Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study
Objectives Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, dia...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2021-03-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/11/3/e045120.full |
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| author | Thamer Alaifan Robert Arntfield Blake VanBerlo Nathan Phelps Matthew White Rushil Chaudhary Jordan Ho Derek Wu |
| author_facet | Thamer Alaifan Robert Arntfield Blake VanBerlo Nathan Phelps Matthew White Rushil Chaudhary Jordan Ho Derek Wu |
| author_sort | Thamer Alaifan |
| collection | DOAJ |
| description | Objectives Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.Design A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.Setting Two tertiary Canadian hospitals.Participants 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).Results The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.Conclusions A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited. |
| format | Article |
| id | doaj-art-df12b05c6f204c1a802b9ad48f4f6f00 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-df12b05c6f204c1a802b9ad48f4f6f002024-11-20T18:50:09ZengBMJ Publishing GroupBMJ Open2044-60552021-03-0111310.1136/bmjopen-2020-045120Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning studyThamer Alaifan0Robert Arntfield1Blake VanBerlo2Nathan Phelps3Matthew White4Rushil Chaudhary5Jordan Ho6Derek Wu7Division of Critical Care Medicine, Western University, London, Ontario, CanadaDivision of Critical Care, Western University, London, Ontario, CanadaSchulich School of Medicine and Dentistry, Western University, London, Ontario, CanadaDepartment of Computer Science, Western University, London, Ontario, CanadaDivision of Critical Care Medicine, Western University, London, Ontario, CanadaDepartment of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, CanadaSchulich School of Medicine and Dentistry, Western University, London, Ontario, CanadaSchulich School of Medicine and Dentistry, Western University, London, Ontario, CanadaObjectives Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.Design A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.Setting Two tertiary Canadian hospitals.Participants 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).Results The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.Conclusions A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.https://bmjopen.bmj.com/content/11/3/e045120.full |
| spellingShingle | Thamer Alaifan Robert Arntfield Blake VanBerlo Nathan Phelps Matthew White Rushil Chaudhary Jordan Ho Derek Wu Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study BMJ Open |
| title | Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study |
| title_full | Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study |
| title_fullStr | Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study |
| title_full_unstemmed | Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study |
| title_short | Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study |
| title_sort | development of a convolutional neural network to differentiate among the etiology of similar appearing pathological b lines on lung ultrasound a deep learning study |
| url | https://bmjopen.bmj.com/content/11/3/e045120.full |
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