Classifying aortic dissection based on computed topography using a machine learning approach

Machine learning application for aortic dissection - Machine learning application for aortic dissection

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000038434

Enrollment

2000

Registered

2019-10-31

Start date

2019-12-15

Completion date

Unknown

Last updated

2026-06-29

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

Conditions

aortic dissection

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: People who visited Chibanishi General Hospital with suspicion of having aortic dissection and underwent CT scan

Exclusion criteria

Exclusion criteria: none

Design outcomes

Primary

Measure	Time frame
diagnosis of aortic dissection	—

Secondary

Measure	Time frame
pathophysiology of aortic dissection	—

Countries

Japan

Contacts

Public ContactAoki Yuta

Showa University Medical Institute of Developmental Disabilities Research

yuaoki-tky@umin.ac.jp03-5315-9357

Outcome results