Deep Learning Clinical Trial
Official title:
Study on Classification Method of Indocyanine Green Lymphography in Diagnosing Breast Cancer-related Lymphedema Based on Deep Learning
Breast cancer related lymphedema (BCRL) is the most common complication after breast cancer surgery, which brings a heavy psychological and spiritual burden to patients. For a long time, the diagnosis and treatment of lymphedema has been a difficult point in domestic and foreign research. To a large extent, it is because most of the patients who come to see a doctor have already developed obvious lymphedema, and the internal lymphatic vessels have undergone pathological remodeling[1] Therefore, it is particularly important to detect early lymphedema and intervene in time through the use of sensitive screening tools. Indocyanine green (ICG) lymphangiography is a relatively new method, which can display superficial lymph flow in real time and quickly, and will not be affected by radioactivity [7]. In 2007, indocyanine green lymphography was used for the first time to evaluate the function of superficial lymphatic vessels. In 2011, Japanese scholars found skin reflux signs based on ICG lymphography data of 20 patients with lymphedema after breast cancer surgery, and they were roughly divided into three types according to their severity: splash, star cluster, and diffuse (Figure 1) [8]. Later, in 2016, a prospective study involving 196 people affirmed the value of ICG lymphography in the early diagnosis of lymphedema, and made the images of ICG lymphography more specific stages 0-5 [9], but The staging is still based on the three types of skin reflux symptoms found in a small sample clinical study in 2011, which is not completely applicable in actual clinical applications. In addition, when abnormal skin reflux symptoms appear on ICG lymphangiography, the pathophysiological changes that occur in the body lack research and exploration. Therefore, this research hopes to refine the image features of ICG lymphography through machine learning (deep learning), and establish a PKUPH model for diagnosing early lymphedema by staging the image features.
n/a
Status | Clinical Trial | Phase | |
---|---|---|---|
Not yet recruiting |
NCT05550012 -
A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT
|
N/A | |
Completed |
NCT04921488 -
Interest of Artificial Intelligence in Cancer Screening Colonoscopy
|
N/A | |
Completed |
NCT06274502 -
Automated Detection and Diagnosis of Pathological DRGs in PHN Patients Using Deep Learning and Magnetic Resonance
|
||
Recruiting |
NCT05046366 -
Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.
|
||
Completed |
NCT04828187 -
Development and Validation of Deep Neural Networks for Blinking Identification and Classification
|
||
Recruiting |
NCT04592068 -
AI Classifies Multi-Retinal Diseases
|
||
Recruiting |
NCT05058599 -
Reconstruction Technology to Auxiliary Diagnosis and Guarantee Patient Privacy
|
||
Recruiting |
NCT05536024 -
Combing a Deep Learning-Based Radiomics With Liquid Biopsy for Preoperative and Non-invasive Diagnosis of Glioma
|
||
Completed |
NCT05323279 -
Evaluate the Effects of An AI System on Colonoscopy Quality of Novice Endoscopists
|
N/A | |
Completed |
NCT06278272 -
AI Evaluation of Pancreatic Exocrine Insufficiency in CP Patients
|
||
Not yet recruiting |
NCT06462924 -
Feasibility of Gadolinium Contrast Reduced Brain MRI: the Potential of Deep Learning
|
N/A | |
Enrolling by invitation |
NCT06444425 -
Artificial Intelligence in Detecting Cardiac Function
|
||
Recruiting |
NCT06372756 -
Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
|
||
Recruiting |
NCT05426135 -
Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment
|
||
Recruiting |
NCT05444166 -
Explore the Relationship Between the Percentage of Colonoscopy Withdrawal Overspeed and the ADR
|
||
Recruiting |
NCT05617469 -
DLCS for Predicting Neoadjuvant Chemotherapy Response
|
||
Active, not recruiting |
NCT05182099 -
High Resolution HBA-MRI Using Deep Learning Reconstruction
|
N/A | |
Recruiting |
NCT05204186 -
Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence
|
||
Recruiting |
NCT06383546 -
Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
|
||
Active, not recruiting |
NCT05041777 -
Optical-Coherence Tomography for the Non-invasive Diagnosis and Subtyping of Basal Cell Carcinoma
|