Clinical trials: Deep Neural Network
Cardiac Surgical Procedures, Pediatrics
Bayesian Networks in Pediatric Cardiac Surgery
Pediatric cardiac surgery with cardiopulmonary bypass is associated with significant morbidity and mortality. Also score systems for risk factors, such as Risk Adjustment for Congenital Heart surgery (RACHS 1) score or the…
- Conditions
- Cardiac Surgical Procedures, Pediatrics, Cardiopulmonary Bypass
- Trial name
- NCT05537168
- Testing
- Pediatric cardiac surgery under cardiopulmonary bypass
- Where
- Belgium
- Volunteers
- 1,364 people
- Sponsor
- Brugmann University 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
Ophthalmological Disorder, Image, Body
Explainable Ocular Fundus Diseases Report Generation System
To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images.
- Trial name
- NCT05622565
- Testing
- Various modalities of ocular fundus imaging
- Where
- China
- Volunteers
- 15,000 people, healthy volunteers welcome
- Sponsor
- Sun Yat-sen 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
Solitary Skin Lesions, Skin Neoplasms
CNN-Based AI Versus Physicians for Solitary Skin Lesion Diagnosis
The goal of this observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions.
- Conditions
- Solitary Skin Lesions, Skin Neoplasms, Skin Neoplasm Malignant
- Trial name
- NCT07415291
- Where
- Turkey (Türkiye)
- Volunteers
- 17,625 people
- Sponsor
- Istanbul Training and Research Hospital
Skin Diseases
Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologists
Background: Deep neural networks (DNN) has been applied to many kinds of skin diseases in experimental settings. Objective: The objective of this study is to confirm the augmentation of deep neural networks for the diagnosis of…
- Trial name
- NCT04636164
- Testing
- Model Dermatology (deep neural networks; Build 2020)
- Where
- South Korea
- Volunteers
- 55 people
- Sponsor
- Pyoeng Gyun Choe
Hypertension
Reduce Medication Errors by Translating AESOP Model Into CPOE Systems
Medication errors are common, life-threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings.
- Trial name
- AESOP (NCT03484793)
- Testing
- AESOP service system
- Where
- Taiwan
- Volunteers
- 37 people
- Sponsor
- Taipei Medical University
Androgenetic Alopecia
Harnessing Artificial Intelligence for Diagnosing Androgenetic Alopecia: A Training and Validation Study
The aim of this study is to develop and validate deep learning models in diagnosis of male and female pattern hair loss, and assessment of its severity based on clinical and trichoscopic image by handheld dermoscopy and…
- Trial name
- NCT07294313
- Where
- Egypt
- Volunteers
- 400 people, healthy volunteers welcome
- Sponsor
- Cairo University
Endometriosis
Deep Neural Network Stratification for Use Detecting Endometriosis in Women Affected by Chronic Pelvic Pain (EndoCheck)
The goal of this observational study is to determine the clinical validity of a deep neural network algorithm that utilizes protein biomarker detection of Endometriosis - "EndoCheck" - as an "aid in diagnosis" for endometriosis…
- Trial name
- NCT05245695
- Testing
- Observational study, no intervention
- Where
- 10 sites in United States
- Volunteers
- 1,200 people
- Sponsor
- Aspira Women's Health
Skull Defect
Development of Three-dimensional Deep Learning for Automatic Design of Skull Implants
This project aims to develop an effective deep learning system to generate numerical implant geometry based on 3D defective skull models from CT scans.
- Trial name
- NCT05603949
- Testing
- 3D deep learning neural network system
- Where
- Taiwan
- Volunteers
- 6 people
- Sponsor
- Chang Gung Memorial Hospital