Evaluation of a Novel Auto Segmentation Algorithm for Normal Structure Delineation in Radiation Treatment Planning
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Mayo Clinic
- Enrollment
- 200
- Locations
- 14
- Primary Endpoint
- Proportion of success
Study Overview
Brief Summary
This study measures the utility of a novel artificial intelligence (AI) algorithm for performing auto-segmentation of computed tomography (CT) scans for radiation therapy planning.
Detailed Description
PRIMARY OBJECTIVE:
I. To measure the observed utility of an AI algorithm for normal segmentation by recording study subjects' observations of its function.
OUTLINE: This is an observational study.
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •Employment at Mayo Clinic Arizona, Florida, or Rochester (which includes Regional Practice sites located at Mayo Clinic Health System locations) as train clinical staff that participate in normal tissue segmentation
Exclusion Criteria
- •Inability to complete study surveys
Arms & Interventions
Observational
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.
Intervention: Non-Interventional Study (Other)
Outcomes
Primary Outcomes
Proportion of success
Time Frame: Baseline
Will be evaluated by question 1 of the end user survey, which evaluates the level of modification to the artificial intelligence generated auto-segmentation structures that was required (no modification, minor modification, or major modification). Auto-segmentation algorithm data will be collected through an electronic data collection form.
Secondary Outcomes
No secondary outcomes reported