Lymphedema Imaging and AI: A Review of Diagnostic Modalities, Biomarkers, and Clinical Integration.
Authors
Affiliations (5)
Affiliations (5)
- Department of Precision Health and Intelligent Medicine, Graduate School of Advanced Technology, National Taiwan University, Taipei 10051, Taiwan. Electronic address: [email protected].
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
- Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, South Korea.
- Department of Precision Health and Intelligent Medicine, Graduate School of Advanced Technology, National Taiwan University, Taipei 10051, Taiwan; Institute of Medical Device and Imaging, National Taiwan University, College of Medicine, Taipei 10051, Taiwan; Department of Plastic Surgery, National Taiwan University Hospital, Taipei 10022, Taiwan. Electronic address: [email protected].
- Department of Plastic Surgery, National Taiwan University Hospital, Taipei 10022, Taiwan; Center for Craniofacial Medicine and Morphological Sciences, National Taiwan University Hospital, Taipei 10022, Taiwan. Electronic address: [email protected].
Abstract
Lymphedema, a chronic lymphatic disorder characterized by swelling, fibrosis, and adipose tissue accumulation, requires precise, stage-specific imaging for effective diagnosis and management. This review evaluates conventional imaging modalities, including indocyanine green lymphography (ICG-L), lymphoscintigraphy, magnetic resonance imaging (MRI), and computed tomography (CT), alongside emerging artificial intelligence (AI) applications to enhance diagnostic accuracy and treatment planning. We analyze their capabilities in assessing lymphatic function and tissue changes through quantitative biomarkers, comparing their strengths across disease stages. ICG-L excels in detecting early lymphatic dysfunction, while MRI and CT provide detailed visualization of advanced fibrotic and adipose changes. AI-driven tools, such as automated segmentation and biomarker quantification, show promise in improving tissue characterization and supporting surgical planning. However, clinical integration of AI is hindered by data heterogeneity, lack of interpretability, and regulatory challenges. To address these, we propose a strategic framework incorporating federated learning for privacy-preserving model training, explainable AI for clinical transparency, and standardized imaging protocols. Future efforts should prioritize multicenter validation and harmonized guidelines to enhance reproducibility and ensure equitable, scalable adoption of advanced imaging technologies for lymphedema management worldwide.