Evaluation of a Novel Auto Segmentation Algorithm for Normal Structure Delineation in Radiation Treatment Planning
Purpose
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.
Conditions
- Malignant Solid Neoplasm
- Hematopoietic and Lymphatic System Neoplasm
Eligibility
- Eligible Ages
- All ages
- Eligible 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
Study Design
- Phase
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
| Observational | Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study. |
|
Recruiting Locations
Mayo Clinic in Arizona
Scottsdale, Arizona 85259
Scottsdale, Arizona 85259
Mayo Clinic in Florida
Jacksonville, Florida 32224-9980
Jacksonville, Florida 32224-9980
Mayo Clinic Health System in Albert Lea
Albert Lea, Minnesota 56007
Albert Lea, Minnesota 56007
Mayo Clinic Health Systems-Mankato
Mankato, Minnesota 56001
Mankato, Minnesota 56001
Mayo Clinic in Rochester
Rochester, Minnesota 55905
Rochester, Minnesota 55905
Mayo Clinic Health System-Eau Claire Clinic
Eau Claire, Wisconsin 54701
Eau Claire, Wisconsin 54701
Mayo Clinic Health System-Franciscan Healthcare
La Crosse, Wisconsin 54601
La Crosse, Wisconsin 54601
More Details
- Status
- Recruiting
- Sponsor
- Mayo Clinic
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.