Automatic Diagnosis of Spinal Stenosis on CT

Spinal Stenosis Clinical Trial

— ASSIST  

Official title:

Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT03746561
• Other study ID #: SHSY181022
• Status: Not yet recruiting
• Start date: November 2018
• Est. completion date: May 2019

Study information

• Verified date: November 2018
• Source: Shanghai 10th People's Hospital
• Contact: Shisheng He, MD
• Phone: 021-66307580
• Email: TJHSS7418[@]TONGJI.EDU.CN
• Is FDA regulated: No
• Study type: Observational

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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 500
• Est. completion date: May 2019
• Est. primary completion date: April 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Age >18 years

• with radiologists' CT reports on cervical, thoracic and lumbar stenosis

Exclusion Criteria:

• not applicable (only specific levels with extensive infections, fractures, tumor, high-grade spondylolisthesis would be excluded for analysis).

Related Conditions & MeSH terms

• Constriction, Pathologic
• Spinal Stenosis

Intervention

Diagnostic Test:
• deep learning
detect and classify spinal stenosis by deep learning

Locations

Country	Name	City	State
n/a

Sponsors (4)

Lead Sponsor	Collaborator
Shanghai 10th People's Hospital	Brigham and Women's Hospital, Shanghai East Hospital, Shanghai Tongji Hospital, Tongji University School of Medicine

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	diagnostic accuracy of deep learning	Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT	1 day
Secondary	Diagnostic Performance of deep learning	Sensitivity, specificity, positive predictive value and negative predictive value of deep learning compared with radiologists' labels based on CT	1 day