Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis
The joint model integrating T2WI radiomics and clinical data effectively predicted EEF values for HIFU ablation in adenomyosis. This approach provides a foundation for optimizing HIFU dosing strategies and enhancing treatment safety and efficacy.
Authors
- Xiaohua Huang (ORCID: https://orcid.org/0000-0002-3490-4142)
- Ziyi Liu (ORCID: https://orcid.org/0000-0002-6796-650X)
- Yuan Wang (ORCID: https://orcid.org/0000-0002-5215-0487)
- Xiaofeng Wan (ORCID: https://orcid.org/0000-0001-5301-8480)
- Ziyan Liu (ORCID: https://orcid.org/0000-0003-3992-9194)
Institutions
- Affiliated Hospital of North Sichuan Medical College (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-08-15
- DOI
- https://doi.org/10.3389/fphys.2025.1602866
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00