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Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting
Open accessAbstract
Background Most low- and middle-income countries lack access to organized breast cancer screening, and women with lumps may wait months for diagnostic assessment. Purpose To demonstrate that artificial intelligence (AI) software applied to breast US images obtained with low-cost portable equipment and by minimally trained observers could accurately classify palpable breast masses for triage in a low-resource setting. Materials and Methods This prospective multicenter study evaluated participants with at least one palpable mass who were enrolled in a hospital in Jalisco, Mexico, from December 2017 through May 2021. Orthogonal US images were obtained first with portable US with and without calipers of any findings at the site of lump and adjacent tissue. Then women were imaged with standard-of-care (SOC) US with Breast Imaging Reporting and Data System assessments by a radiologist. After exclusions, 758 masses in 300 women were analyzable by AI, with outputs of benign, probably benign, suspicious, and malignant. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. Results The mean patient age ± SD was 50.0 years ± 12.5 (range, 18–92 years) and mean largest lesion diameter was 13 mm ± 8 (range, 2–54 mm). Of 758 masses, 360 (47.5%) were palpable and 56 (7.4%) malignant, including six ductal carcinoma in situ. AI correctly identified 47 or 48 of 49 women (96%–98%) with cancer with either portable US or SOC US images, with AUCs of 0.91 and 0.95, respectively. One circumscribed invasive ductal carcinoma was classified as probably benign with SOC US, ipsilateral to a spiculated invasive ductal carcinoma. Of 251 women with benign masses, 168 (67%) imaged with SOC US were classified as benign or probably benign by AI, as were 96 of 251 masses (38%, P < .001) with portable US. AI performance with images obtained by a radiologist was significantly better than with images obtained by a minimally trained observer. Conclusion AI applied to portable US images of breast masses can accurately identify malignancies. Moderate specificity, which could triage 38%–67% of women with benign masses without tertiary referral, should further improve with AI and observer training with portable US. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Slanetz in this issue.
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The use of AI-assisted US has been shown to reduce the amount of unnecessary hospital admissions due to misdiagnosis.
“the use of AI-assisted US has been shown to reduce the amount of unnecessary hospital admissions due to misdiagnosis and unnecessary breast biopsies [197,199,200].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [197]
The Discussion says AI-assisted portable breast US could potentially reduce unnecessary referrals for benign lesions in resource-limited regions. It does not report a measured reduction in hospital admissions or establish that such admissions were due to misdiagnosis. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTwo studies used AI algorithms in combination with handheld US devices.
“Two studies used AI algorithms in combination with handheld US devices [197,198].”
Berg et al. pointed out the importance of training for sonographers to obtain a reasonable image quality for AI analysis.
“Berg et al. pointed out the importance of training for sonographers to obtain a reasonable image quality for AI analysis [197]”
The use of AI-assisted US has been shown to reduce unnecessary breast biopsies.
“the use of AI-assisted US has been shown to reduce the amount of unnecessary hospital admissions due to misdiagnosis and unnecessary breast biopsies [197,199,200].”
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“Berg et al. pointed out the importance of training for sonographers to obtain a reasonable image quality for AI analysis [197], while Huang et al. compared handheld US to robotically performed AI-assisted US and showed reduced costs, shorter examination times, and a higher detection rate in the…”
BackgroundMethod
Metadata
- Authors
- Berg, W. A., López Aldrete, A.-L., Jairaj, A., Ledesma Parea, J. C., García, C. Y., McClennan, R. C., Cen, S. Y., Larsen, L. H., de Lara, M. T. S., & Love, S.
- Affiliations
- University of Southern California (US)
- ORCID
- Venue
- Radiology
- Published
- 2023-05-01
- Volume
- 307
- Issue
- 4
- Pages
- e223351-e223351
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC10323289
- OpenAlex
- W4367668211
- Cited by
- 36
- References
- 24
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 5 · 2023: 6
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (UH3CA189966)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, breast ultrasound, palpable breast mass, resource-limited settings, portable ultrasound, BI-RADS classification, breast cancer screening, ductal carcinoma in situ, Jalisco, low- and middle-income countries, diagnostic accuracy, breast cancer mortality reduction
- MeSH terms
- Artificial Intelligence, Breast Neoplasms, Female, Humans, Prospective Studies, Triage, Ultrasonography, Mammary, Carcinoma, Ductal
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