ArticleScientific reports2023
Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Artificial Intelligence and Robotic Surgery in Lymphedema: A Scoping Review of Current Applications, Clinical Translation, and Evidence Gaps.Plastic and reconstructive surgery. Global open · 2026Article
- Lymphedema imaging and AI: A review of diagnostic modalities, biomarkers, and clinical integration.Biomedical journal · 2026Review
- Computer vision applications in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2026Article
- Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.Scientific reports · 2025Article
- Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.Journal of vascular surgery. Venous and lymphatic disorders · 2025Article
- Artificial Intelligence-Based Indocyanine Green Lymphography Pattern Classification for Management of Lymphatic Disease.Plastic and reconstructive surgery. Global open · 2024Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.
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Registered trials
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