FindTrial
Sign In

Efficacy and Safety of Deep Learning for Detecting Acute Aortic Syndrome in Non-Contrast CT: A Prospective, Single-Arm Clinical Trial

Efficacy and Safety of Deep Learning for Detecting Acute Aortic Syndrome in Non-Contrast CT: A Prospective, Single-Arm Clinical Trial

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400094192
Enrollment
Unknown
Registered
2024-12-18
Start date
2024-12-20
Completion date
Unknown
Last updated
2025-01-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

acute aortic syndrome

Interventions

Gold Standard:The diagnosis of AAS is made by a senior radiologist (researcher) based on relevant authoritative international guidelines or standards, combined with the medical history, related examin
Index test:Evaluate the performance of the binary classification task of acute aortic syndrome vs. non-acute aortic syndrome using metrics such as AUC, sensitivity, specificity, positive predictive va

Sponsors

Shanghai Changhai Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Patients in the emergency department undergo chest CT scans; 2. Age > 18 years

Exclusion criteria

Exclusion criteria: 1. Initial suspicion of acute aortic syndrome; 2. Poor image quality.

Design outcomes

Primary

MeasureTime frame
The area under the receiver operating characteristic curve (AUC-ROC);—

Secondary

MeasureTime frame
accuracy;sensitivity;specificity;positive predictive value (PPV);negative predictive value (NPV);balance accuracy;—

Countries

China

Contacts

Public ContactYun Bian

Shanghai Changhai Hospital

bianyun2012@foxmail.com+86 138 1635 7024

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026