Selected article for: "accuracy auc and lung lesion"

Author: Feng, Youzhen; Liu, Sidong; Cheng, Zhongyuan; Quiroz, Juan; Rezazadegan, Data; Chen, Pingkang; Lin, Qiting; Qian, Long; Liu, Xiaofang; Berkovsky, Shlomo; Coiera, Enrico; Song, Lei; Qiu, Xiaoming; Cai, Xiangran
Title: Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT
  • Cord-id: 9i56zxqi
  • Document date: 2020_8_4
  • ID: 9i56zxqi
    Snippet: Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesio
    Document: Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. [Manuscript last updated on 20 May, 2020.]

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