ArticleKorean journal of radiology2019
Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.
Article in Korean journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
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Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.JAMA network open · 2021Pooled it
- Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.Journal of medical Internet research · 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
- Artificial intelligence-assisted breast ultrasound: modest AUROC improvement and shorter interpretation time without significant change in diagnostic accuracy.Frontiers in radiology · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 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
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- AI-Based Ultrasound Nomogram for Differentiating Invasive from Non-Invasive Breast Cancer Masses.Cancers · 2025Article
- Value of deep learning model for predicting Breast Imaging Reporting and Data System 3 and 4A lesions on mammography.Quantitative imaging in medicine and surgery · 2025Article
- Comparative Study of AI Modes in Ultrasound Diagnosis of Breast Lesions.Diagnostics (Basel, Switzerland) · 2025Article
- Application of an artificial intelligence-assisted diagnostic system for breast ultrasound: a prospective study.Gland surgery · 2024Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.Journal of imaging informatics in medicine · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.Heliyon · 2024Article
- Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review.NPJ precision oncology · 2024Review
- Article
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
objectiveTo investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). MATERIALS AND
methodsB-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared.
resultsWhen the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%;
conclusionDeep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.
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