ArticleiScience2022
The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system.
Article in iScience, 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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Who cites it
5 citing papers in PubMed.
- MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network forQuantitative imaging in medicine and surgery · 2025Article
- Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements.Scientific reports · 2024Article
- Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images.BMC medical informatics and decision making · 2024Article
- Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6
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