Unique ID issued by UMIN | UMIN000038434 |
---|---|
Receipt number | R000043270 |
Scientific Title | Machine learning application for aortic dissection |
Date of disclosure of the study information | 2019/10/31 |
Last modified on | 2020/10/29 11:36:10 |
Classifying aortic dissection based on computed topography using a machine learning approach
Machine learning application for aortic dissection
Machine learning application for aortic dissection
Machine learning application for aortic dissection
Japan |
aortic dissection
Vascular surgery |
Others
NO
This study aims to classify patients with aortic dissection and those without applying a machine learning approach for CT images.
Others
classification
diagnosis of aortic dissection
pathophysiology of aortic dissection
Observational
20 | years-old | <= |
100 | years-old | > |
Male and Female
People who visited Chibanishi General Hospital with suspicion of having aortic dissection and underwent CT scan
none
2000
1st name | Ito |
Middle name | |
Last name | Yujiro |
Chibanishi general hospital
Dept. Cardiovascular surgery
270-2251
1-107 Kanegasaku Matsudo, Chiba
047-384-8111
yujiroito@hotmail.com
1st name | Aoki |
Middle name | Y |
Last name | Yuta |
Showa University
Medical Institute of Developmental Disabilities Research
157-8577
6-11-11 Kitakarasuyama Setagaya, Tokyo, Japan
03-5315-9357
yuaoki-tky@umin.ac.jp
Chibanishi general hospital
self funding
Self funding
Japan
MIDDR at Showa University
IRB of Chibanishi general hospital
1-107 Kanegasaki Matsudo, Chiba
047-384-8111
yujiroito@hotmail.com
NO
2019 | Year | 10 | Month | 31 | Day |
Unpublished
Enrolling by invitation
2019 | Year | 12 | Month | 15 | Day |
2019 | Year | 10 | Month | 23 | Day |
2019 | Year | 12 | Month | 15 | Day |
2022 | Year | 03 | Month | 31 | Day |
observational study
2019 | Year | 10 | Month | 30 | Day |
2020 | Year | 10 | Month | 29 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000043270
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