Clinical trials: Deep Learning
Atrial Fibrillation (AF), Ischemic Stroke
Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
This study aims to create and assess a deep learning framework for extracting left atrial appendage features in atrial fibrillation patients and combining them with clinical data to predict ischemic stroke risk.
- Trial name
- NCT06611995
- Testing
- observational study
- Where
- China
- Volunteers
- 1,500 people
- Sponsor
- First Affiliated Hospital of Zhejiang University
Motor Development Assessment, Artificial Intelligence-Based Movement Analysis
AI-Based Video Analysis for Motor Development Assessment in Children
This is a non-interventional, prospective observational study aimed at developing and validating an artificial intelligence-based system for assessing motor development in children using video analysis.
- Trial name
- AMD-AI (NCT07597356)
- Testing
- Observational Assessment Only
- Where
- Turkey (Türkiye)
- Volunteers
- 60 people, healthy volunteers welcome
- Sponsor
- Medipol University
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)
Breast Neoplasms
Multi-center Study of Deep Learning AI in Breast Mass
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
- Trial name
- NCT05443672
- Testing
- Yizhun BUSMS
- Where
- China
- Volunteers
- 1,122 people
- Sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical Sciences
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
Fractures, Musculoskeletal
Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning
This retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays.
- Trial name
- NCT06644391
- Testing
- Carebot AI Bones
- Where
- Czechia
- Volunteers
- 600 people
- Sponsor
- Carebot s.r.o.
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
Post-hepatectomy Liver Failure, Hepatocellular Carcinoma
Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for…
- Conditions
- Post-hepatectomy Liver Failure, Hepatocellular Carcinoma, Artificial Intelligence
- Trial name
- NCT06031818
- Testing
- The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation, The model prediction, The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation
- Where
- China
- Volunteers
- 80 people
- Sponsor
- Maastricht University
Arrhythmia
Deep Learning for Intelligent Identification of Arrhythmias
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.
- Trial name
- ECG-LEARNING (NCT05967546)
- Testing
- Observational
- Where
- China
- Volunteers
- 4,000 people
- Sponsor
- First Affiliated Hospital Xi'an Jiaotong University
Scoliosis Idiopathic Adolescent, Scoliosis
Deep Learning for Early Scoliosis Detection Using mmWave Radar Gait Data
Scoliosis is a sideways curvature of the spine that often develops during childhood and adolescence. When detected early, scoliosis can be managed effectively with non-invasive approaches such as bracing and physiotherapy, while…
- Trial name
- ScoliRadar-AI (NCT07593560)
- Testing
- mmWave Radar Gait Assessment
- Where
- Turkey (Türkiye)
- Volunteers
- 200 people, healthy volunteers welcome
- Sponsor
- Gebze Technical University
Visual Function, Vision
Visual Function Screening System With Special Needs Children and Typical Preschoolers
The early visual screening of children plays a critical role in promoting visual development, especially for those with visual impairments.
- Conditions
- Visual Function, Vision, Special Needs Children
- Trial name
- NCT07081139
- Testing
- observational study, no interventional
- Where
- Taiwan
- Volunteers
- 1,300 people, healthy volunteers welcome
- Sponsor
- National Taiwan University Hospital
Radiomics, Deep Learning
Retrospective Analysis of the Correlation Between Imaging Features and Pathology, Prognosis in Renal Tumors
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor.
- Conditions
- Radiomics, Deep Learning, Artificial Intelligence, Body Composition
- Trial name
- NCT06167863
- Testing
- radiomics
- Where
- China
- Volunteers
- 1,000 people
- Sponsor
- Zhen Li