ArticleMedical image analysis2021
AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.Medical image analysis · 2021Pooled it
- 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
- CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example.Scientific reports · 2025Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- Augmented prediction of vertebral collapse after osteoporotic vertebral compression fractures through parameter-efficient fine-tuning of biomedical foundation models.Scientific reports · 2024Article
- Corticosteroid treatment prediction using chest X-ray and clinical data.Computational and structural biotechnology journal · 2024Article
- Image biomarkers and explainable AI: handcrafted features versus deep learned features.European radiology experimental · 2024Review
- Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study.Heliyon · 2024Article
- Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review.JMIR medical informatics · 2024Review
- Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis.STAR protocols · 2024Article
- Shallow and deep learning classifiers in medical image analysis.European radiology experimental · 2024Review
- MERGE: A model for multi-input biomedical federated learning.Patterns (New York, N.Y.) · 2023Article
- Quantitative evaluation of CT scan images to determinate the prognosis of COVID-19 patient using deep learning.European journal of translational myology · 2023Article
- A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging.Radiology. Artificial intelligence · 2023Review
- POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021).Scientific data · 2023Article
- Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.Scientific reports · 2023Article
- DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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28 authors.
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Abstract
Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.
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