A commercial AI system can identify up to 33% of interval breast cancers missed by radiologists on digital breast tomosynthesis exams.
Key Details
- 1Study published in Radiology tested AI on digital breast tomosynthesis (DBT) exams preceding confirmed interval cancer diagnoses.
- 2The AI algorithm (Lunit INSIGHT DBT v1.1) flagged up to one-third of interval cancers missed by radiologists.
- 3Interval breast cancers are often more aggressive and have worse prognoses than screen-detected cancers.
- 4Nearly 12 years of retrospective DBT data (Feb 2011–Jun 2023) were analyzed.
- 5Algorithm scored lesions; those over 10 marked as positive, and radiologist review correlated AI findings with actual cancer sites.
Why It Matters
Interval cancers are frequently aggressive and linked with higher morbidity and mortality due to later detection. AI's ability to identify a significant portion of these previously missed cancers suggests potential for earlier intervention and improved patient outcomes in breast imaging.
Source
Health Imaging
Related News
•AuntMinnie
AI Enables Safe 75% Gadolinium Reduction in Breast MRI Without Losing Sensitivity
AI-enhanced breast MRI with a 75% reduced gadolinium dose maintained diagnostic sensitivity comparable to full-dose protocols.
•Cardiovascular Business
Deep Learning AI Model Detects Coronary Microvascular Dysfunction Via ECG
A new AI algorithm rapidly detects coronary microvascular dysfunction using ECGs, with validation incorporating PET imaging.
•AuntMinnie
Study: Patients Prefer AI in Radiology as Assistive, Not Standalone Tool
Survey finds patients support AI-assisted radiology but not AI-only interpretations.