Clinical trials: Artificial Neural Network
Lung Diseases, Lung Neoplasm
The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle…
- Trial name
- NCT03849040
- Testing
- Endobronchial Ultrasound
- Where
- Canada
- Volunteers
- 52 people
- Sponsor
- St. Joseph's Healthcare Hamilton
Valvular Heart Disease
Evaluation of an Automated Smartphone-based Digital Auscultation Application for Detecting Abnormal Heart Sounds Using Deep Learning Techniques
This pilot study is to investigate the feasibility of obtaining medical grade audio phonocardiogram (PCG) recordings using a smartphone-based auscultation device in the first step.
- Trial name
- AVDA (NCT05325723)
- Where
- Switzerland
- Volunteers
- 102 people, healthy volunteers welcome
- Sponsor
- University Hospital, Basel, Switzerland
Pulmonary Hypertension (Diagnosis)
Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study
The goal of this observational study is to apply Artificial Intelligence (AI) and machine learning technology to the resting 12-lead electrocardiogram (ECG) and assess whether it can assist doctors in the early diagnosis of…
- Trial name
- NCT05942859
- Testing
- Artificial Intelligence and Machine Learning technology
- Where
- United Kingdom
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Royal United Hospitals Bath NHS Foundation Trust
Slippery Pulse, Choppy Pulse
Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study of TCM Pathological Logic Analysis
The diagnoses processes of Traditional Chinese Medicine (TCM) focus on the following four main types of diagnoses methods consisting of inspection, olfaction, inquiry, and palpation.
- Trial name
- NCT05630248
- Testing
- Recurrent Neural Network
- Where
- Taiwan
- Volunteers
- 240 people, healthy volunteers welcome
- Sponsor
- China Medical University Hospital
Non-Alcoholic Fatty Liver Disease, Non-Alcoholic Steatohepatitis
Clinical Performance of LIVERFASt Test Compared w/ Liver Biopsy in Patients w/ NAFLD.
The primary objective of this study is to assess the clinical performance of LIVERFAStTM In Vitro Diagnostic (IVD) Tests (Fibrosis score, Activity score and Steatosis score) in NAFLD suspected patients for staging of fibrosis and…
- Conditions
- Non-Alcoholic Fatty Liver Disease, Non-Alcoholic Steatohepatitis, Liver Fibroses, Liver Inflammation
- Trial name
- NCT04579874
- Testing
- LIVERFASt
- Where
- 8 sites in United States
- Volunteers
- 241 people
- Sponsor
- Fibronostics USA, Inc
Residual Paralysis, Post Anesthesia, Postoperative Complications
Predicting Neuromuscular Recovery in Surgical Patients Using Machine Learning
Despite emerging efforts to decrease residual paralysis and postoperative complications with the use of quantitative neuromuscular monitoring and reversal agents their incidences remain high.
- Conditions
- Residual Paralysis, Post Anesthesia, Postoperative Complications, Neuromuscular Blockade, Quantitative Neuromuscular Monitoring
- Trial name
- PINES (NCT05471882)
- Where
- 2 sites in Germany
- Volunteers
- 240,000 people
- Sponsor
- University Hospital Ulm
Artificial Intelligence, Colorectal Polyp
Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist.
- Trial name
- POLAR (NCT03822390)
- Testing
- CAD-CNN system
- Where
- Netherlands
- Volunteers
- 292 people
- Sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Atrial Fibrillation, Insulin Resistance
Insulin Resistance as a Predictor of Pulsed Field Ablation Success in Atrial Fibrillation (HOMA-PULSE)
This study investigates whether insulin resistance, a metabolic condition where the body's cells respond poorly to insulin, can predict the success of atrial fibrillation (AF) ablation using pulsed field ablation (PFA)…
- Conditions
- Atrial Fibrillation, Insulin Resistance, Catheter Ablation, Artificial Intelligence
- Trial name
- HOMA-PULSE (NCT07551947)
- Where
- Czechia
- Volunteers
- 120 people
- Sponsor
- Nemocnice AGEL Trinec-Podlesi a.s.
Artificial Intelligence, Endobronchial Ultrasound
Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence
To evaluate the usefulness of Deep neural network (DNN) in the evaluation of mediastinal and hilar lymph nodes with Endobronchial ultrasound (EBUS).
- Conditions
- Artificial Intelligence, Endobronchial Ultrasound, Lung Cancer
- Trial name
- NCT05739331
- Testing
- machine learning algorithm
- Where
- 2 sites in Norway
- Volunteers
- 50 people
- Sponsor
- Norwegian University of Science and Technology
Arrhythmias, Cardiac, Cardiac Arrest
Development of a Novel Convolution Neural Network for Arrhythmia Classification
Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health.
- Conditions
- Arrhythmias, Cardiac, Cardiac Arrest, Cardiac Arrythmias
- Trial name
- AI-ECG (NCT03662802)
- Testing
- Neural Network Classifier
- Where
- United States
- Volunteers
- 25,458 people, healthy volunteers welcome
- Sponsor
- Scripps Clinic
Acute Exacerbation of Chronic Obstructive Pulmonary Disease
The Severity Assessment Model for AECOPD With Combination of Disease and Syndrome
The establishment of the AECOPD severity assessment model can quickly and accurately evaluate the condition, which is of great significance for standardizing diagnosis and treatment, improving efficacy, and improving prognosis.
- Trial name
- NCT06918353
- Testing
- Diagnosis and Severity Grading of AECOPD Disease
- Where
- China
- Volunteers
- 1,500 people
- Sponsor
- Henan University of Traditional Chinese Medicine
Diabetic Retinopathy, Glaucoma
AI for the Detection of Retinal Disease and Glaucoma in Patients With Diabetes Mellitus in Primary Care
Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries.
- Conditions
- Diabetic Retinopathy, Glaucoma, Age-Related Macular Degeneration, Choroidal Nevus
- Trial name
- NCT04132401
- Testing
- algorithm
- Where
- Spain
- Volunteers
- 902 people
- Sponsor
- Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
Postoperative Delirium, Postoperative Neurocognitive Disorder
Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients
The incidence of postoperative delirium in elderly patients is high, which can lead to long-term postoperative neurocognitive disorders. Its high risk factors are not yet clear.
- Conditions
- Postoperative Delirium, Postoperative Neurocognitive Disorder, Surgery
- Trial name
- NCT06423547
- Testing
- no intervention
- Where
- China
- Volunteers
- 10,000 people
- Sponsor
- Xuanwu Hospital, Beijing
Pneumonia
An Observational Clinical Study on the Construction of an Artificial Neural Network Model for ICU Pneumonia
To achieve rapid, intelligent and accurate microbiological diagnosis and treatment for ICU pneumonia, an artificial neural network model for microbiological diagnosis is established, which depends on many clinical cases and…
- Trial name
- NCT06661499
- Testing
- establish an artificial neural network model
- Where
- China
- Volunteers
- 600 people
- Sponsor
- Chinese Medical Association
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
Dental Prosthesis
Artificial Intelligence Designed Single Tooth Dental Prostheses
Tooth loss is common and as consequence deteriorate patient's health and quality-of-life. Dental prostheses aim to restore patients' appearance and functions by replacement of missing teeth.
- Trial name
- NCT05056948
- Testing
- artificial intelligence (AI) computer assisted design (CAD)
- Where
- Hong Kong
- Volunteers
- 250 people, healthy volunteers welcome
- Sponsor
- The University of Hong Kong
Glaucoma, Tomography, Optical Coherence
The Diagnostic Performance of BMO-MRW and RNFL Thickness and Their Combinational Index Using Artificial Neural Network
This study evaluates the relationship between BMO-MRW and RNFL thickness measured by OCT. SD-OCT exam will be performed to all patients in this study.
- Trial name
- NCT03257020
- Testing
- SD-OCT
- Where
- South Korea
- Volunteers
- 402 people
- Sponsor
- Pusan National University Hospital
COVID19
Evaluation of the AudibleHealth Dx AI/ML-Based Dx SaMD Using FCV-SDS in the Diagnosis of COVID-19 Illness
The AudibleHealth Dx is a diagnostic software as a medical device (Dx SaMD) consisting of an ensemble of software subroutines that interacts with a proprietary database of Signal Data Signatures (SDS), using Artificial…
- Trial name
- NCT05175690
- Testing
- Diagnostic Software as Medical Device
- Where
- United States
- Volunteers
- 1,126 people, healthy volunteers welcome
- Sponsor
- AudibleHealth AI, Inc.
Basal Cell Carcinoma
AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images
This retrospective observational study aims to develop and evaluate a convolutional neural network (CNN)-based artificial intelligence model for risk classification and histopathological subtype prediction of basal cell carcinoma…
- Trial name
- BCC-AI (NCT07677124)
- Where
- Turkey (Türkiye)
- Volunteers
- 2,500 people
- Sponsor
- Istanbul Training and Research Hospital
Gastroesophageal Hernia
Hill-grade Knowledge Via Integrated Neural-network for Gastroscopy
The Hill classification, also known as the Hill grade, is a system used to classify the severity of gastroesophageal valve incompetence, specifically related to gastroesophageal reflux disease (GERD) and hiatal hernia.
- Trial name
- HillKING (NCT06040723)
- Testing
- EndoMind
- Where
- Germany
- Volunteers
- 195 people, healthy volunteers welcome
- Sponsor
- Wuerzburg University Hospital