A deep learning model named Sybil can predict future lung cancer risk from a single low-dose chest CT scan, as validated in a large Asian cohort.
Key Details
- 1The Sybil model was validated using data from over 21,000 individuals aged 50-80 who underwent LDCT screening between 2009 and 2021.
- 2Sybil demonstrated strong predictive performance for lung cancer diagnosis both at one and six years following the scan.
- 3The model was effective even for never-smokers, a group for whom conventional screening guidelines may not apply.
- 4The research was presented at the ATS 2025 International Conference.
- 5Continued prospective validation is planned to assess clinical use and prediction of lung cancer-specific mortality.
Why It Matters
This advancement could help tailor lung cancer screening strategies, especially benefiting populations (such as Asian never-smokers) not well-served by current guidelines. A validated AI risk tool from a single scan could make screening more efficient and personalized in routine radiological practice.
Source
EurekAlert
Related News
•EurekAlert
Major Study Reveals Barriers to Implementing AI Chest Diagnostics in NHS Hospitals
A UCL-led study identifies significant challenges in deploying AI tools for chest diagnostics across NHS hospitals in England.
•EurekAlert
AI Model Enhances Prediction of Infection Risks from Oral Mucositis in Stem Cell Transplant Patients
Researchers developed an explainable AI tool that accurately predicts infection risks related to oral mucositis in hematopoietic stem cell transplant patients.
•EurekAlert
AI-Enabled Hydrogel Patch Provides Long-Term High-Fidelity EEG and Attention Monitoring
Researchers unveil a reusable hydrogel patch with machine learning capabilities for high-fidelity EEG recording and attention assessment.