Clinical trials: Deep Learning
Gingivitis
Artificial Intelligence for the Diagnosis of Gingivitis
this study aims to use AI to diagnose the stages of gingivitis
- Trial name
- NCT07727421
- Testing
- Artificial intelligence Diagnostic Model
- Where
- Egypt
- Volunteers
- 250 people, healthy volunteers welcome
- Sponsor
- Al-Azhar University
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
Stroke Outcome Prediction Supported by Deep Learning Algorithm
STOP-stroke: STroke Outcome Prediction in the Acute Treatment Setting
The STOP-stroke project aims at improving prediction of outcome early after stroke. In order to achieve this, we need to understand reasons (important variables) for prediction in a real clinical prognostication process.
- Trial name
- STOP-STroke (NCT06534645)
- Where
- Switzerland
- Volunteers
- 250 people
- Sponsor
- University of Zurich
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
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
Childhood Obesity
Latinx Family Wellness
Childhood is an opportune time to intervene in obesity because behaviors that are developed during this time can have long-lasting effects and disrupt trajectories of obesity.
- Trial name
- NCT05735600
- Testing
- AyuDa Virtual: Aprender y Utilizar Decisiones Apreciables (Spanish) - Learning and Utilizing Significant Choices (English), AyuDa Written: Aprender y Utilizar Decisiones Apreciables (Spanish) - Learning and Utilizing Significant Choices (English)
- Where
- 2 sites in United States
- Volunteers
- 57 people, healthy volunteers welcome
- Sponsor
- Ana Maria Linares
Endometrial Cancer
Developing a MRI-based Deep Learning Model to Predict MMR Status
In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to…
- Trial name
- NCT05783986
- Testing
- randomly divided
- Volunteers
- 600 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
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
Ovarian Cancer
Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids
The present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict…
- Trial name
- NCT06317610
- Testing
- growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AI
- Where
- China
- Volunteers
- 100 people
- Sponsor
- Chongqing University Cancer Hospital
Androgenetic Alopecia
Harnessing Artificial Intelligence for Diagnosing Androgenetic Alopecia: A Training and Validation Study
The aim of this study is to develop and validate deep learning models in diagnosis of male and female pattern hair loss, and assessment of its severity based on clinical and trichoscopic image by handheld dermoscopy and…
- Trial name
- NCT07294313
- Where
- Egypt
- Volunteers
- 400 people, healthy volunteers welcome
- Sponsor
- Cairo University
Scoliosis Patients
Artificial Intelligence and Scoliosis
The aim of the study to use artificial intelligence technology in assessment of scoliosis degree of severity and to personalize for treatment plan for each pa tient .
- Trial name
- NCT06589583
- Where
- Egypt
- Volunteers
- 5,000 people, healthy volunteers welcome
- Sponsor
- Delta University for Science and Technology
Artificial Intelligence, Submerging Tooth
A Deep Learning Approach to Submerged Teeth Classification and Detection
Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG).
- Trial name
- NCT04309851
- Testing
- deep learning
- Where
- Turkey (Türkiye)
- Volunteers
- 74 people, healthy volunteers welcome
- Sponsor
- Eskisehir Osmangazi University
Breast Neoplasms, Magnetic Resonance Imaging
Artificial Intelligence Analysis for Magnetic Resonance Imaging in Screening and Diagnosis of Breast Cancer
Use Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Build an abbreviated protocal, and investigate whether an abbreviated protocol was suitable for breast…
- Trial name
- NCT05243121
- Testing
- MRI
- Where
- China
- Volunteers
- 5,000 people
- Sponsor
- Peking University People's Hospital
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
Kidney Transplant Failure and Rejection
Predicting Prognostic Factors in Kidney Transplantation Using A Machine Learning
Kidney transplantation (KT) is the most effective treatment for end-stage renal disease, offering improved quality of life and long-term survival.
- Trial name
- NCT06394596
- Testing
- Prognostic factors affecting graft survival
- Where
- South Korea
- Volunteers
- 4,077 people
- Sponsor
- Sung Shin
Lung Cancer
D-Lung: An Analytics Platform for Lung Cancer Based on Deep Learning Technology
Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer.
- Trial name
- NCT04036903
- Testing
- computed tomography
- Where
- Hong Kong
- Volunteers
- 130 people
- Sponsor
- Chinese University of Hong Kong
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
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
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
Knee Injuries
Deep Learning of Knee Joint MRI Intelligent Detection
Knee joint is the most common part of sports injury. MRI is a powerful tool to diagnose knee joint injury. However, it takes a long time to read the film, needs a lot, and some hidden injuries have a high rate of missed…
- Trial name
- NCT04958408
- Where
- China
- Volunteers
- 50,000 people
- Sponsor
- Peking University Third Hospital