ArticleResearch in diagnostic and interventional imaging2022
Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT.
Article in Research in diagnostic and interventional imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification.PeerJ. Computer science · 2024Article
- Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients.Research in diagnostic and interventional imaging · 2022Article
- Prediction model for patient prognosis in idiopathic pulmonary fibrosis using hybrid radiomics analysis.Research in diagnostic and interventional imaging · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
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13 authors.
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Abstract
Objectives: 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. Methods: 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. Results: 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; Conclusions: 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.
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