Clinical trials: Deep Learning
Bowel Obstruction, Artificial Intelligence
Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
The study will compare the diagnostic accuracy and time to diagnosis of computed tomography images of patients with suspected intestinal obstruction seen in the emergency room by residents and surgeons, with and without…
- Trial name
- NCT06481358
- Testing
- Artificial intelligence
- Where
- Japan
- Volunteers
- 17 people, healthy volunteers welcome
- Sponsor
- Nagoya 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)
Deep Learning, Corneal Disease
Artificial Intelligence for Screening of Multiple Corneal Diseases
This study developed a deep learning algorithm based on anterior segment images and prospectively validated its ability to identify corneal diseases.The effectiveness and accuracy of this algorithm was evaluated by sensitivity…
- Conditions
- Deep Learning, Corneal Disease, Screening
- Trial name
- NCT06211218
- Testing
- Cornea diseases diagnosed by artificial intelligence algorithm
- Where
- China
- Volunteers
- 3,000 people
- Sponsor
- Tianjin Eye Hospital
Retinoblastoma
Deep Learning in Retinoblastoma Detection and Monitoring.
Retinoblastoma is the most common eye cancer of childhood. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment.
- Trial name
- NCT05308043
- Testing
- Deep learning algorism
- Where
- China
- Volunteers
- 200 people
- Sponsor
- Beijing Tongren Hospital
Glioma
MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence
This registry aims to collect clinical, molecular and radiologic data including detailed clinical parameters, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new…
- Trial name
- NCT04217018
- Testing
- Prediction of molecular pathology
- Where
- China
- Volunteers
- 3,000 people, healthy volunteers welcome
- Sponsor
- The First Affiliated Hospital of Zhengzhou University
Glioma
Histopathology Images Based Survival Prediction of Glioma Patients Using Artificial Intelligence
This registry aims to collect clinical, molecular and histopathology imaging including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and…
- Trial name
- NCT04215224
- Testing
- Histopathology images based survival prediction for glioma patients
- Where
- China
- Volunteers
- 3,500 people
- Sponsor
- The First Affiliated Hospital of Zhengzhou 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
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
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
Synovial Diseases, Pigmented Villonodular Synovitis
Clinical Study of Magnetic Resonance Imaging and Deep Learning of Joint Synovial Disease
Through the high-throughput feature extraction of magnetic resonance images, the deep learning prediction model of joint synovial lesions is constructed used for the diagnosis, differential diagnosis and curative effect…
- Conditions
- Synovial Diseases, Pigmented Villonodular Synovitis, Gout, Rheumatoid Arthritis
- Trial name
- NCT04952896
- Testing
- Synovitis diagnosis
- Where
- China
- Volunteers
- 350 people
- Sponsor
- Peking University Third Hospital
Artificial Intelligence, Diagnostic Imaging
Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging
This prospective multicenter study will evaluate the efficacy of a real-time artificial intelligence system for detecting multiple ocular fundus lesions by ultra-widefield fundus imaging in real-world settings.
- Conditions
- Artificial Intelligence, Diagnostic Imaging, Abnormality of the Fundus, Diagnostic Screening Programs
- Trial name
- NCT04859634
- Testing
- Taking an ultra-widefield fundus image
- Where
- China
- Volunteers
- 2,000 people, healthy volunteers welcome
- Sponsor
- Sun Yat-sen University
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
Deep Learning
A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT
CT-enhanced scans are routine imaging modality for the diagnosis and follow-up of liver disease. However, this means that patients will receive more radiation dose.
- Trial name
- NCT05550012
- Testing
- low-dose CT
- Where
- China
- Volunteers
- 100 people
- Sponsor
- Qianfoshan 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
Keratoconus, Eye Diseases
Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images
Monocentric clinical study to develop an imaging analysis algorithm for the Eyestar 900 to identify keratoconus corneas and improve biometry for intraocular lens calculations
- Conditions
- Keratoconus, Eye Diseases, Cataract, Corneal Ectasia
- Trial name
- DKDA (NCT04763785)
- Testing
- Corneal tomography with Eyestar 900, Corneal tomography with Pentacam, Biometry with IOL-Master
- Where
- Switzerland
- Volunteers
- 4,800 people, healthy volunteers welcome
- Sponsor
- Insel Gruppe AG, University Hospital Bern
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
Hepatoblastoma, Hepatic Hemangioendothelioma
Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants
Hepatic tumors in the perinatal period are associated with significant morbidity and mortality in affected patients. The conventional diagnostic tool, such as alpha-fetoprotein (AFP) shows limited value in diagnosis of infantile…
- Trial name
- NCT05170282
- Testing
- Radiomic Algorithm
- Where
- China
- Volunteers
- 200 people
- Sponsor
- West China Hospital
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
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
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