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Clinical Trial Summary

MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis.


Clinical Trial Description

MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis. It would be a time-saving workflow if the software can assist the radiologists to detect and locate the suspected lesion. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT03746561
Study type Observational
Source Shanghai 10th People's Hospital
Contact Shisheng He, MD
Phone 021-66307580
Email TJHSS7418@TONGJI.EDU.CN
Status Not yet recruiting
Phase
Start date November 2018
Completion date May 2019

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