AIMC Topic: Lymphedema
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Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.
Scientific reports
Apr 11, 2025
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), a...
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
Dec 16, 2024
OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method o...
Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree models.
European journal of oncology nursing : the official journal of European Oncology Nursing Society
Jun 26, 2024
PURPOSE: This study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymphedema after cervical cancer surgery.
100 anastomoses: a two-year single-center experience with robotic-assisted micro- and supermicrosurgery for lymphatic reconstruction.
Journal of robotic surgery
Apr 6, 2024
Robotic-assisted microsurgery has gained significant attention in recent years following the introduction of two dedicated microsurgical robotic systems specifically designed for this purpose. These feature higher degrees of movement and motion scali...
Robot-assisted rehabilitation of people with breast cancer developing upper limb lymphedema: protocol of a randomized controlled trial with a 6-month follow‑up.
Trials
Nov 15, 2023
Upper limb lymphedema (ULLy) is an external (and/or internal) manifestation of lymphatic system insufficiency and deranged lymph transport for more than 3 months and frequently affects people as a consequence of breast cancer (BC). ULLy is often unde...
Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.
Scientific reports
Sep 27, 2023
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 ...
Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm.
IEEE transactions on bio-medical engineering
Aug 30, 2023
OBJECTIVE: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in exces...
The Utilization of e-Health in Lymphedema Care: A Narrative Review.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Aug 2, 2023
Electronic health (e-Health), refers to technologies that can be utilized to enhance patient care as well as collect and share health information. e-Health comprises several umbrella terms, including telehealth, mobile health, e-Health, wearables, a...
Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images.
Scientific reports
Sep 13, 2022
In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can...
Deep learning for standardized, MRI-based quantification of subcutaneous and subfascial tissue volume for patients with lipedema and lymphedema.
European radiology
Aug 17, 2022
OBJECTIVES: To contribute to a more in-depth assessment of shape, volume, and asymmetry of the lower extremities in patients with lipedema or lymphedema utilizing volume information from MR imaging.
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