ArticleEuropean radiology2022
COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines 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
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Construction and validation of high altitude pulmonary edema prediction models based on deep learning and quantitative analysis of X-ray images.BMC medical imaging · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes.International journal of clinical pharmacy · 2025Article
- Article
- The Use of the Modified Brixia Score for Predicting Mortality and Acute Respiratory Distress Syndrome in Patients with COVID-19 Pneumonia: What Have We Learned?Diagnostics (Basel, Switzerland) · 2025Article
- A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.JAMIA open · 2025Article
- Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.The international journal of cardiovascular imaging · 2025Article
- COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm.Scientific reports · 2024Article
- COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.Sensors (Basel, Switzerland) · 2024Article
- Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.Heliyon · 2024Article
- A Risk Model for 28-Day in-Hospital Mortality in 173 COVID-19 Patients Admission to ICU: A Retrospective Study.Infection and drug resistance · 2024Article
- Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.Harvard data science review · 2024Article
- Characteristics of patients with COVID-19 who have deteriorating chest X-ray findings within 48 h: a retrospective cohort study.Scientific reports · 2023Article
- Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.Journal of cardiovascular development and disease · 2023Article
- A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest X-rays.Scientific reports · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
25 authors.
Funding
Abstract
objectivesWe aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU).
methodsSix hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models.
resultsA higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732.
conclusionsThe addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.
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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.