Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images
Issued Date
2025-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105003189218
Journal Title
Scientific Reports
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.15 No.1 (2025)
Suggested Citation
Lewsirirat T., Titijaroonroj T., Apichonbancha S., Uthatham A., Suwanruangsri V., Suwan N., Bokerd S., Prapassaro T., Chinchalongporn W., Yodrabum N. Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images. Scientific Reports Vol.15 No.1 (2025). doi:10.1038/s41598-025-97564-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109857
Title
Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images
Corresponding Author(s)
Other Contributor(s)
Abstract
Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to symptom overlap with conditions like chronic venous insufficiency (CVI), deep vein thrombosis (DVT), and systemic diseases, often leading to diagnostic delays that can extend up to ten years. These delays negatively impact patient outcomes and burden healthcare systems. Conventional diagnostic methods rely heavily on clinical expertise, which may fail to distinguish subtle variations between these conditions. This study investigates the application of artificial intelligence (AI), specifically deep learning, to improve diagnostic accuracy for lower limb edema. A dataset of 1622 clinical images was used to train sixteen convolutional neural networks (CNNs) and transformer-based models, including EfficientNetV2, which achieved the highest accuracy of 78.6%. Grad-CAM analyses enhanced model interpretability, highlighting clinically relevant features such as swelling and hyperpigmentation. The AI system consistently outperformed human evaluators, whose diagnostic accuracy plateaued at 62.7%. The findings underscore the transformative potential of AI as a diagnostic tool, particularly in distinguishing conditions with overlapping clinical presentations. By integrating AI with clinical workflows, healthcare systems can reduce diagnostic delays, enhance accuracy, and alleviate the burden on medical professionals. While promising, the study acknowledges limitations, such as dataset diversity and the controlled evaluation environment, which necessitate further validation in real-world settings. This research highlights the potential of AI-driven diagnostics to revolutionize lymphedema care, bridging gaps in conventional methods and supporting healthcare professionals in delivering more precise and timely interventions. Future work should focus on external validation and hybrid systems integrating AI and clinical expertise for comprehensive diagnostic solutions.