Chest x-ray-based biologic aging and aging velocity estimated by deep learning are linked to all-cause and disease-specific mortality.
Key Details
- 1Study analyzed 421,894 healthy Korean adults who underwent chest x-rays between 2006–2020.
- 2Deep-learning model 'AgeNet' estimated radiographic age and aging velocity from x-ray images.
- 3Baseline accelerated aging (radiographic age > chronological by 5+ years) raised mortality risk (HR 1.26 for men, 1.52 for women).
- 4Aging velocity was tracked among 179,667 with ≥3 scans; accelerated aging velocity (≥1.5 years/year) increased mortality risk (rate ratios: 1.51 for men, 1.71 for women).
- 5Decelerated aging velocity (<0.5 years/year) reduced mortality risk, especially in women (rate ratio: 0.50).
- 6Associations held for all-cause, cardiovascular, cancer, and respiratory deaths over median 8.5-year follow-up.
Why It Matters
These findings validate chest x-ray-based AI as a practical tool for estimating biological aging and predicting mortality, supporting its potential for guiding interventions aimed at improving population health and longevity. This highlights the expanding role of imaging beyond diagnosis to ongoing health monitoring.
Source
AuntMinnie
Related News
•Radiology Business
AI Guidance Cuts Novice Ultrasound Exam Time by 34%
AI guidance significantly reduces exam times and enhances diagnostic quality for novice ultrasound operators performing shoulder exams.
•Radiology Business
NYC Health + Hospitals CEO Considers AI to Replace Radiologists
NYC Health + Hospitals CEO suggests AI could partially replace radiologists, pending regulatory approval.
•Radiology Business
UCLA Appoints Inaugural Associate Dean for Health AI Strategy
UCLA has appointed Katherine P. Andriole as its first associate dean for Health AI Strategy and Innovation, with an initial focus on radiology.