Multimodal radiomics integrating deep learning and clinical features for diagnosing multidrug-resistant tuberculosis in HIV/AIDS patients

Multimodal radiomics integrating deep learning and clinical features for diagnosing multidrug-resistant tuberculosis in HIV/AIDS patients

Background: This study aimed to develop and validate a predictive model based on multimodal data, including clinical features, radiomics features, and deep learning features, to distinguish multidrug-resistant tuberculosis (MDR-TB) in HIV/AIDS patients, thereby improving diagnostic accuracy. Methods...

Full description

Saved in:
Bibliographic Details
Main Authors: Chang Song, Aichun Huang, Chunyan Zhao, Lemin Wen, Shulin Song, Yanrong Lin, Chaoyan Xu, Hangbiao Qiang, Qingdong Zhu
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Journal of Global Antimicrobial Resistance
Subjects:
Hiv/aids
Tuberculosis
Multidrug-resistant
Radiomics
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S221371652500089X
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S221371652500089X

Similar Items