Related Experiment Video
Updated: Jun 11, 2026
Clinical Imaging of Microwave Mammography
Published on: November 14, 2025
Diagnostic accuracy of an artificial intelligence-based breast ultrasound tool in pregnant and lactating patients
Dennis Dwan1, Leslie R Lamb1, Hana L Haver2
1Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Objectives:
To assess the diagnostic performance of an artificial intelligence (AI)-based decision support tool for breast ultrasound in pregnant and lactating patients and compare BI-RADS assessments with radiologist interpretation.
Materials And Methods:
This retrospective study included consecutive pregnant or lactating patients presenting with breast complaints at two academic medical centers from 2018-2021. Eligible patients underwent ultrasound with one or more findings assessed as BI-RADS category 2-5 and had biopsy or at least two years of follow-up. Examinations were analyzed using an AI-based decision support tool and compared with radiologist interpretations using the reference standard. Diagnostic performance was compared using McNemar's test.
Results:
A total of 504 women (mean age, 33 years; range, 19-45) with 639 breast ultrasound findings were included. Five findings (0.8%) were malignant, and 634 (99.2%) were benign. Both radiologists and the AI tool classified four malignancies as BI-RADS 4 or 5 and one as BI-RADS 3, yielding a sensitivity of 80.0% (4/5). Among benign lesions, the AI tool recommended more biopsies than radiologists (37.1% vs 21.0%, p < 0.001). After excluding galactoceles, fluid collections, and skin lesions, biopsy recommendation rates were similar (27.4% vs 23.8%, p = 0.18). The AI tool categorized more benign lesions as BI-RADS 2 (53.1% vs 32.9%, p < 0.001) and fewer as BI-RADS 3 (19.5% vs 43.3%, p < 0.001).
Conclusion:
In pregnant and lactating patients, an AI-based decision support tool demonstrated sensitivity comparable to that of radiologists. After excluding lesions outside the AI tool's intended use, the AI tool assigned more BI-RADS 2 and fewer BI-RADS 3 assessments.
Key Points:
Question Can an AI-based decision support tool for breast ultrasound perform comparably to radiologists in pregnant and lactating patients? Findings The AI tool demonstrated sensitivity comparable to radiologists and categorized fewer lesions as BI-RADS 3; biopsy rates were similar after excluding lesions outside intended use. Clinical relevance AI-based decision support for breast ultrasound may reduce unnecessary follow-up imaging in pregnant and lactating patients after excluding lesions outside the AI tool's intended use, while demonstrating sensitivity comparable to radiologists.
