AI triaging halved breast MRI scan times while preserving diagnostic performance, enabling efficient, adaptive imaging workflows.
Key Details
- 1Simulation study analyzed retrospective data from 863 women (1,423 MRI exams); 51 breast cancers diagnosed within 12 months.
- 2AI-directed triaging assigned about 50% of exams to an abbreviated protocol based on real-time suspicion scoring.
- 3Diagnostic performance: Sensitivity (AI triage 88.2%, conventional 86.3%); specificity (AI triage 80.8%, conventional 81.4%).
- 4Cancer detection rates were nearly identical (31.6 vs 30.9 per 1,000 exams); interval cancer rates slightly improved with AI triaging (4.2 vs 4.9 per 1,000).
- 5No cases were missed by abbreviated MRI that would have been detected by the full protocol.
- 6Study highlights potential for workflow efficiency and personalized MRI acquisition.
Why It Matters
This approach could make high-volume breast MRI screenings more efficient, reducing scan and patient time without sacrificing cancer detection. It marks a step toward adaptive, AI-driven imaging protocols and improved resource utilization in breast imaging.
Source
AuntMinnie
Related News
•Radiology Business
Hybrid AI Approach Cuts Mammography Workload by 38%
A Dutch research team demonstrated that a 'hybrid' AI strategy can reduce radiologist workload in mammography screening by nearly 40% without affecting performance.
•AuntMinnie
Habitat AI Model Improves Risk Stratification of Lung Nodules on LDCT
A 'habitat' AI model outperforms standard 2D approaches in stratifying lung adenocarcinoma risk in subsolid nodules on low-dose CT scans.
•AuntMinnie
AI Model Uses Chest CT to Diagnose and Grade COPD Severity
A machine learning model based on chest CT images accurately diagnoses and grades the severity of COPD.