Clinical trials: Deep Learning Model, open to volunteers
Nasopharyngeal Cancinoma (NPC), Distant Metastasis
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
An AI model was developed to predict the likelihood of distant metastasis in patients with nasopharyngeal cancer based on pathology slides and MRI scans of the primary tumor.
- Trial name
- NCT06831357
- Where
- 2 sites in China
- Volunteers
- 500 people
- Sponsor
- Sun Yat-sen University
Carotid Stenosis
Risk Prediction Model for Cerebrovascular Events in Carotid Artery Stenosis
Atherosclerotic carotid artery stenosis is a major cause of stroke, and early identification of high-risk patients combined with surgical intervention can significantly reduce stroke risk.
- Trial name
- NCT06452173
- Testing
- Plasma, carotid high-resolution magnetic resonance imaging
- Where
- China
- Volunteers
- 300 people
- Sponsor
- Beijing Tiantan Hospital
BIS, BIS-EEG
Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive.
- Conditions
- BIS, BIS-EEG, Artifical Intelligence, Intraoperative
- Trial name
- NCT07536230
- Where
- Belgium
- Volunteers
- 115 people, healthy volunteers welcome
- Sponsor
- Universitair Ziekenhuis Brussel
Congenital Microtia
Deep Learning for Flap Perfusion Detection
The aim of this study is to develop a convolutional neural network-based image recognition model to identify flap perfusion abnormalities after first-stage auricular reconstruction in patients with congenital microtia.
- Trial name
- DL-FPA (NCT07846332)
- Testing
- AI-assisted remote monitoring of flap perfusion abnormalities after first-stage auricular reconstruction
- Where
- China
- Volunteers
- 232 people
- Sponsor
- Jing Xu
Nasopharyngeal Cancinoma (NPC), Lymph Node Metastasis
A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)
(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it.
- Trial name
- NPC (NCT06829147)
- Where
- China
- Volunteers
- 500 people
- Sponsor
- Sun Yat-sen University
Gummy Smile
Deep Learning for Gummy Smile Segmentation
A gummy smile (excessive visibility of the gums when smiling) is not merely an aesthetic issue but also an important parameter in terms of periodontal health.
- Trial name
- NCT06965387
- Where
- Turkey (Türkiye)
- Volunteers
- 1,740 people, healthy volunteers welcome
- Sponsor
- Gazi University
Endodontic Retreatment, Non-surgical Retreatment
Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal…
- Conditions
- Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence)
- Trial name
- Ai Retreatment (NCT07611279)
- Testing
- Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
- Volunteers
- 123 people
- Sponsor
- Cairo University
Moyamoya Disease, Intracranial Hemorrhages
5T MRI and Metabolomics-Based Hemorrhage Risk Prediction in Moyamoya Disease
Intracranial hemorrhage is a leading cause of death and disability in patients with moyamoya disease (MMD), yet clinically available tools for predicting long-term hemorrhage risk are lacking.
- Conditions
- Moyamoya Disease, Intracranial Hemorrhages, Stroke
- Trial name
- NCT07781579
- Where
- China
- Volunteers
- 500 people, healthy volunteers welcome
- Sponsor
- Beijing Hospital
Kawasaki Disease, Chest X-ray for Clinical Evaluation
Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease
The goal of this observational study is to develop an AI-based early warning system for Kawasaki Disease (KD) using chest X-rays (CXR) in children diagnosed with Kawasaki Disease. The main question[s] it aims to answer are: 1.
- Conditions
- Kawasaki Disease, Chest X-ray for Clinical Evaluation, Mucocutaneous Lymph Node Syndrome
- Trial name
- NCT07405658
- Testing
- AI-Based Early Warning System for Kawasaki Disease
- Where
- China
- Volunteers
- 20,000 people, healthy volunteers welcome
- Sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Skin Lesions
Human-AI Uncertainty Callibration for Improved Skin Lesion Segmentation
The goal of this randomized controlled study is to compare the effect of a new, personalized uncertainty-aware decision model (FDM) to a standard image recognition model in improving the diagnostic accuracy while reducing…
- Trial name
- NCT07468357
- Testing
- Base Model, FDM
- Volunteers
- 50 people, healthy volunteers welcome
- Sponsor
- Copenhagen Academy for Medical Education and Simulation
Bladder Cancer
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy. Postoperative survival stratification based on radiomics and deep learning may be useful for treatment decisions to improve prognosis.
- Trial name
- NCT06092450
- Testing
- develop and validate a deep learning radiomics model based on preoperative enhanced CT image
- Where
- China
- Volunteers
- 500 people
- Sponsor
- First Affiliated Hospital of Chongqing Medical University
Pediatric Obstructive Sleep Apnea, Polysomnography
Development of a Multimodal Deep Learning Model for Pediatric Patients
This study aims to develop a multimodal data-driven model integrating multiple noninvasive physiological signals to assess the severity of pediatric sleep-disordered breathing, using standard clinical sleep study results as the…
- Trial name
- NCT07805486
- Testing
- fingertip pulse oximeter, pressure-sensing mattresses, millimeter-wave radar
- Where
- Taiwan
- Volunteers
- 50 people
- Sponsor
- Fu Jen Catholic University
Hemorrhage Stroke
Hemorrhage Stroke Decision Making Model Based Deep Learning (BrainHemoAI System)
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke…
- Trial name
- NCT07570680
- Testing
- Large language model
- Where
- China
- Volunteers
- 7,100 people, healthy volunteers welcome
- Sponsor
- Second Affiliated Hospital of Nanchang University
Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model
Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)
This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's…
- Conditions
- Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model, Recurrent Renal Cell Cancer
- Trial name
- NCT06088134
- Where
- China
- Volunteers
- 800 people
- Sponsor
- Mingzhao Xiao
Breast Cancer
AI Model for Classifying Breast Cancer From Histopathology Images
Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes.
- Trial name
- NCT06717984
- Testing
- Biopsy, Mastectomy, Histopathology
- Where
- Bangladesh
- Volunteers
- 500 people, healthy volunteers welcome
- Sponsor
- Taufiq Hasan, PhD
Bone Marrow Transplantation, AML
Model-Informed Precision Dosing on Cyclosporine Therapy in Hematopoietic Stem Cell Transplant Recipients
The purpose of this study is to develop a new tool that helps doctors choose the right cyclosporine dose for patients undergoing bone marrow transplantation.
- Trial name
- NCT07695571
- Volunteers
- 300 people
- Sponsor
- Yasmin medhat munir Mohamed
Cancer Diagnosis, Liver Cancer, Adult
Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening.
- Conditions
- Cancer Diagnosis, Liver Cancer, Adult, Cancer Screening, Colorectal Cancer (CRC)
- Trial name
- NCT06822413
- Testing
- No Interventions
- Where
- 4 sites in China
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine