Clinical trials: Deep Learning
Deep Learning, Retinal Diseases
AI Classifies Multi-Retinal Diseases
The objective of this study is to establish deep learning (DL) algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.
- Trial name
- NCT04592068
- Testing
- Retinal multi-diseases diagnosed by DL algorithm, Retinal multi-diseases diagnosed by expert panel
- Where
- China
- Volunteers
- 10,000 people
- Sponsor
- Beijing Tongren Hospital
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
Epilepsy, Temporal Lobe
A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging
This study aims to use radiomics analysis and deep learning approaches for seizure focus detection in pediatric patients with temporal lobe epilepsy (TLE).
- Trial name
- NCT04169581
- Where
- China
- Volunteers
- 201 people
- Sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
Cerebral Palsy, Periventricular White Matter Abnormalities
Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI
The goal of this study is to determin the MRI features associated with cerebral palsy and to develop prediction models of pediatric disorders by combining MRI with artificial intelligence.
- Trial name
- NCT06575283
- Testing
- No intervention will be performed in this cohort study
- Where
- China
- Volunteers
- 1,000 people, healthy volunteers welcome
- Sponsor
- First Affiliated Hospital Xi'an Jiaotong University
Malocclusion, Craniofacial Morphology
Research on Deep Learning-Based Intelligent Diagnosis and Treatment
This study aims to develop and evaluate deep learning-based artificial intelligence models for craniomaxillofacial multi-modal imaging analysis and clinical decision support.
- Conditions
- Malocclusion, Craniofacial Morphology, Dentofacial Deformities
- Trial name
- NCT07551622
- Volunteers
- 2,000 people, healthy volunteers welcome
- Sponsor
- Xi'an Jiaotong 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
Neoplasms (Cancer / Tumors), Colorectal Neoplasms
A Preregistered Multi-Cohort Evaluation of the FATHOM AI System for Molecular Testing Prioritization to Support Clinical Trial Enrollment
Many clinical trials evaluating cancer treatments require patients to undergo testing for specific molecular markers as part of eligibility screening, typically using immunohistochemistry or sequencing.
- Conditions
- Neoplasms (Cancer / Tumors), Colorectal Neoplasms, Lung Neoplasms, Endometrial Neoplasms
- Trial name
- FATHOM (NCT07814872)
- Where
- United States
- Volunteers
- 30,000 people
- Sponsor
- Harvard Medical School (HMS and HSDM)
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
Duchenne Muscular Dystrophy, Becker Muscular Dystrophy
Brain Involvement in Dystrophinopathies Part 2
The objective of this study is to understand the relationship between DMD and BMD brain comorbidities, and the location of the gene mutation which causes the disease.
- Trial name
- NCT04668716
- Where
- 7 sites in Denmark, France and 4 more
- Volunteers
- 339 people, healthy volunteers welcome
- Sponsor
- Great Ormond Street Hospital for Children NHS Foundation Trust
Age-Related Macular Degeneration
Deciphering AMD by Deep Phenotyping and Machine Learning- Prospective Study - SUMMIT (Part 2)
We will conduct a prospective, non-interventional roll-over extension of the PINNACLE study (Deciphering AMD by deep phenotyping and machine learning).
- Trial name
- NCT06682455
- Where
- 8 sites in Austria, United Kingdom
- Volunteers
- 300 people
- Sponsor
- University of Southampton
Stump
Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity (such as leiomyomas) or malignancy (such as leiomyosarcomas) are called STUMP (smooth muscle tumor of uncertain malignant potential).
- Trial name
- STUMP (NCT06540846)
- Testing
- No intervention
- Where
- France
- Volunteers
- 392 people
- Sponsor
- Institut Bergonié
Femoroacetabular Impingement Syndrome
AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome (FAIS-AI)
Femoroacetabular impingement syndrome (FAIS) is the leading cause of hip pain in young adults and frequently progresses to osteoarthritis, often exacerbated by delayed diagnosis in primary care.
- Trial name
- FAIS-AI (NCT07737223)
- Where
- China
- Volunteers
- 2,617 people
- Sponsor
- ChunBao Li