Deep Learning (DL) pipeline, based on supervised convolutionnal neural networks achieve Dice coefficient of overall COVID-19 lesions on low-dose chest CT (ground-glass opacity and consolidation) of 0.75 ± 0.08 on low-dose computed tomography.
The developed pipeline computes clinical parameters: lesion volume (cm3) and extend (%). Lesion extent automatic quantification had a mean absolute error of 2.1% ± 2.4 with good correlation to manual ground-truth reference (r = 0.947: p<0.001).
After stepwise selection and adjustment on clinical characteristics of 1621 patients, DL driven automatic quantification was shown to be a strong prognostic marker of adverse events during COVID-19 infection (prognosis accuracy of the model from 0.82 without DL to 0.90 with DL-driven quantification (p<0.0001)).
1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.
This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy.
The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001).
A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.Le texte complet de cet article est disponible en PDF.
Keywords : COVID-19, Artificial intelligence, Multidetector computed tomography, Deep learning, Diagnostic imaging
Abbreviations : ACE, BMI, Cons, CNN, COVID-19, CT-SS, DL, DSC, GGO, ICU, LDCT, MAE, MVSF, ROC
| Institution from which the work originated: Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre 13385 Marseille Cedex 05, France.