ECU researchers developed an AI algorithm that improves early detection and disease staging using medical imaging.
Key Details
- 1Supervised Contrastive Ordinal Learning algorithm was developed at Edith Cowan University.
- 2It uses imaging modalities such as bone density scans and ultrasounds for detection and staging.
- 3Achieved 85% accuracy and 79% sensitivity for Abdominal Aortic Calcification (early CVD indicator).
- 4Attained 87% accuracy/84% sensitivity in diagnosing Diabetic Retinopathy and 91% accuracy for breast cancer staging.
- 5Can differentiate between healthy and diseased individuals by learning disease-specific traits.
- 6Results will be presented at MICCAI Conference in Korea later this year.
Why It Matters
This AI system demonstrates significant improvements in accuracy for diagnosing and staging major diseases, potentially reducing subjectivity and the workload for clinicians. Early, precise detection using widely available imaging could enhance patient outcomes and expand access to advanced diagnostics.
Source
EurekAlert
Related News
•EurekAlert
Chinese Researchers Unveil Photonic Chip for Ultra-Fast Image Processing
A new photonic chip achieves image processing at 25 million frames per second with high energy efficiency, promising major advances in real-time imaging and AI applications.
•EurekAlert
AI Model Predicts Growth Spurts from Pediatric Neck X-rays for Orthodontics
Korean researchers developed an AI system (ARNet-v2) that predicts children's growth spurts from neck X-rays to enhance orthodontic treatment planning.
•EurekAlert
Dana-Farber Showcases AI and Clinical Trial Advances at ESMO 2025
Dana-Farber researchers present major cancer clinical trial results, including AI-driven data analysis, at ESMO Congress 2025.