ArticleNPJ digital medicine2021
AI-based analysis of CT images for rapid triage of COVID-19 patients.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.The British journal of radiology · 2022Trial
- Early prediction of severe Omicron pneumonia using a multimodal a.i. model integrating delta CT radiomics and laboratory indicators.Scientific reports · 2026Article
- Lobar-level radiomic clustering reveals background lung changes associated with lung cancer risk: a new perspective for early screening.Insights into imaging · 2026Article
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Development and validation of a pneumonia severity prediction model using AI analysis of abdominal and paravertebral intramuscular fat on chest CT in COVID-19 patients.Journal of thoracic disease · 2025Article
- Common genetic variation influencing the human lung imaging phenotypes.Nature communications · 2025Article
- Radiomics-enhanced modelling approach for predicting the need for ECMO in ARDS patients: a retrospective cohort study.Scientific reports · 2025Article
- Association between age and lung cancer risk: evidence from lung lobar radiomics.BMC medical imaging · 2025Article
- Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.PLoS computational biology · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness.European journal of radiology open · 2024Article
- Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.Reproductive biology and endocrinology : RB&E · 2024Article
- Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study.NPJ digital medicine · 2024Article
- Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19.Research square · 2023Article
- Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.Journal of sport and health science · 2023Article
- A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.Nature biomedical engineering · 2023Article
- Data science in the intensive care unit.World journal of critical care medicine · 2022Article
- Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases.NPJ digital medicine · 2022Article
- COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.European radiology · 2022Article
- Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU.Journal of infection and public health · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.