Clinical trials: Deep Learning Model
Myopia
Myopia Intervention in Children and Adolescents and Establishment of a Precise Intervention Model
Investigators are going to conduct a multi-centered randomized clinical trial that myopic children are randomly allocated into three groups: 0.01% atropine， 0.04% atropine and orthokeratology in order to evaluate the efficacy and…
- Trial name
- NCT05357326
- Testing
- Atropine Sulfate 0.01% Eye Drop, Orthokeratology, Atropine Sulfate 0.04% Eye Drop
- Where
- China
- Volunteers
- 186 people
- Sponsor
- Shanghai Eye Disease Prevention and Treatment Center
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
Asthma in Children
Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Artificial intelligence (AI) offers substantial opportunities for healthcare, supporting better diagnosis, treatment, prevention and personalized care.
- Trial name
- BREATHE (NCT05140889)
- Where
- Italy
- Volunteers
- 25 people
- Sponsor
- Fondazione IRCCS Policlinico San Matteo di Pavia
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
Breast Cancer
Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization
The correct categorization of breast density is essential to adapt the diagnostic examination to the needs of each patient. Assessment of breast density is performed visually by radiologists.
- Trial name
- NCT05021055
- Volunteers
- 277 people, healthy volunteers welcome
- Sponsor
- Hospital Italiano de Buenos Aires
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
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
Lateral Ligament, Ankle
Deep Learning of Anterior Talofibular Ligament: Comparison of Different Models
The purpose of this study is to study the injury of the anterior talofibular ligament by deep learning method and compare a variety of different deep learning models to establish a deep learning method that can accurately…
- Trial name
- NCT04955067
- Testing
- Diagnositic test
- Where
- China
- Volunteers
- 1,000 people, healthy volunteers welcome
- Sponsor
- Peking University Third Hospital
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
Intraoperative Hypotension, General Anesthesia
Deep Learning Models for Prediction of Intraoperative Hypotension Using Non-invasive Parameters
The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.
- Trial name
- NCT05762237
- Where
- South Korea
- Volunteers
- 5,175 people
- Sponsor
- Samsung Medical Center
Glioma
Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
Accurately predicting the survival of pediatric glioma patients is crucial for informed clinical decision-making and selecting appropriate treatment strategies.
- Trial name
- NCT06199388
- Testing
- Survival state
- Where
- China
- Volunteers
- 9,532 people
- Sponsor
- Tang-Du Hospital
Labrum Injury of the Hip Joint
Research on Acetabular Labrum Injury Based on MR: Multi-angle Deep Learning Model
The purpose of this study is to study the MRI images of acetabular labrum injury by deep learning method, and try to establish a combination model of axial and coronal serial images, and compare with the diagnostic accuracy of…
- Trial name
- NCT04950036
- Testing
- Diagnostic Test
- Where
- China
- Volunteers
- 1,261 people, healthy volunteers welcome
- Sponsor
- Peking University Third Hospital
Bone Age
Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment
The purpose of this study is to understand the effects of using an Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system.
- Trial name
- NCT03530098
- Testing
- BoneAgeModel
- Where
- 6 sites in United States
- Volunteers
- 1,903 people
- Sponsor
- Stanford University
Facet Joints; Degeneration ; Deep Learning ;Artificial Intelligence
Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration
Spinal facet joint osteoarthritis is a disease with high incidence among people over 40 years old. It is a disease characterized by a series of degenerative pathological changes and clinical features of synovium, articular…
- Trial name
- NCT05635006
- Where
- China
- Volunteers
- 1,132 people
- Sponsor
- Hai Lv
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
Dental Plaque (Diagnosis), Deep Learning
Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning
Background: Dental plaque contributes to a number of common oral conditions such as caries, gingivitis, and periodontitis. As a result, detection and management of plaque is of great importance for the oral health of individuals.
- Trial name
- NCT06603233
- Testing
- The Difference Between The AI Model and Dentists Group, Deep Learning Models
- Where
- Turkey (Türkiye)
- Volunteers
- 31 people, healthy volunteers welcome
- Sponsor
- Ankara Medipol University
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
Hepatocellular Carcinoma (HCC)
Precision Recurrence Risk Assessment in Early-stage Hepatocellular Carcinoma
This retrospective observational study aims to evaluate whether artificial intelligence (AI) models can predict aggressive recurrence in patients who underwent liver resection for early-stage hepatocellular carcinoma (HCC).
- Trial name
- NCT07030842
- Testing
- liver resection
- Where
- China
- Volunteers
- 579 people
- Sponsor
- Tongji Hospital
Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
This study aims to develop a deep learning model based on noncontrast CT images to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection，which can serve as potential tool to assist…
- Trial name
- DL-Rec-ILA (NCT06659601)
- Where
- China
- Volunteers
- 9 people
- Sponsor
- First Affiliated Hospital of Chongqing Medical University