ArticleRadiology. Artificial intelligence2020
Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
75 citing papers in PubMed.
- Article
- Automated evaluation of pulmonary lesion changes on chest radiograph during follow-up using semantic segmentation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Machine learning evaluation of pneumonia severity: subgroup performance in the Medical Imaging and Data Resource Center modified radiographic assessment of lung edema mastermind challenge.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.New microbes and new infections · 2024Article
- Automated Pulmonary Tuberculosis Severity Assessment on Chest X-rays.Journal of imaging informatics in medicine · 2024Article
- Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach.Scientific reports · 2024Article
- Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.Radiology. Artificial intelligence · 2024Article
- Article
- Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.Journal of the National Cancer Institute · 2024Article
- Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.Medical image learning with limited and noisy data : second international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MILLanD (Workshop) : (2nd : 2023 : Vancouver, B... · 2023Article
- Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2023Article
- A Prospective Observational Study on Short and Long-Term Outcomes of COVID-19 Patients with Acute Hypoxic Respiratory Failure Treated with High-Flow Nasal Cannula.Journal of clinical medicine · 2023Article
- The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.Journal of digital imaging · 2023Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
13 authors.
Funding
Abstract
purposeTo develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. MATERIALS AND
methodsA convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ∼160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated.
resultsPXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)).
conclusionA Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.
Identifiers
What OpenQuestion holds
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.