Clinical Trials Logo

Clinical Trial Summary

The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.


Clinical Trial Description

1. The raw data from patients who underwent head and neck CTA, coronary CTA, and abdominal CTA in both standard dose and double low-dose groups were included. 2. Techniques such as filtered back projection, iterative reconstruction, and deep learning reconstruction were performed. 3. The feasibility of deep learning reconstruction in double low-dose CTA was evaluated based on image quality and diagnostic performance. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT06372756
Study type Observational
Source Tongji Hospital
Contact Youfa M Tang, Doctor
Phone 8613554101223
Email 1525573397@qq.com
Status Recruiting
Phase
Start date June 1, 2023
Completion date March 2026

See also
  Status Clinical Trial Phase
Not yet recruiting NCT05550012 - A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT N/A
Completed NCT04921488 - Interest of Artificial Intelligence in Cancer Screening Colonoscopy N/A
Completed NCT06274502 - Automated Detection and Diagnosis of Pathological DRGs in PHN Patients Using Deep Learning and Magnetic Resonance
Recruiting NCT05046366 - Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.
Completed NCT04828187 - Development and Validation of Deep Neural Networks for Blinking Identification and Classification
Recruiting NCT04824378 - Study on Classification Method of Indocyanine Green Lymphography Based on Deep Learning
Recruiting NCT04592068 - AI Classifies Multi-Retinal Diseases
Recruiting NCT05058599 - Reconstruction Technology to Auxiliary Diagnosis and Guarantee Patient Privacy
Recruiting NCT05536024 - Combing a Deep Learning-Based Radiomics With Liquid Biopsy for Preoperative and Non-invasive Diagnosis of Glioma
Completed NCT05323279 - Evaluate the Effects of An AI System on Colonoscopy Quality of Novice Endoscopists N/A
Completed NCT06278272 - AI Evaluation of Pancreatic Exocrine Insufficiency in CP Patients
Recruiting NCT05426135 - Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment
Recruiting NCT05444166 - Explore the Relationship Between the Percentage of Colonoscopy Withdrawal Overspeed and the ADR
Recruiting NCT05617469 - DLCS for Predicting Neoadjuvant Chemotherapy Response
Active, not recruiting NCT05182099 - High Resolution HBA-MRI Using Deep Learning Reconstruction N/A
Recruiting NCT05204186 - Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence
Recruiting NCT06383546 - Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
Active, not recruiting NCT05041777 - Optical-Coherence Tomography for the Non-invasive Diagnosis and Subtyping of Basal Cell Carcinoma
Not yet recruiting NCT06118840 - IDEAL Study: Blinded RCT for the Impact of AI Model for Cerebral Aneurysms Detection on Patients' Diagnosis and Outcomes N/A
Completed NCT06167863 - Retrospective Analysis of the Correlation Between Imaging Features and Pathology, Prognosis in Renal Tumors