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Automatic Diagnosis of Spinal Stenosis on CT

Conditions
Spinal Stenosis
Interventions
Diagnostic Test: deep learning
Registration Number
NCT03746561
Lead Sponsor
Shanghai 10th People's Hospital
Brief 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.

Detailed 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 & Eligibility

Status
UNKNOWN
Sex
All
Target Recruitment
500
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).

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
spinal stenosisdeep learningSpinal stenosis is a narrowing of the spaces within your spine, which can put pressure on the nerves that travel through the spine. Spinal stenosis occurs most often in the lower back and the neck.
Primary Outcome Measures
NameTimeMethod
diagnostic accuracy of deep learning1 day

Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT

Secondary Outcome Measures
NameTimeMethod
Diagnostic Performance of deep learning1 day

Sensitivity, specificity, positive predictive value and negative predictive value of deep learning compared with radiologists' labels based on CT

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