ArticleMedicine2019
A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist.
Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.JAMA network open · 2021Pooled it
- Supplemental breast cancer-screening ultrasonography in women with dense breasts: a systematic review and meta-analysis.British journal of cancer · 2020Pooled it
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- 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
- Ultrasound-based artificial intelligence for breast lesion classification.Frontiers in oncology · 2026Review
- 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
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- Roles of artificial intelligence and high frame-rate contrast-enhanced ultrasound in the differential diagnosis of Breast Imaging Reporting and Data System 4 breast nodules.Gland surgery · 2025Article
- Comparative Study of AI Modes in Ultrasound Diagnosis of Breast Lesions.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.Translational cancer research · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review.NPJ precision oncology · 2024Review
- Article
- Artificial intelligence in breast ultrasound: application in clinical practice.Ultrasonography (Seoul, Korea) · 2024Article
- Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting.Radiology · 2023Article
- Deep learning model for the diagnosis of breast cancers smaller than 1 cm with ultrasonography: integration of ultrasonography and clinical factors.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the value of the computer-aided diagnosis (CAD) program applied to diagnostic breast ultrasonography (US) based on operator experience.US images of 100 breast masses from 91 women over 2 months (from May to June 2015) were collected and retrospectively analyzed. Three less experienced and 2 experienced breast imaging radiologists analyzed the US features of the breast masses without and with CAD according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. We then compared the diagnostic performance between the experienced and less experienced radiologists and analyzed the interobserver agreement among the radiologists.Of the 100 breast masses, 41 (41%) were malignant and 59 (59%) were benign. Compared with the experienced radiologists, the less experienced radiologists had significantly improved negative predictive value (86.7%-94.7% vs 53.3%-76.2%, respectively) and area under receiver operating characteristics curve (0.823-0.839 vs 0.623-0.759, respectively) with CAD assistance (all P < .05). In contrast, experienced radiologists had significantly improved specificity (52.5% and 54.2% vs 66.1% and 66.1%) and positive predictive value (55.6% and 58.5% vs 64.9% and 64.9%, respectively) with CAD assistance (all P < .05). Interobserver variability of US features and final assessment by categories were significantly improved and moderate agreement was seen in the final assessment after CAD combination regardless of the radiologist's experience.CAD is a useful additional diagnostic tool for breast US in all radiologists, with benefits differing depending on the radiologist's level of experience. In this study, CAD improved the interobserver agreement and showed acceptable agreement in the characterization of breast masses.
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