Automatic Diagnosis of Spinal Stenosis on CT (ASSIST)

Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning

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.

Study Overview

• Status: Unknown
• Conditions: Spinal Stenosis
• Intervention / Treatment: Diagnostic test: deep learning

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.

• Study Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

• Study Contact: Name: Shisheng He, MD; Phone Number: 021-66307580; Email: TJHSS7418@TONGJI.EDU.CN
• Study Contact Backup: Name: GUOXIN FAN, MD; Phone Number: 021-66307580; Email: GFAN@TONGJI.EDU.CN

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Spinal stenosis is a narrowing of the spaces within the spine, which can put pressure on the nerves that travel through the spine. Spinal stenosis occurs most often in the back, the neck, and sometimes the thoracic spine.

Some people with spinal stenosis may not have symptoms. Others may experience pain, tingling, numbness and muscle weakness. Symptoms can worsen over time.

Description

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 Plan

How is the study designed?

Design Details

• Observational Models: Case-Only
• Time Perspectives: Retrospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
spinal stenosis; Spinal 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.	Diagnostic test: deep learning; detect and classify spinal stenosis by deep learning

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
diagnostic accuracy of deep learning	Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT	1 day

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Shanghai 10th People's Hospital
• Collaborators: Brigham and Women's Hospital; Shanghai East Hospital; Shanghai Tongji Hospital, Tongji University School of Medicine

Study record dates

Study Major Dates

• Study Start (Anticipated): November 1, 2018
• Primary Completion (Anticipated): April 1, 2019
• Study Completion (Anticipated): May 1, 2019

Study Registration Dates

• First Submitted: November 7, 2018
• First Submitted That Met QC Criteria: November 16, 2018
• First Posted (Actual): November 19, 2018

Study Record Updates

• Last Update Posted (Actual): November 19, 2018
• Last Update Submitted That Met QC Criteria: November 16, 2018
• Last Verified: November 1, 2018

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Pathological Conditions, Anatomical; Spinal Diseases; Bone Diseases; Constriction, Pathologic; Spinal Stenosis
• Other Study ID Numbers: SHSY181022

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Undecided

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No