tetano
Editor, Senior Moderator
PLoS One
. 2023 May 2;18(5):e0285121.
doi: 10.1371/journal.pone.0285121. eCollection 2023. A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative
Laurens Topff[SUP] 1 2 [/SUP], José Sánchez-García[SUP] 3 [/SUP], Rafael López-González[SUP] 3 [/SUP], Ana Jiménez Pastor[SUP] 3 [/SUP], Jacob J Visser[SUP] 4 [/SUP], Merel Huisman[SUP] 5 [/SUP], Julien Guiot[SUP] 6 [/SUP], Regina G H Beets-Tan[SUP] 1 2 [/SUP], Angel Alberich-Bayarri[SUP] 3 [/SUP], Almudena Fuster-Matanzo[SUP] 3 [/SUP], Erik R Ranschaert[SUP] 7 8 [/SUP]; Imaging COVID-19 AI initiative
Affiliations
Background: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).
Objectives: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.
Methods: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.
Results: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.
Conclusion: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.
. 2023 May 2;18(5):e0285121.
doi: 10.1371/journal.pone.0285121. eCollection 2023. A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative
Laurens Topff[SUP] 1 2 [/SUP], José Sánchez-García[SUP] 3 [/SUP], Rafael López-González[SUP] 3 [/SUP], Ana Jiménez Pastor[SUP] 3 [/SUP], Jacob J Visser[SUP] 4 [/SUP], Merel Huisman[SUP] 5 [/SUP], Julien Guiot[SUP] 6 [/SUP], Regina G H Beets-Tan[SUP] 1 2 [/SUP], Angel Alberich-Bayarri[SUP] 3 [/SUP], Almudena Fuster-Matanzo[SUP] 3 [/SUP], Erik R Ranschaert[SUP] 7 8 [/SUP]; Imaging COVID-19 AI initiative
Affiliations
- PMID: 37130128
- DOI: 10.1371/journal.pone.0285121
Background: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).
Objectives: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.
Methods: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.
Results: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.
Conclusion: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.