X-Raydar is a deep learning system trained on over 2.5 million chest X-ray studies from six NHS hospitals across the UK, spanning 13 years of clinical data. Given a chest X-ray, it automatically screens for 37 radiological findings and returns a probability for each — in real-time.
It can be used to prioritise urgent cases, flag abnormalities for radiologist review, or as an automated second-reader. Upload a DICOM image to the online platform and receive a full report in seconds.
X-Raydar is freely available to the research community for non-clinical evaluation via our online platform.
This project was funded by a Wellcome Trust Innovator Award.














37 radiological findings detected by X-Raydar, based on the Fleischner glossary and RadLex terminology.
| Finding | Glossary Term | Examples in Free Text |
|---|---|---|
| Lungs | ||
| Parenchymal opacification | Consolidation, Airspace | Consolidation; airspace opacification; batwing shadowing; pulmonary oedema |
| Interstitial opacification / Fibrosis | Reticular pattern, Honeycombing | Interstitial shadowing; fibrosis; Kerley B lines; septal thickening |
| Ground-glass opacity | Ground-glass opacity | Ground glass opacity; ground glass change |
| Parenchymal mass or nodule | Mass, Nodule, Opacity | Lung mass; pulmonary nodule; lung metastases |
| Cavity | Cavity | Cavitation; cavity; cavitating lesion |
| Bulla | Bulla | Bulla; bullae; lung lucencies |
| Emphysema | Emphysema | Emphysema; emphysematous change |
| Hyperexpanded lungs | Air trapping | Hyperexpanded lungs; hyperinflation; large lung volume |
| Bronchial changes | Bronchiectasis | Bronchiectasis; bronchial wall thickening |
| Collapse / Volume loss | Collapse | Lung collapse; decreased lung volume |
| Atelectasis | Atelectasis | Atelectasis; atelectatic bands; linear atelectasis |
| Apical changes | Apical cap | Apical thickening; apical fibrosis |
| Pulmonary blood flow redistribution | Pulmonary blood flow redistribution | Upper lobe diversion; prominent upper lobe vessels |
| Mediastinum & Hila | ||
| Pneumomediastinum | Pneumomediastinum | Air in mediastinum |
| Mediastinum, displaced | Mediastinal compartments | Mediastinal shift |
| Mediastinum, widened | Mediastinal compartments | Mediastinal widening |
| Hilum / Paratracheal changes | Hilum lymphadenopathy | Hilar enlargement; hilar lymphadenopathy |
| Pleural Space | ||
| Pneumothorax | Pneumothorax | Pneumothorax; tension pneumothorax |
| Pleural effusion | Pleural space | Pleural effusion; pleural fluid; blunting of costophrenic angle |
| Pleura, abnormality | Pleural plaque | Pleural plaques; pleural thickening; pleural scarring |
| Diaphragm | ||
| Diaphragm, abnormality | Diaphragm | Elevated hemidiaphragm; eventration of diaphragm |
| Cardiovascular | ||
| Cardiomegaly | Enlargement Heart | Cardiomegaly; heart enlarged; large heart |
| Cardiac calcification | Calcification Heart | Coronary artery calcification |
| Dextrocardia | — | Dextrocardia; situs inversus |
| Aortic calcification | Calcification Aorta | Calcification of the aorta |
| Aortic tortuosity | Tortuous Aorta | Unfolding of the thoracic aorta |
| Osseous Structures & Chest Wall | ||
| Fracture, rib | Fracture Rib | Rib fracture |
| Fracture, clavicle | Fracture Clavicle | Clavicle fracture |
| Bone, lesion | Bone-forming neoplasm | Bone metastasis |
| Scoliosis | Scoliosis | Scoliosis; kyphoscoliosis; kyphosis |
| Mass, paraspinal | Mass Paraspinal | Paraspinal density |
| Skin & Soft Tissue | ||
| Emphysema, subcutaneous | Emphysema, Subcutaneous | Subcutaneous emphysema; surgical emphysema |
| Mass, soft tissue | Mass, Soft tissue density | Axillary mass; axillary lymphadenopathy |
| Abdomen | ||
| Bowel, dilated | Bowel Dilated | Dilated bowel loops; distended stomach |
| Pneumoperitoneum | Pneumoperitoneum | Pneumoperitoneum |
| Hernia | Hernia | Hiatus hernia |
| Other | ||
| Abnormal, not clinically important | Normal variant | Granuloma; pectus deformity; anatomical variant |
| Medical object | Medical Object | — |
| Normal anatomy | Normal anatomy | — |
Based on the Fleischner Society glossary and RadLex terminology.
If you use X-Raydar in your research, please cite:
YD Cid, M Macpherson, L Gervais-Andre, Y Zhu, G Franco, R Santeramo, et al. Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study. The Lancet Digital Health, 6(1), e44–e57, 2024.