ReviewMedical science monitor : international medical journal of experimental and clinical research2021
A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses.
Review in Medical science monitor : international medical journal of experimental and clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis.Frontiers in endocrinology · 2023Pooled it
- Lipid metabolism, neuroinflammation, and oxidative stress in Alzheimer disease: an integrated mechanistic review.Acta neurologica Belgica · 2026Review
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- 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
- Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.European radiology · 2024Article
- Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review.European radiology · 2024Article
- Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.Heliyon · 2024Article
- Comparison of artificial intelligence, elastic imaging, and the thyroid imaging reporting and data system in the differential diagnosis of suspicious nodules.Quantitative imaging in medicine and surgery · 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
- AI: Can It Make a Difference to the Predictive Value of Ultrasound Breast Biopsy?Diagnostics (Basel, Switzerland) · 2023Article
- Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features.Frontiers in oncology · 2023Article
- Ultrasonography-based radiomics and computer-aided diagnosis in thyroid nodule management: performance comparison and clinical strategy optimization.Frontiers in endocrinology · 2023Article
- No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans.PLOS digital health · 2022Article
- The Diagnostic Value of Artificial Intelligence Ultrasound S-Detect Technology for Thyroid Nodules.Computational intelligence and neuroscience · 2022Article
- S-Thyroid Computer-Aided Diagnosis Ultrasound System of Thyroid Nodules: Correlation Between Transverse and Longitudinal Planes.Frontiers in physiology · 2022Article
- Evaluating Diagnostic Strategies for Thyroid Nodule Assessment Using S-Detect in Transverse and Longitudinal Ultrasound Planes.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.