The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Artificial intelligence breast ultrasound and handheld ultrasound in the BI-RADS categorization of breast lesions: A pilot head to head comparison study in screening program
Open accessAbstract
Background Artificial intelligence breast ultrasound diagnostic system (AIBUS) has been introduced as an alternative approach for handheld ultrasound (HHUS), while their results in BI-RADS categorization has not been compared. Methods This pilot study was based on a screening program conducted from May 2020 to October 2020 in southeast China. All the participants who received both HHUS and AIBUS were included in the study (N = 344). The ultrasound videos after AIBUS scanning were independently watched by a senior radiologist and a junior radiologist. Agreement rate and weighted Kappa value were used to compare their results in BI-RADS categorization with HHUS. Results The detection rate of breast nodules by HHUS was 14.83%, while the detection rates were 34.01% for AIBUS videos watched by a senior radiologist and 35.76% when watched by a junior radiologist. After AIBUS scanning, the weighted Kappa value for BI-RADS categorization between videos watched by senior radiologists and HHUS was 0.497 (p < 0.001) with an agreement rate of 78.8%, indicating its potential use in breast cancer screening. However, the Kappa value of AIBUS videos watched by junior radiologist was 0.39, when comparing to HHUS. Conclusion AIBUS breast scan can obtain relatively clear images and detect more breast nodules. The results of AIBUS scanning watched by senior radiologists are moderately consistent with HHUS and might be used in screening practice, especially in primary health care with limited numbers of radiologists.
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“Two studies used AI algorithms in combination with handheld US devices [197,198].”
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 [198]
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“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 latter [198].”
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 [198]
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“AI-assisted US also has the potential to allow for cost-effective, population-based screening methods, e.g., for breast US [193,198,199].”
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Ultrasound as the Primary Screening Test for Breast Cancer: Analysis From ACRIN 6666
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Self-citationPerformance of ultrasonography screening for breast cancer: a systematic review and meta-analysis
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Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44
Merkel, D., Züllich, T. F., Schneider, C., Yousefzada, M., Beer, D., Ludwig, M., Weimer, A., Künzel, J., Kloeckner, R., & Weimer, J. (2023) · Diagnostics · doi:10.3390/diagnostics13243622 · Cited by 16
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Rink, M., Künzel, J., Stroszczynski, C., Jung, F., & Jung, E. M. (2024) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2331-0951 · Cited by 5
Pan, X., Wang, S., Jia, B., Yue, L., & Chen, S. (2024) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s457345 · Cited by 2
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Metadata
- Authors
- Huang, X., Qiu, Y., Bao, F., Wang, J., Lin, C., Lin, Y., Wu, J., & Yang, H.
- Affiliations
- Fujian Medical University (CN)
- Corresponding author
- Huang, Xiaoxi
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2023-01-18
- Volume
- 10
- Pages
- 1098639-1098639
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC9889643
- OpenAlex
- W4317395264
- Cited by
- 6
- References
- 11
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 3
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Keywords
- handheld ultrasound, breast cancer screening, breast nodules, breast lesions, inter-radiologist agreement, primary care
- MeSH terms
- Artificial Intelligence, Breast, Breast Neoplasms, Female, Humans, Pilot Projects, Ultrasonography, Mammary
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