Clinical trials: Deep Learning
Knowledge, Satisfaction
Deep Learning Model Detecting Pressure Injury
In the health care system, pressure injuries, which are among the quality indicators, are a serious patient safety problem that affects the length of hospital stay and the cost of care.
- Trial name
- NCT06641258
- Testing
- Control Group (Standard Procedure), Experimental Group
- Where
- Turkey (Türkiye)
- Volunteers
- 60 people, healthy volunteers welcome
- Sponsor
- University of Beykent
Obstructive Sleep Apnea, Obstructive Sleep Apnea-hypopnea
Updating Deep Learning Algorithms for OSA Monitoring
The objective is to enhance the reliability of the algorithm to match that of Level 1 polysomnography by leveraging the diverse data obtained from Level 1 polysomnography to refine the deep learning algorithm.
- Trial name
- NCT06522815
- Testing
- CART-I plus, Polysomnography
- Where
- South Korea
- Volunteers
- 107 people, healthy volunteers welcome
- Sponsor
- Sky Labs
Lung Diseases
Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution
The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising…
- Trial name
- NCT05398887
- Testing
- Low radiation dose CT, Underwent ultra dose chest CT, Artificial Intelligence based model
- Volunteers
- 200 people
- Sponsor
- Intermed Hospital
Essential Tremor, Deep Brain Stimulation
Probabilistic Targeting of the VIM
After having included the most improved patients by DBS among those implanted in Bordeaux Hospital University, and having defined the most active plots on tremor by its prospective grading on Fahn-Tolosa-Marin (FTM) scale and…
- Trial name
- PROBA-VIM (NCT03696420)
- Testing
- Coordinates of active plots and VIM landmarks calculation
- Where
- France
- Volunteers
- 1 people
- Sponsor
- University Hospital, Bordeaux
Artificial Intelligence
Accuracy of Artificial Intelligence in Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT
Convolutional neural network (CNN) are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter10, aiming at improving accuracy and…
- Trial name
- NCT05350228
- Testing
- CNN based model
- Where
- Egypt
- Volunteers
- 50 people
- Sponsor
- Cairo University
Surgical Procedure, Unspecified
Deep-learning Based Classification of Spine CT
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance.
- Trial name
- DETECT (NCT03790930)
- Testing
- deep learning
- Where
- China
- Volunteers
- 500 people
- Sponsor
- Shanghai 10th People's Hospital
Esophageal Neoplasms Malignant
Multimodal Endoscopic Image Fusion for Assessing Infiltration in Superficial Esophageal Squamous Cell Carcinoma
The objective of this project is to pioneer a novel protocol for the adjunctive screening of early-stage esophageal cancer and its precancerous lesions.
- Trial name
- NCT06412419
- Testing
- Magnifying Endoscopy and Endoscopic Ultrasonography
- Where
- China
- Volunteers
- 450 people
- Sponsor
- Changhai Hospital
Diabetic Retinopathy
Multicenter Diagnostic Clinical Performance Study For Automated Detection of Diabetic Retinopathy
This study is a retrospective study to assess the efficacy and accuracy of the EyeCheckup software in screening for diabetic retinopathy in healthcare settings.
- Trial name
- NCT05471986
- Testing
- Color Fundus Photography/Non-Mydriatic Opthalmic Cameras
- Volunteers
- 900 people, healthy volunteers welcome
- Sponsor
- Ural Telekomunikasyon Sanayi Ticaret Anonim Sirketi
End Stage Cancer
Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward
The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device.
- Trial name
- NCT04883879
- Where
- Taiwan
- Volunteers
- 80 people
- Sponsor
- Taipei Medical University
Artificial Intelligence, Ultrasonography
Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data
The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination.
- Conditions
- Artificial Intelligence, Ultrasonography, Elasticity Imaging Techniques, Liver Diseases
- Trial name
- LivSPECTRUS (NCT06317181)
- Testing
- Collection of elastography data, Collection of ultrasonic raw data
- Where
- 4 sites in Germany
- Volunteers
- 200 people, healthy volunteers welcome
- Sponsor
- Technische Universität Dresden
Cervical Myelopathy
Convolutional Neural Network for the Detection of Cervical Myelomalacia
Deep learning technology has been used increasingly in spine surgery as well as in many medical fields. However, it is noticed that most of the studies about this subject in the literature have been conducted except of the…
- Trial name
- NCT04796987
- Testing
- Convolutional Neural Network
- Where
- Turkey (Türkiye)
- Volunteers
- 125 people
- Sponsor
- Istanbul University
Gastrectomy for Gastric Cancer, Gastric Cancer
AI-assisted Decision-making of Reoperation for Postoperative Bleeding of Gastric Cancer
The goal of this observational study is to develop and validate a deep learning model to dynamically assess postoperative bleeding risk and assist in decision-making for re-operation in adult patients (≥18 years) diagnosed with…
- Trial name
- NCT07525765
- Where
- China
- Volunteers
- 7,000 people
- Sponsor
- First Affiliated Hospital of Zhejiang University
Hepatocellular Carcinoma, Contrast-enhanced Ultrasound
Clinical Decision Biological Biomarker and Prognosis Prediction of Hepatocellular Carcinoma by Deep Learning
Developing a deep learning model based on contrast-enhanced ultrasound (CEUS) to predict the prognosis of hepatocellular carcinoma (HCC) and aid choose operation decisions
- Conditions
- Hepatocellular Carcinoma, Contrast-enhanced Ultrasound, Prognosis, Surgery
- Trial name
- NCT05257694
- Testing
- Surgery, Ablation (Microwave ablation or Radiofrequency ablation)
- Where
- China
- Volunteers
- 1 people
- Sponsor
- Ping Liang
Pancancer
Retrospective Pathology Foundation Models
By integrating retrospective multimodal data such as pathology and imaging, AI technologies offer novel solutions for disease classification, tumor grading, histological and molecular subtyping, selection of chemotherapy…
- Trial name
- NCT07239297
- Where
- 2 sites in China
- Volunteers
- 2,000 people
- Sponsor
- Nanfang Hospital, Southern Medical University
Macular Degeneration Choroidal Neovascularization
Deep Learning Optical Coherence Tomography (OCT) and OCT Angiography (OCTA) in NVC
To evaluate the activity of neovascular macula degeneretion as assessed by SD-OCTand OCT-A using a split-person study design and deep-learning quantification.
- Trial name
- OCTA (NCT05969418)
- Testing
- Diagnostic test: using artificial intelligence to evaluate the changes OCT and OCTA parameter
- Where
- Italy
- Volunteers
- 50 people, healthy volunteers welcome
- Sponsor
- Federico II University
Cardiac Amyloidosis, Transthyretin Amyloidosis
Retrospective Identification of Scintigraphic Cardiac Amyloidosis (RISCA)
This study aims to determine the positive predictive value of incidental cardiac uptake on bone scintigraphy for diagnosing transthyretin cardiac amyloidosis, in patients who underwent scintigraphy for reasons other than…
- Trial name
- RISCA (NCT06921408)
- Volunteers
- 57 people
- Sponsor
- University Hospital, Lille
Root Canal Treatment, Obturation Quality
Diagnostic Accuracy of a Deep Learning Framework for Automated Evaluation of Root Canal Obturation Quality From Periapical Radiographs
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images.
- Trial name
- NCT07684482
- Testing
- Deep learning model
- Volunteers
- 490 people
- Sponsor
- Cairo University
Artificial Intelligence, Electrocardiogram
Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of…
- Conditions
- Artificial Intelligence, Electrocardiogram, Deep Learning, Congenital Heart Disease in Children
- Trial name
- AI-ECG-CHD (NCT06383546)
- Where
- China
- Volunteers
- healthy volunteers welcome
- Sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Gastrointestinal Disease, Endoscopy
A Multicentric Validation Study on the Accuracy of Artificial Intelligence Assisted System in Clinical Application of Digestive Endoscopy
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.
- Conditions
- Gastrointestinal Disease, Endoscopy, Artificial Intelligence
- Trial name
- NCT04232462
- Testing
- Endoscopists refer to AI for diagnosis
- Where
- China
- Volunteers
- 10,000 people
- Sponsor
- Renmin Hospital of Wuhan University
Lung Cancer, Health Screening
Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT
The goal of this clinical trial is to evaluate whether an AI tool that alerts providers to patients at high 6-year risk of lung cancer based on their chest x-ray images will improve lung cancer screening CT participation.
- Conditions
- Lung Cancer, Health Screening, Early Cancer Detection, Deep Learning
- Trial name
- NCT06910956
- Testing
- CXR-LC
- Where
- United States
- Volunteers
- 1,500 people
- Sponsor
- Massachusetts General Hospital