Artificial intelligence-enhanced ultrasound imaging for thyroid nodule detection and malignancy classification: a study on YOLOv11
YOLOv11 proved to be a powerful tool for thyroid nodule detection and malignancy classification, offering high P and real-time performance. These attributes are vital for dynamic ultrasound examinations and enhancing diagnostic efficiency. Future research will focus on expanding datasets and validating the model's clinical utility in real-time settings.
Authors
- Jiaqi Yang (ORCID: https://orcid.org/0000-0003-4083-4993)
- Jing Zhang (ORCID: https://orcid.org/0000-0002-0905-967X)
- Yanting Wen
- Zhigang Luo
Institutions
- Chongqing University of Posts and Telecommunications (CN)
- Sichuan University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-08-21
- DOI
- https://doi.org/10.21037/qims-2025-257
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36