Lymphedema imaging and AI: A review of diagnostic modalities, biomarkers, and clinical integration

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.

Keywords: Artificial intelligence; Clinical integration; Deep learning; Diagnostic biomarkers; Lymphedema; Medical imaging segmentation.