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Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics.
Moradi Khaniabadi, Pegah; Bouchareb, Yassine; Al-Dhuhli, Humoud; Shiri, Isaac; Al-Kindi, Faiza; Moradi Khaniabadi, Bita; Zaidi, Habib; Rahmim, Arman.
  • Moradi Khaniabadi P; Department of Radiology and Molecular Imaging, College of Medicine and Health Sciences, Sultan Qaboos University, PO. Box 35, PC123, Al Khoud, Muscat, Oman. Electronic address: pegah32121065@gmail.com.
  • Bouchareb Y; Department of Radiology and Molecular Imaging, College of Medicine and Health Sciences, Sultan Qaboos University, PO. Box 35, PC123, Al Khoud, Muscat, Oman. Electronic address: y.bouchareb@squ.edu.om.
  • Al-Dhuhli H; Department of Radiology and Molecular Imaging, College of Medicine and Health Sciences, Sultan Qaboos University, PO. Box 35, PC123, Al Khoud, Muscat, Oman.
  • Shiri I; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland.
  • Al-Kindi F; Department of Radiology, Royal Hospital, Muscat, Oman.
  • Moradi Khaniabadi B; Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Zaidi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Gr
  • Rahmim A; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
Comput Biol Med ; 150: 106165, 2022 Oct 05.
Article in English | MEDLINE | ID: covidwho-2104646
ABSTRACT

OBJECTIVE:

To develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features.

METHODS:

Three hundred CT scans (3-classes 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%) followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation, followed by testing using the test set.

RESULTS:

Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC.

CONCLUSION:

The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article