Hematopoietic and Lymphatic System Neoplasm, Malignant Solid Neoplasm
Conditions
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.
Interventions
OTHERNon-Interventional Study
Non-interventional study
Sponsors
Mayo Clinic
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of success | 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. |
Countries
United States
Contacts
CONTACTClinical Trials Referral Office
PRINCIPAL_INVESTIGATORDoug J. Moseley, PhD
Mayo Clinic in Rochester
Outcome results
None listed