Clinical trials: Deep Learning Model
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
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
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
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
Gastric Cancer (GC)
Recurrence and Prognosis Prediction Model for Gastric Cancer
This study, utilizing a large-scale multicenter Eastern database, has established a Deep Learning-based predictive model for recurrence following gastric cancer surgery, which demonstrates robust discriminatory power for early…
- Trial name
- NCT07243847
- Testing
- surgery and/or chemo
- Volunteers
- 5,000 people
- Sponsor
- Fudan University
Endodontic Retreatment, Non-surgical Retreatment
Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal…
- Conditions
- Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence)
- Trial name
- Ai Retreatment (NCT07611279)
- Testing
- Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
- Volunteers
- 123 people
- Sponsor
- Cairo 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
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
Renal Cell Carcinoma (RCC), Renal Cyst
The CT-based Deep Learning Model Predicts Complications in Partial Nephrectomy
The investigators combine radiomics and deep learning to analyze the lesions more thoroughly, aiming for a more accurate prediction of complications in partial nephrectomy, and compare this approach with traditional models.
- Trial name
- NCT06876584
- Where
- China
- Volunteers
- 1,474 people
- Sponsor
- Du Lingzhi
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
Carotid Stenosis
Risk Prediction Model for Cerebrovascular Events in Carotid Artery Stenosis
Atherosclerotic carotid artery stenosis is a major cause of stroke, and early identification of high-risk patients combined with surgical intervention can significantly reduce stroke risk.
- Trial name
- NCT06452173
- Testing
- Plasma, carotid high-resolution magnetic resonance imaging
- Where
- China
- Volunteers
- 300 people
- Sponsor
- Beijing Tiantan 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
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
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
Non-Small Cell Lung Cancer
AI Models for Predicting Occult Pleural Dissemination in NSCLC
Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery.
- Trial name
- NCT07065422
- Where
- China
- Volunteers
- 326 people
- Sponsor
- Daping Hospital and the Research Institute of Surgery of the Third Military Medical 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
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
Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model
Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)
This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's…
- Conditions
- Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model, Recurrent Renal Cell Cancer
- Trial name
- NCT06088134
- Where
- China
- Volunteers
- 800 people
- Sponsor
- Mingzhao Xiao