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Articles That Use Artificial Intelligence for Ultrasound: A Reader's Guide
Ming Kuang1,2, Hang-Tong Hu1, Wei Li1
1Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Frontiers in Oncology
|June 28, 2021
Summary
Artificial intelligence (AI) enhances medical imaging analysis for precision medicine. This guide helps evaluate AI-powered ultrasound studies, focusing on their strengths and weaknesses.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Ultrasound Technology
Background:
- Artificial intelligence (AI) is revolutionizing medical image analysis, enabling high-throughput data mining.
- Machine learning algorithms within AI support precision medicine through applications like lesion detection, segmentation, diagnosis, and prognosis prediction.
- The number of AI-related publications, particularly in ultrasound, has surged recently.
Purpose of the Study:
- To provide a critical guide for assessing AI-powered ultrasound studies.
- To highlight key considerations for identifying robust versus underpowered research in this rapidly growing field.
Main Methods:
- Review of current literature on AI in medical imaging, with a focus on ultrasound.
- Identification of unique ultrasound characteristics (real-time, operator-dependence, multi-modality) relevant to AI study design.
- Development of criteria for evaluating the strength and limitations of ultrasound AI studies.
Main Results:
- AI transforms medical images into valuable data for clinical decision support.
- Ultrasound's unique properties necessitate specific evaluation criteria for AI studies.
- A framework is proposed to differentiate strong from weak ultrasound AI research.
Conclusions:
- AI significantly advances precision medicine by enhancing medical image analysis.
- Critical appraisal of AI ultrasound studies is essential due to the modality's specific attributes.
- This review equips readers to discern high-quality research in AI-enhanced ultrasound imaging.
