Clinical trials: Deep Learning Model
Heart Failure, Heart Failure With Reduced Ejection Fraction
AI-ECG for Time-Resolved Prediction of HFrEF
This study aims to develop and validate a deep learning-based electrocardiogram (ECG) model for predicting the future risk of heart failure with reduced ejection fraction (HFrEF).
- Conditions
- Heart Failure, Heart Failure With Reduced Ejection Fraction, Left Ventricular Dysfunction, Cardiac Remodeling
- Trial name
- NCT07519434
- Where
- China
- Volunteers
- 286,709 people, healthy volunteers welcome
- Sponsor
- Shanghai Zhongshan Hospital
Osteoporosis
AI Model for Bone Mineral Density Prediction From X-Ray Images
Osteoporosis, a pervasive skeletal disorder characterized by diminished bone strength predisposing individuals to an increased risk of fractures, presents a substantial public health challenge globally.
- Trial name
- NCT06652061
- Testing
- Dual Energy X-Ray Absorptiometry Scan (DEXA Scan), X-Ray (Hip and Spine)
- Where
- Bangladesh
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Bangladesh University of Engineering and Technology
Extra Canals
AI Detection Model of Extra Root Canals in Mandibular Premolars Using CBCT Scans
Successful endodontic treatment depends on the complete identification and management of the entire root canal system. Missed root canals are a major cause of endodontic failure, particularly in mandibular premolars, which…
- Trial name
- NCT07689708
- Testing
- AI Model to detect any extra canals in mandibular premolars
- Volunteers
- 272 people, healthy volunteers welcome
- Sponsor
- Cairo University
AKI - Acute Kidney Injury
Risk Factors and Deep Learning Model for CI-AKI
Contrast-associated acute kidney injury (CI-AKI) is a sudden and significant decline in renal function resulting from the use of a contrast agent.
- Trial name
- NCT06596785
- Where
- China
- Volunteers
- 2,000 people, healthy volunteers welcome
- Sponsor
- Xiao Li，MD
Type2 Diabetes
Social Network Analysis to Identify Community Health Workers
The objective of this study is to recruit influential community members using Snowball Sampling Methods. Community members identified through social network analysis as influential and well-connected will be trained as community…
- Trial name
- NCT04210024
- Testing
- Diabetes Empowerment Education Program
- Where
- 2 sites in United States
- Volunteers
- 91 people, healthy volunteers welcome
- Sponsor
- Brittany L Smalls
Cancer Diagnosis, Liver Cancer, Adult
Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening.
- Conditions
- Cancer Diagnosis, Liver Cancer, Adult, Cancer Screening, Colorectal Cancer (CRC)
- Trial name
- NCT06822413
- Testing
- No Interventions
- Where
- 4 sites in China
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
Ophthalmic Imaging, Deep Learning
Diagnostic Performance of an AI-based Model for TCM Constitution Classification Using Ophthalmic Imaging
To evaluate the diagnostic performance of a multimodal deep learning model for identifying biased Traditional Chinese Medicine (TCM) constitutions using ophthalmic imaging
- Conditions
- Ophthalmic Imaging, Deep Learning, Artificial Intelligence (AI), Traditional Chinese Medicine Constitution
- Trial name
- NCT07127939
- Testing
- AI-based Ocular Diagnosis Model for TCM Constitutions
- Where
- China
- Volunteers
- 1,024 people, healthy volunteers welcome
- Sponsor
- Second Affiliated Hospital of Nanchang University
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
Uterine Cervical Neoplasms
AI Model for Cervical Cancer Detection From Colposcopy Images
Cervical cancer is a significant health issue, particularly in low-income countries, where late diagnosis and limited access to screenings contribute to high mortality rates.
- Trial name
- NCT06644248
- Testing
- Colposcopy
- Where
- Bangladesh
- Volunteers
- 500 people, healthy volunteers welcome
- Sponsor
- Bangladesh University of Engineering and Technology
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
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
Diagnostic Imaging, Common Systemic Diseases
Prospective User Study and Clinical Validation of Multimodal Medical Imaging Large Models
Following model development and locking, the fixed model is evaluated in prospectively collected CT cohorts from a single center. The study is observational and does not affect clinical care.
- Conditions
- Diagnostic Imaging, Common Systemic Diseases, Artificial Intelligence (AI)
- Trial name
- NCT07555002
- Testing
- Standalone Radiologist Interpretation, AI-assisted Radiologist Interpretation
- Where
- China
- Volunteers
- 310 people, healthy volunteers welcome
- Sponsor
- The Third Affiliated Hospital of Southern Medical University
AKI
Deep Learning Model and Risk Factors for Tacrolimus-related Acute Kidney Injury
In this study, the investigators aim to develop a risk prediction model for acute kidney injury (AKI) in hospitalized patients using the calcineurin inhibitor tacrolimus.
- Trial name
- NCT06596798
- Where
- China
- Volunteers
- 1,200 people, healthy volunteers welcome
- Sponsor
- Qianfoshan Hospital
Anterior Segment Diseases
Multimodal Deep Learning Model for Multi-task Diagnosis and Triage Suggestions of Ophthalmic Diseases
Accurate and comprehensive interpretation of anterior segment diseases from slit-lamp and smartphone photographs remains a clinical challenge due to the limited specificity and structure of existing Artificial Intelligence tools.
- Trial name
- NCT07447973
- Testing
- Multimodal Vision-language Model Diagnosis
- Where
- China
- Volunteers
- 2,000 people, healthy volunteers welcome
- Sponsor
- Guangdong Provincial People's Hospital
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
Bone Aging, Osteoporosis Diagnosis
Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
This study is a retrospective analysis that uses abdominal CT scans, which were originally taken for other medical reasons, to estimate bone age.
- Trial name
- NCT07162168
- Where
- China
- Volunteers
- 3,000 people, healthy volunteers welcome
- Sponsor
- Peking University People's Hospital
Coronary Heart Disease, Acute Myocardial Infarction
Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
Based on the clinical data of patients, a machine learning model for coronary heart disease diagnosis was established to evaluate whether the model could improve the accuracy of coronary heart disease diagnosis, and to evaluate…
- Conditions
- Coronary Heart Disease, Acute Myocardial Infarction, Angina
- Trial name
- NCT05018715
- Testing
- Machine learning model diagnosis
- Where
- China
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Xiang Ma
Jaundice, Deep Learning
A Prospective Study: Smart Phone Application for Measure Serum Bilirubin Through Sclera Images
The primary efficacy endpoints are the standard deviation and coefficient of determination (R2) between predicted and actual values for the bilirubin regression model, and the grading accuracy for the jaundice severity…
- Conditions
- Jaundice, Deep Learning, Hyperbilirubinemia, Sclera
- Trial name
- NCT06421844
- Where
- China
- Volunteers
- 270 people, healthy volunteers welcome
- Sponsor
- Air Force Military Medical University, China
AKI, Therapeutic Drug Monitoring (TDM)
Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring
In this study, the investigators will focus on hospitalized patients using cyclosporine and develop an acute kidney injury risk prediction model through in-depth analysis of electronic medical record data, employing interpretable…
- Trial name
- NCT06596811
- Where
- China
- Volunteers
- 1,200 people, healthy volunteers welcome
- Sponsor
- Qianfoshan Hospital
Gingival Recessions
AI-Based Prediction of Root Coverage Outcome From Intraoral Photographs
This study evaluates whether the outcome of root coverage surgery can be predicted from a preoperative intraoral photograph. Adults with Cairo RT1,RT2 or RT3 gingival recessions treated with a coronally advanced flap and a…
- Trial name
- NCT07775365
- Testing
- Coronally advanced flap with subepithelial connective tissue graft, Deep learning based prediction of root coverage outcome
- Where
- Turkey (Türkiye)
- Volunteers
- 36 people, healthy volunteers welcome
- Sponsor
- Marmara University