ECU's New AI Enhances Early Detection Across Multiple Diseases

ECU researchers developed an AI algorithm that improves early detection and disease staging using medical imaging.

Key Details

• Supervised Contrastive Ordinal Learning algorithm was developed at Edith Cowan University.
• It uses imaging modalities such as bone density scans and ultrasounds for detection and staging.
• Achieved 85% accuracy and 79% sensitivity for Abdominal Aortic Calcification (early CVD indicator).
• Attained 87% accuracy/84% sensitivity in diagnosing Diabetic Retinopathy and 91% accuracy for breast cancer staging.
• Can differentiate between healthy and diseased individuals by learning disease-specific traits.
• Results 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.

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