Trial reportThe British journal of radiology2022
Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.
Trial report in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.Frontiers in public health · 2024Pooled it
- Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods.Scientific reports · 2025Article
- Data set terminology of deep learning in medicine: a historical review and recommendation.Japanese journal of radiology · 2024Review
- Article
- Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.Scientific reports · 2024Article
- Fairness of artificial intelligence in healthcare: review and recommendations.Japanese journal of radiology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs.
methodsThis retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values.
resultsThe mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (
conclusionsThese results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together. ADVANCES IN KNOWLEDGE: This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.
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