ArticleEuropean radiology experimental2021
Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.
Article in European radiology experimental, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 5 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers.European journal of radiology · 2021Pooled 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
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review.NPJ primary care respiratory medicine · 2026Article
- The Accuracy of ChatGPT-4o in Interpreting Chest and Abdominal X-Ray Images.Journal of personalized medicine · 2025Article
- The Effect of Simulated Dose Reduction on the Performance of Artificial Intelligence in Chest Radiography.Journal of imaging · 2025Article
- Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department.Diseases (Basel, Switzerland) · 2023Article
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- 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
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- A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.Scientific reports · 2023Article
- Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future.Diagnostics (Basel, Switzerland) · 2022Review
- ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System.Arabian journal for science and engineering · 2022Article
- A comparison of Covid-19 early detection between convolutional neural networks and radiologists.Insights into imaging · 2022Article
- Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer.Medical & biological engineering & computing · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy.
methodsWe used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard.
resultsAt 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2.
conclusionsThis preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
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