Clinical trials: Deep Learning, open to volunteers
Contouring, Segmentation
Locally Optimised Contouring With AI Technology for Radiotherapy
LOCATOR is a multicentre phase II randomised clinical trial that is looking at the process of contouring in radiation treatment for breast cancer patients.
- Conditions
- Contouring, Segmentation, Radiation Therapy, Artificial Intelligence
- Trial name
- LOCATOR (NCT06546592)
- Testing
- AI assisted contouring
- Where
- 3 sites in Australia
- Volunteers
- 444 people, healthy volunteers welcome
- Sponsor
- Royal North Shore Hospital
Education, Joint Diseases
The Application of Artificial Intelligence in Wrist and Hand Joint Ultrasound
1. To develop an AI system that can automatically identify standard sections and save images during wrist and hand joint ultrasound scans, while labeling key anatomical structures. 2.
- Trial name
- NCT06883669
- Where
- China
- Volunteers
- 500 people, healthy volunteers welcome
- Sponsor
- West China Hospital
Breast Neoplasms, Artificial Intelligence (AI)
Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement
This study is conducted under the ethics-approved project titled "Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast…
- Conditions
- Breast Neoplasms, Artificial Intelligence (AI), Magnetic Resonance Imaging (MRI), Diffusion Magnetic Resonance Imaging
- Trial name
- NCT07598084
- Testing
- Non-contrast breast MRI diagnostic model
- Volunteers
- 12,000 people, healthy volunteers welcome
- Sponsor
- Peking University People's Hospital
Coronary Artery Disease, Coronary Atheroscleroses
Screening Coronary Artery Disease Using artiFicial intelligencE in Non-contrast Computed Tomography
This project aims to improve direct patient care by reducing the risks of futile exposure to ionizing radiation and iodinated contrast in patients referred for coronary computed tomography angiography
- Trial name
- SAFE-CT (NCT06438393)
- Testing
- CT coronary angiography and non-contrast CT
- Where
- Portugal
- Volunteers
- 1,000 people, healthy volunteers welcome
- Sponsor
- Universidade do Porto
Liver Fibrosis
Prediction of Significant Liver Fibrosis
The deep learning method based on convolutional neural network (CNN) was used to extract the relevant features of liver fibrosis classification from the multi-modal information of digital pathological sections, clinical…
- Trial name
- PSLF (NCT06509230)
- Where
- China
- Volunteers
- 700 people, healthy volunteers welcome
- Sponsor
- Huang Haijun
Mediastinum Disease
Clinical Research on a Novel Deep-learning Based System in Mediastinal Endoscopic Ultrasound Scanning
The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in mediastinal ultrasound endoscopic scanning.
- Trial name
- NCT05792280
- Testing
- AI system
- Where
- China
- Volunteers
- 180 people, healthy volunteers welcome
- Sponsor
- The Third Xiangya Hospital of Central South University
Bone Aging, Osteoporosis Diagnosis
Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
This study is a retrospective analysis that uses abdominal CT scans, which were originally taken for other medical reasons, to estimate bone age.
- Trial name
- NCT07162168
- Where
- China
- Volunteers
- 3,000 people, healthy volunteers welcome
- Sponsor
- Peking University People's Hospital
Healthy Subjects or Volunteers
Training and Testing Database for IMU Based Gait Analysis Methods
The goal of this study is to establish a high-quality, synchronised dataset of gait events (GE) by simultaneously collecting inertial measurement unit (IMU) data and validated ground truth detections using a Vicon motion capture…
- Trial name
- IMU-GAIT (NCT07236008)
- Where
- Belgium
- Volunteers
- 150 people, healthy volunteers welcome
- Sponsor
- Vrije Universiteit Brussel
Neurodegenerative Diseases, Aging
Simulation-informed Modelling and Personalized Evaluation (SIMPLE) in Ageing Populations
Multimodal magnetic resonance imaging (MRI), detecting brain structural and functional changes, has emerged as a powerful and promising technique to study individual's brain, as T1-weighted scans can detect morphometric features…
- Conditions
- Neurodegenerative Diseases, Aging, Brain, Cognitive Dysfunction
- Trial name
- NCT06572332
- Where
- Hong Kong
- Volunteers
- 300 people, healthy volunteers welcome
- Sponsor
- Chinese University of Hong Kong
Parkinson Disease, Alzheimer Disease
Application of AI Technology for the Diagnosis and Treatment of Geriatric Diseases
1) Characteristics of handwriting, gait, speech, eye movements, biological samples (blood, urine, stool, saliva, etc.), images, EEG, and other relevant markers in patients with Alzheimer's disease.
- Conditions
- Parkinson Disease, Alzheimer Disease, Nervous System Diseases, Geriatric Diseases
- Trial name
- NCT06295263
- Testing
- Handwriting, gait, speech, eye movements, biological samples (blood, urine, stool, saliva, etc.), images, EEG, and other relevant markers
- Where
- China
- Volunteers
- 460 people, healthy volunteers welcome
- Sponsor
- Qilu Hospital of Shandong University
Root Canal
AI-Based Root Canal Length, Curvature, and Morphology Assessment Using CBCT
The primary aim of this study is to develop, evaluate, and validate a deep learning-based software system capable of generating automated, comprehensive clinical reports that detect, segment, and quantify root canal curvature…
- Trial name
- NCT07843992
- Testing
- AI-Based Deep Learning System for Root Canal Assessment
- Volunteers
- 155 people, healthy volunteers welcome
- 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
Lymph Nodes With Tumor Metastasis
Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
In this study, the investigators aimed to construct a deep learning diagnostic model that uses cytological images to predict primary unknown tumor origins in patients with tumors combined with lymph node metastases.
- Trial name
- NCT06810349
- Where
- China
- Volunteers
- 10,000 people, healthy volunteers welcome
- Sponsor
- West China Hospital
Pulmnary Hypertension
Detecting Pulmonary Hypertension With the Eko CORE 500 Digital Stethoscope
This prospective, observational study will evaluate whether synchronized heart sound (phonocardiogram, PCG) and three-lead electrocardiogram (ECG) recordings (entered as separate interventions in PRS, though collected together in…
- Trial name
- EKO-PH (NCT07136623)
- Testing
- Eko CORE 500 phonocardiogram (PCG) recording, Eko CORE 500 three-lead electrocardiogram (ECG) recording
- Where
- 2 sites in United States
- Volunteers
- 1,513 people, healthy volunteers welcome
- Sponsor
- Eko Devices, Inc.
Anterior Segment Diseases
Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools.
- Trial name
- NCT07447973
- Testing
- Multimodal Vision-language Model Diagnosis
- Where
- China
- Volunteers
- 2,000 people, healthy volunteers welcome
- Sponsor
- Guangdong Provincial People's Hospital
Tumor
Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT
This study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and…
- Trial name
- NCT07639567
- Where
- China
- Volunteers
- 100,000 people, healthy volunteers welcome
- Sponsor
- Lian Yang
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
Cardiac
Spectral Precise Image Study for Coronary Artery
This is a single-center, comparative, observational, retrospective data study. Datasets from patients who underwent spectral coronary CT angiography (CCTA) for suspected coronary artery disease (CAD) or other diagnosis needs as…
- Trial name
- NCT07620457
- Testing
- Non-Interventional Study
- Where
- China
- Volunteers
- 200 people, healthy volunteers welcome
- Sponsor
- Philips Clinical & Medical Affairs Global
Irritable Bowel Syndrome (IBS)
Deep Learning-Based Confocal Laser Microendoscopy Feature Atlas Construction and Its Application in Intelligent Diagnosis of Irritable Bowel Syndrome
In this study, the healthy control group and IBS patients are taken as the research subjects, and CLE is applied to image and analyze the enrolled persons respectively, to derive and compare the characteristic microstructural…
- Trial name
- NCT07051226
- Volunteers
- 90 people, healthy volunteers welcome
- Sponsor
- Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
Traumatic Brain Injury (TBI) Patients, Severe Traumatic Brain Injury
SeeMe: Using Automated Facial Tracking to Detect Voluntary Behavior in Brain Injury
Objective: This prospective interventional study introduces "SeeMe," an automated, high-resolution computer vision platform designed to objectively quantify microscopic, auditory command-evoked movements in patients with…
- Trial name
- NCT07560631
- Testing
- SeeMe Multimodal Auditory Command Protocol
- Where
- United States
- Volunteers
- 80 people, healthy volunteers welcome
- Sponsor
- Stony Brook University