A new deep-learning model accurately detects, segments, and quantifies breast arterial calcifications on mammography, aiding both cancer and cardiovascular risk screening.
Key Details
- 1The DL model uses a modified U-Net architecture tailored for BAC segmentation and quantification.
- 2In testing with mammograms from 369 women, the model achieved Dice scores of 0.90 (training) and 0.89 (validation) for segmentation.
- 3Classification accuracy for BAC detection was high, with F1 scores of 0.97 (validation) and 0.93 (training).
- 4Bland-Altman analysis validated the model's reliability for calcium quantification, with a mean difference of -0.98 mg in the training set.
- 5Manual annotation by seven radiologists enabled categorization into six BAC mass levels.
- 6The approach could transform mammography into a dual-purpose screening tool for breast cancer and cardiovascular disease risk.
Why It Matters
Accurate, automated BAC quantification may enable earlier, simultaneous screening for cardiovascular risk during routine mammography, streamlining workflows and providing broader patient benefit. Adoption of such AI tools could advance integrated care in women's imaging.
Source
AuntMinnie
Related News
•Radiology Business
Framework Assesses Real-World Financial Impact of Radiology AI Adoption
A new analysis presents a financial calculator for objectively assessing the return on investment (ROI) of implementing radiology AI solutions.
•Radiology Business
AI Technique Unveils Previously Hidden MS Gray Matter Lesions on MRI
Researchers developed an AI-enhanced method to detect previously invisible gray matter lesions in multiple sclerosis using MRI.
•Radiology Business
Majority of Patients Want Disclosure When AI Used in Imaging
A new survey finds that nearly all patients want to be informed when AI is utilized in medical imaging interpretation.