
Southampton researchers developed an AI that surpassed radiologists in detecting hard-to-see airway obstructions on chest CT scans.
Key Details
- 1AI tool developed at University of Southampton detects radiolucent airway foreign bodies on chest CT.
- 2In study of 70 CT scans (14 positive for radiolucent FBA), radiologists detected 36% of cases; AI detected 71%.
- 3Radiologists had 100% precision but lower recall, while AI had 77% precision (some false positives).
- 4AI's F1 score (74%) exceeded that of radiologists (53%).
- 5Model tested on over 400 patient scans across three cohorts; aims to support, not replace, radiologists.
- 6Study published in npj Digital Medicine; research collaboration included UK and China.
Why It Matters

Source
EurekAlert
Related News

NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.

Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.

AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.