Open-source Sybil AI detects lung cancer risk effectively in Asian heavy smokers on low-dose CT.
Key Details
- 1Sybil is an open-source deep learning model that uses low-dose CT (LDCT) to predict lung cancer risk.
- 2Study included 18,057 Asian patients (with at least one follow-up scan) from 2004–2021.
- 3Sybil achieved overall AUC of 0.91 (1-year) and 0.74 (6-year); heavy smoking subgroup AUCs were 0.94 (1-year, visible cancers) and 0.7 (6-year, future cancers).
- 4Performance was weaker (AUC 0.56 for 6-year, future cancers) in never/light-smoking subgroup.
- 5The model may help optimize follow-up intervals in lung cancer screening programs.
- 6External validation is needed before widespread adoption.
Why It Matters
This research demonstrates promising performance of an AI tool for individualized lung cancer risk estimation using routine LDCT in underrepresented Asian populations, potentially supporting more tailored screening protocols. Reliable, open-source risk assessment models like Sybil may improve early detection and resource allocation in lung cancer care.
Source
AuntMinnie
Related News
•AuntMinnie
AI Model Shows Promise for Detecting Meningiomas on Skull X-rays
South Korean researchers developed an AI model that detects meningiomas on skull x-rays, showing high accuracy in initial tests.
•AuntMinnie
AI and Collaborative Strategies Advance Lung Cancer Screening Uptake
Collaborative initiatives and novel AI tools are helping to advance lung cancer screening, but participation barriers and disparities persist despite guideline expansions.
•AuntMinnie
AI Advances Push Opportunistic Imaging Into Clinical Focus
AI-powered opportunistic screening is transforming routine radiological images into proactive tools for risk detection of major diseases.