Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis
Arvin Arian, Mohammad-Mehdi Mehrabi Nejad, Mostafa Zoorpaikar, Navid Hasanzadeh, Saman Sotoudeh-Paima, Shahriar Kolahi, Masoumeh Gity, Hamid Soltanian-Zadeh
PLOS ONE, doi:10.1371/journal.pone.0294899
Background Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.
Objectives This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance.
Subjects and methods A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups.
Results There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and
Author Contributions Conceptualization: Arvin Arian. Validation: Shahriar Kolahi, Masoumeh Gity.
Data curation: Visualization: Navid Hasanzadeh. Writing -original draft: Arvin Arian, Mostafa Zoorpaikar, Saman Sotoudeh-Paima.
Writing -review & editing: Hamid Soltanian-Zadeh.
References
Abkhoo, Shaker, Mehrabinejad, Azadbakht, Sadighi et al., Factors predicting outcome in intensive care unit-admitted COVID-19 patients: using clinical, laboratory, and radiologic characteristics, Critical Care Research and Practice,
doi:10.1155/2021/9941570
Arian, Mehrabinejad, Zoorpaikar, Hasanzadeh, Sotoudeh-Paima et al., COVID-19 & Normal CT Segmentation Dataset, Mendeley Data,
doi:10.17632/pfmgfpwnmm.1
Arru, Ebrahimian, Falaschi, Hansen, Pasche et al., Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia, Clinical Imaging,
doi:10.1016/j.clinimag.2021.06.036
Arunmozhi, Sarojini, Pavithra, Varghese, Deepti et al., Automated detection of COVID-19 lesion in lung CT slices with VGG-UNet and handcrafted features
Cai, Liu, Xue, Luo, Wang et al., CT quantification and machine-learning models for assessment of disease severity and prognosis of COVID-19 patients, Academic radiology,
doi:10.1016/j.acra.2020.09.004
Dong, Tang, Wang, Hui, Gong et al., The role of imaging in the detection and management of COVID-19: a review, IEEE reviews in biomedical engineering,
doi:10.1109/RBME.2020.2990959
Ebrahimian, Homayounieh, Rockenbach, Putha, Raj et al., Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study, Scientific Reports,
doi:10.1038/s41598-020-79470-0
Fang, He, Li, Dong, Yang et al., CT radiomics can help screen the coronavirus disease 2019 (COVID-19): a preliminary study, Science China Information Sciences,
doi:10.1007/s11432-020-2849-3
Hasanzadeh, Paima, Bashirgonbadi, Naghibi, Soltanian-Zadeh, Segmentation of covid-19 infections on ct: Comparison of four unet-based networks
Hofmanninger, Prayer, Pan, Ro ¨hrich, Prosch et al., Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem, European Radiology Experimental,
doi:10.1186/s41747-020-00173-2
Iyer, Raj, Ghildiyal, Nersisson, Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images, PeerJ Computer Science,
doi:10.7717/peerj-cs.368
Lanza, Muglia, Bolengo, Santonocito, Lisi et al., Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation, European radiology,
doi:10.1007/s00330-020-07013-2
Mushtaq, Pennella, Lavalle, Colarieti, Steidler et al., Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients, European radiology,
doi:10.1007/s00330-020-07269-8
Nemoto, Futakami, Kunieda, Yagi, Takeda et al., Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs, Radiological Physics and Technology,
doi:10.1007/s12194-021-00630-6
Phua, Weng, Ling, Egi, Lim et al., Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations, The lancet respiratory medicine,
doi:10.1016/S2213-2600(20)30161-2
Pravitasari, Iriawan, Almuhayar, Azmi, Irhamah et al., UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation, TELKOMNIKA (Telecommunication Computing Electronics and Control),
doi:10.12928/telkomnika.v18i3.14753
Romei, Falaschi, Danna, Airoldi, Tonerini et al., Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis, European radiology,
doi:10.1007/s00330-021-08485-6
Ronneberger, Fischer, Brox, U-Net: Convolutional networks for biomedical image segmentation
Salahshour, Mehrabinejad, Toosi, Gity, Ghanaati et al., Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system, European radiology,
doi:10.1007/s00330-020-07623-w
Salvatore, Dl, Cesare, Alfredo, Giuliano, Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis, La radiologia medica,
doi:10.1007/s11547-020-01293-w
Scapicchio, Chincarini, Ballante, Berta, Bicci et al., A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia, European Radiology Experimental,
doi:10.1186/s41747-023-00334-z
Shaikh, Andersen, Sohail, Mulero, Awan et al., Current landscape of imaging and the potential role for artificial intelligence in the management of COVID-19, Current Problems in Diagnostic Radiology,
doi:10.1067/j.cpradiol.2020.06.009
Shi, Wang, Shi, Wu, Wang et al., Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19, IEEE reviews in biomedical engineering,
doi:10.1109/RBME.2020.2987975
Sotoudeh-Paima, Hasanzadeh, Bashirgonbadi, Aref, Naghibi et al., A Multicentric Evaluation of Deep Learning Models for Segmentation of COVID-19 Lung Lesions on Chest CT Scans, Iranian Journal of Radiology,
doi:10.5812/iranjradiol-117992
Wasilewski, Mruk, Mazur, Po ´łtorak-Szymczak, Sklinda et al., COVID-19 severity scoring systems in radiological imaging-a review, Polish journal of radiology,
doi:10.5114/pjr.2020.98009
Yang, Li, Liu, Zhen, Zhang et al., Chest CT severity score: an imaging tool for assessing severe COVID-19, Radiology: Cardiothoracic Imaging,
doi:10.1148/ryct.2020200047
Yu, Liu, Xu, Zhang, Lan et al., Prediction of the development of pulmonary fibrosis using serial thin-section CT and clinical features in patients discharged after treatment for COVID-19 pneumonia, Korean journal of radiology,
doi:10.3348/kjr.2020.0215
Zhao, Zhong, Xie, Yu, Liu, Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study, Ajr Am J Roentgenol,
doi:10.2214/AJR.20.22976
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'<jats:title>Objectives</jats:title>\n'
'<jats:p>This study investigates the performance of an AI-aided quantification model in '
'predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with '
'radiologists’ performance.</jats:p>\n'
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'<jats:title>Subjects and methods</jats:title>\n'
'<jats:p>A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were '
'recruited in this cross-sectional study. Quantification of the total and compromised lung '
'parenchyma was performed by two expert radiologists using a volumetric image analysis '
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'segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for '
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'<jats:title>Results</jats:title>\n'
'<jats:p>There was an excellent agreement between the radiologist-obtained and AI-assisted '
'measurements (intraclass correlation coefficient = 0.88, <jats:italic>P</jats:italic> < '
'0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the '
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'<jats:p>AI-assisted measurements are similar to quantitative radiologist-obtained '
'measurements in determining lung involvement in COVID-19 subjects.</jats:p>\n'
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'patients: using clinical, laboratory, and radiologic characteristics',
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'author': 'D Dong',
'year': '2020',
'journal-title': 'IEEE reviews in biomedical engineering'},
{ 'issue': '4',
'key': 'pone.0294899.ref011',
'doi-asserted-by': 'crossref',
'DOI': '10.5812/iranjradiol-117992',
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'COVID-19 Lung Lesions on Chest CT Scans',
'volume': '19',
'author': 'S Sotoudeh-Paima',
'year': '2022',
'journal-title': 'Iranian Journal of Radiology'},
{ 'key': 'pone.0294899.ref012',
'unstructured': 'Arian A, Mehrabinejad MM, Zoorpaikar M, Hasanzadeh N, Sotoudeh-Paima S, '
'Kolahi S, et al. COVID-19 & Normal CT Segmentation Dataset. 2023. '
'Mendeley Data. https://doi.org/10.17632/pfmgfpwnmm.1.'},
{ 'issue': '1',
'key': 'pone.0294899.ref013',
'doi-asserted-by': 'crossref',
'first-page': '1',
'DOI': '10.1186/s41747-020-00173-2',
'article-title': 'Automatic lung segmentation in routine imaging is primarily a data '
'diversity problem, not a methodology problem',
'volume': '4',
'author': 'J Hofmanninger',
'year': '2020',
'journal-title': 'European Radiology Experimental'},
{ 'key': 'pone.0294899.ref014',
'doi-asserted-by': 'crossref',
'unstructured': 'Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for '
'biomedical image segmentation. arXiv 2015. arXiv preprint '
'arXiv:150504597. 2015;.',
'DOI': '10.1007/978-3-319-24574-4_28'},
{ 'key': 'pone.0294899.ref015',
'doi-asserted-by': 'crossref',
'unstructured': 'Hasanzadeh N, Paima SS, Bashirgonbadi A, Naghibi M, Soltanian-Zadeh H. '
'Segmentation of covid-19 infections on ct: Comparison of four unet-based '
'networks. In: 2020 27th National and 5th International Iranian '
'Conference on Biomedical Engineering (ICBME). IEEE; 2020. p. 222–225.',
'DOI': '10.1109/ICBME51989.2020.9319412'},
{ 'key': 'pone.0294899.ref016',
'doi-asserted-by': 'crossref',
'first-page': 'e368',
'DOI': '10.7717/peerj-cs.368',
'article-title': 'Performance analysis of lightweight CNN models to segment infectious '
'lung tissues of COVID-19 cases from tomographic images',
'volume': '7',
'author': 'TJ Iyer',
'year': '2021',
'journal-title': 'PeerJ Computer Science'},
{ 'key': 'pone.0294899.ref017',
'doi-asserted-by': 'crossref',
'first-page': '185',
'volume-title': 'Digital Future of Healthcare',
'author': 'S Arunmozhi',
'year': '2021',
'DOI': '10.1201/9781003198796-11'},
{ 'issue': '3',
'key': 'pone.0294899.ref018',
'doi-asserted-by': 'crossref',
'first-page': '1310',
'DOI': '10.12928/telkomnika.v18i3.14753',
'article-title': 'UNet-VGG16 with transfer learning for MRI-based brain tumor '
'segmentation',
'volume': '18',
'author': 'AA Pravitasari',
'year': '2020',
'journal-title': 'TELKOMNIKA (Telecommunication Computing Electronics and Control)'},
{ 'key': 'pone.0294899.ref019',
'doi-asserted-by': 'crossref',
'first-page': '318',
'DOI': '10.1007/s12194-021-00630-6',
'article-title': 'Effects of sample size and data augmentation on U-Net-based automatic '
'segmentation of various organs',
'volume': '14',
'author': 'T Nemoto',
'year': '2021',
'journal-title': 'Radiological Physics and Technology'},
{ 'issue': '1',
'key': 'pone.0294899.ref020',
'doi-asserted-by': 'crossref',
'first-page': '32',
'DOI': '10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3',
'article-title': 'Index for rating diagnostic tests',
'volume': '3',
'author': 'WJ Youden',
'year': '1950',
'journal-title': 'Cancer'},
{ 'key': 'pone.0294899.ref021',
'doi-asserted-by': 'crossref',
'first-page': '1',
'DOI': '10.1007/s11432-020-2849-3',
'article-title': 'CT radiomics can help screen the coronavirus disease 2019 (COVID-19): a '
'preliminary study',
'volume': '63',
'author': 'M Fang',
'year': '2020',
'journal-title': 'Science China Information Sciences'},
{ 'issue': '6',
'key': 'pone.0294899.ref022',
'doi-asserted-by': 'crossref',
'first-page': '746',
'DOI': '10.3348/kjr.2020.0215',
'article-title': 'Prediction of the development of pulmonary fibrosis using serial '
'thin-section CT and clinical features in patients discharged after '
'treatment for COVID-19 pneumonia',
'volume': '21',
'author': 'M Yu',
'year': '2020',
'journal-title': 'Korean journal of radiology'},
{ 'key': 'pone.0294899.ref023',
'doi-asserted-by': 'crossref',
'first-page': '6770',
'DOI': '10.1007/s00330-020-07013-2',
'article-title': 'Quantitative chest CT analysis in COVID-19 to predict the need for '
'oxygenation support and intubation',
'volume': '30',
'author': 'E Lanza',
'year': '2020',
'journal-title': 'European radiology'},
{ 'issue': '1',
'key': 'pone.0294899.ref024',
'doi-asserted-by': 'crossref',
'first-page': '858',
'DOI': '10.1038/s41598-020-79470-0',
'article-title': 'Artificial intelligence matches subjective severity assessment of '
'pneumonia for prediction of patient outcome and need for mechanical '
'ventilation: a cohort study',
'volume': '11',
'author': 'S Ebrahimian',
'year': '2021',
'journal-title': 'Scientific Reports'},
{ 'key': 'pone.0294899.ref025',
'doi-asserted-by': 'crossref',
'first-page': '1770',
'DOI': '10.1007/s00330-020-07269-8',
'article-title': 'Initial chest radiographs and artificial intelligence (AI) predict '
'clinical outcomes in COVID-19 patients: analysis of 697 Italian '
'patients',
'volume': '31',
'author': 'J Mushtaq',
'year': '2021',
'journal-title': 'European radiology'},
{ 'issue': '12',
'key': 'pone.0294899.ref026',
'doi-asserted-by': 'crossref',
'first-page': '1665',
'DOI': '10.1016/j.acra.2020.09.004',
'article-title': 'CT quantification and machine-learning models for assessment of disease '
'severity and prognosis of COVID-19 patients',
'volume': '27',
'author': 'W Cai',
'year': '2020',
'journal-title': 'Academic radiology'},
{ 'key': 'pone.0294899.ref027',
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'DOI': '10.1016/j.clinimag.2021.06.036',
'article-title': 'Comparison of deep learning, radiomics and subjective assessment of '
'chest CT findings in SARS-CoV-2 pneumonia',
'volume': '80',
'author': 'C Arru',
'year': '2021',
'journal-title': 'Clinical Imaging'},
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'key': 'pone.0294899.ref028',
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'first-page': '18',
'DOI': '10.1186/s41747-023-00334-z',
'article-title': 'A multicenter evaluation of a deep learning software (LungQuant) for '
'lung parenchyma characterization in COVID-19 pneumonia',
'volume': '7',
'author': 'C Scapicchio',
'year': '2023',
'journal-title': 'European Radiology Experimental'},
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'key': 'pone.0294899.ref029',
'doi-asserted-by': 'crossref',
'first-page': '4314',
'DOI': '10.1007/s00330-021-08485-6',
'article-title': 'Lung vessel volume evaluated with CALIPER software is an independent '
'predictor of mortality in COVID-19 patients: a multicentric '
'retrospective analysis',
'volume': '32',
'author': 'C Romei',
'year': '2022',
'journal-title': 'European radiology'}],
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'ISSN': ['1932-6203'],
'subject': ['Multidisciplinary'],
'container-title-short': 'PLoS ONE',
'published': {'date-parts': [[2023, 12, 8]]}}