ArticleFrontiers in oncology2022
Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 22 citations in OpenAlex.
- Diagnostic accuracy of an artificial intelligence-based breast ultrasound tool in pregnant and lactating patients.European radiology · 2026Article
- Effect of an annotation-free artificial intelligence system for simultaneous detection and diagnosis on breast ultrasonography: a multireader, multicase study across diverse healthcare professionals.Ultrasonography (Seoul, Korea) · 2026Article
- Feasibility of Using an AI System for Breast Ultrasonography Interpretation According to Clinical Expertise: Results of a Pilot Study.Journal of the Korean Society of Radiology · 2026Article
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Integration of AI lesion classification, age, and BI-RADS assessment to reduce benign biopsies on breast ultrasound.European radiology · 2025Article
- Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Review
- Artificial Intelligence in Diagnostic Breast Ultrasound: A Comparative Analysis of Decision Support Among Radiologists With Various Levels of Expertise.European journal of breast health · 2025Article
- Application of an artificial intelligence-assisted diagnostic system for breast ultrasound: a prospective study.Gland surgery · 2024Article
- Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.Heliyon · 2024Article
- Artificial intelligence in breast ultrasound: application in clinical practice.Ultrasonography (Seoul, Korea) · 2024Article
- Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study.World journal of surgery · 2023Article
- Diagnostic performance of ultrasound with computer-aided diagnostic system in detecting breast cancer.Heliyon · 2023Article
- Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions.Cancers · 2023Review
- The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis.Gland surgery · 2022Article
- An AI model of sonographer's evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses.Frontiers in oncology · 2022Article
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Authors and funding
17 authors at 11 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.
methodsNine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites.
resultsA total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77],
conclusionsCompared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.
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