Building a machine learning-based evaluation model of pain intensity and treatment prognosis by analyzing epidurogram contrast patterns in patients with lumbar spinal pain.
Not Applicable
- Conditions
- Diseases of the musculoskeletal system and connective tissue
- Registration Number
- KCT0007931
- Lead Sponsor
- The Catholic University of Korea, Eunpyeong St. Mary's Hospital
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ot yet recruiting
- Sex
- All
- Target Recruitment
- 1000
Inclusion Criteria
Patients who performed transforaminal epidural steroid injection(code RSHA10202G, ELA354T) from 1st, April, 2019 until 31st, August, 2022.
Exclusion Criteria
In case of insufficient information such as VAS scores in medical records.
A person who cannot take painkillers due to gastrointestinal disorders or medical underlying diseases.
In the case where the instrument is identified on the previous history of surgery or PACS image by inserting the instrument in the lumbar spine.
Patients whose image quality is so poor that it is difficult to check the needle.
Study & Design
- Study Type
- Observational Study
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The degree of spread of contrast medium
- Secondary Outcome Measures
Name Time Method pain score(Visual analogue scale);The dose of analgesics