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10.1186/s12938-020-00831-x

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suck abstract from ncbi


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pmid33239006      Biomed+Eng+Online 2020 ; 19 (1): 88
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  • Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection #MMPMID33239006
  • Hussain L; Nguyen T; Li H; Abbasi AA; Lone KJ; Zhao Z; Zaib M; Chen A; Duong TQ
  • Biomed Eng Online 2020[Nov]; 19 (1): 88 PMID33239006show ga
  • BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.
  • |*Machine Learning[MESH]
  • |COVID-19/*complications[MESH]
  • |Humans[MESH]
  • |Image Processing, Computer-Assisted/*methods[MESH]
  • |Lung Diseases/complications/*diagnostic imaging/*virology[MESH]
  • |Radiography, Thoracic/*instrumentation[MESH]


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