Clinical trials: Deep Neural Network
Robot Surgery
Transfer Learning of a Neural Network for Robotic Surgical Assessment
The goal of this observational study is to explore how pretrained artificial intelligence (AI) models, trained on preclinical data, can improve the accuracy of action recognition and skills assessment in robot-assisted surgery…
- Trial name
- NCT06612606
- Testing
- observational study
- Where
- Denmark
- Volunteers
- 5 people, healthy volunteers welcome
- Sponsor
- Aalborg University
Thoracic Diseases, Lung Diseases
AI-Based Assessment of Chest Diseases on Chest X-ray Imaging Using DenseNet-121 and a CheXpert-Trained Model
Chest X-rays are widely used to detect thoracic and lung conditions, but reviewing high volumes of radiographs can lead to workload strain and variation between interpreters.
- Trial name
- NCT07834879
- Volunteers
- 100 people
- Sponsor
- Assiut University
Ultrasound Therapy; Complications
Identification of Interscalene Brachial Plexus on Ultrasonography Using a Deep Neural Network
The purpose of the study is to develop and validate an algorithm based on deep neural networks (DNNs) to identify interscalene brachial plexus on ultrasonography automatically.
- Trial name
- IBRUNNET (NCT04183972)
- Testing
- ultrasound examination
- Where
- China
- Volunteers
- 1,126 people
- Sponsor
- Huashan Hospital
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
Blinking, Deep Learning
Development and Validation of Deep Neural Networks for Blinking Identification and Classification
Primary objective of this study is the development and validation of a system of deep neural networks which automatically detects and classifies blinks as "complete" or "incomplete" in image sequences.
- Trial name
- NCT04828187
- Testing
- Comparison of the proposed artificial network with the ground truth
- Where
- 2 sites in Greece
- Volunteers
- 8 people, healthy volunteers welcome
- Sponsor
- Democritus University of Thrace
Thyroidectomy
DCNN Developed for Detection and Assessing the Perfusion of PTG
Since the anatomical location and appearance of the parathyroid gland (PTG) vary, detection of the PTG and preserving the blood supply are among the difficulties encountered during a thyroidectomy procedure.
- Trial name
- NCT05869058
- Testing
- a deep convolutional neural network
- Where
- China
- Volunteers
- 300 people
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen 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
Gait Disorders, Neurologic, Artificial Intelligence
Deep Neural Network for Stroke Patient Gait Analysis and Classification
Lower limbs of stroke patients gradually recover through Brunnstrom stages, from initial flaccid status to gradually increased spasticity, and eventually decreased spasticitiy. Throughout this process.
- Trial name
- NCT04968418
- Testing
- APDM OPAL system wearable IMU
- Where
- Taiwan
- Volunteers
- 100 people, healthy volunteers welcome
- Sponsor
- Cheng-Hsin General Hospital
Coronary Artery Disease, Plaque, Atherosclerotic
Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography
The goal of this observational study is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients.
- Trial name
- VALUE (NCT06025305)
- Testing
- Intravascular imaging test, Coronary plaque assessment
- Where
- China
- Volunteers
- 2,000 people
- Sponsor
- Jinling Hospital, China
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
Pneumoconiosis
Evaluation of Pneumoconiosis High Risk Early Warning Models
Precaution of pneumoconiosis is more important than treatment. However, the current process can't early warn the high-risk dust exposed workers until they are diagnosed with pneumoconiosis.
- Trial name
- NCT04952675
- Where
- China
- Volunteers
- 200 people
- Sponsor
- Peking University Third Hospital
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
Hepatocellular Carcinoma (HCC), Radiofrequency Ablation
Ga-68 Dolacga PET Scan in HCC Under RFA
This study aims to investigate the use of Ga-68 Dolacga PET scan technology to assess treatment response and liver function changes in patients of early-stage liver cancer receiving RFA.
- Conditions
- Hepatocellular Carcinoma (HCC), Radiofrequency Ablation, PET Scan, Computed Tomography
- Trial name
- HCC; RFA (NCT06792097)
- Testing
- Ga-68 Dolacga PET scan
- Volunteers
- 10 people
- Sponsor
- National Taiwan University Hospital
Renal Cyst
Multiphase CT Deep Learning for Bosniak II-III Renal Cysts
This retrospective study combine radiomics and deep learning models to predict malignancy in Bosniak II-III cystic renal masses, aiming for improving preoperative risk assessment and reducing unnecessary surgery for benign…
- Trial name
- NCT07833124
- Where
- China
- Volunteers
- 223 people
- Sponsor
- Shanghai Zhongshan Hospital
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
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