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Deep Learning Aided Diagnosis of Acute Aortic Syndrome by Ultrasound Images

Deep Learning Aided Diagnosis of Acute Aortic Syndrome by Ultrasound Images

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300075590
Enrollment
Unknown
Registered
2023-09-08
Start date
2023-09-10
Completion date
Unknown
Last updated
2023-09-11

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:This study uses ultrasound impressions as the gold standard, and all reports are signed and issued by doctors more than 2-year-experience. If the impressions indicate AAS, the image is c
Index test:Deep learning model.

Sponsors

Shenzhen Second People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: The ultrasound examination involves the aorta, and the aortic area in the image is clearly visible.

Exclusion criteria

Exclusion criteria: Ultrasound images of patients with incomplete basic information (age, gender).

Design outcomes

Primary

MeasureTime frame
Sensitivity;Specificity;Accuracy;Area under the receiver operating characteristic curve;—

Countries

China

Contacts

Public ContactZhengyi Li

Shenzhen Second People's Hospital

lizhyi009@163.com+86 755 8336 6388

Outcome results

None listed

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