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논문 기본 정보

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학술저널
저자정보
Kang Myeongkyun (Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Korea.) Hong Kyung Soo (Division of Pulmonology and Allergy Department of Internal Medicine Regional Center for Respiratory) Chikontwe Philip (Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Korea.) Luna Miguel (Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Korea.) Jang Jong Geol (Division of Pulmonology and Allergy Department of Internal Medicine Regional Center for Respiratory) Park Jongsoo (Department of Radiology Seoul National University Hospital Seoul Korea.) Shin Kyeong-Cheol (Division of Pulmonology and Allergy Department of Internal Medicine Regional Center for Respiratory) Park Sang Hyun (Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Korea.) Ahn June Hong (Division of Pulmonology and Allergy Department of Internal Medicine Regional Center for Respiratory)
저널정보
대한의학회 Journal of Korean Medical Science Journal of Korean Medical Science Vol.36 No.5
발행연도
2021.1
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1 - 14 (14page)

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Background: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. Methods: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. Results: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. Conclusion: Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.
#COVID-19 #Pneumonia #Bacterial #Computed Tomography #Deep Learning #Cluster Analysis

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참고문헌 (30)

참고문헌 신청
Guan WJ / 2020 / Clinical characteristics of coronavirus disease 2019in China / N Engl J Med 382(18):1708~1720 google schola Xie X / 2020 / Chest CT for typical coronavirus disease 2019(COVID-19)pneumonia : relationship to negative RT-PCR testing / Radiology 296(2):E41~E45 google schola Huang P / 2020 / Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion / Radiology 295(1):22~23 google schola Fang Y / 2020 / Sensitivity of chest CT for COVID-19 : comparison to RT-PCR / Radiology 296(2):E115~E117 google schola Song F / 2020 / Emerging 2019 novel coronavirus(2019-nCoV)pneumonia / Radiology 295(1):210~217 google schola

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