbook-like, even though there is no such classic imaging of lung metastases in the existing textbooks. This medical imaging material will impress medical students and help them learn about the disease deeply. This medical imaging material can warn patients to recognize the horror of lung cancer metastasis and has good popularization of science. This medical imaging material presents a new challenge for AI.

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A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

Cited by 35 publications

References 30 publications

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

Artificial Intelligence System Application in Miliary Lung Metastasis: Experience from a Rare Case

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