AI Algorithm for Radiation Treatment Planning in Cancer
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial tests a new artificial intelligence (AI) tool designed to assist in planning radiation treatments by automatically outlining important areas on CT scans. The researchers aim to determine if this AI can make the process faster and more accurate. Participants will complete surveys about the AI's effectiveness. Clinical staff at Mayo Clinic locations involved in outlining normal tissue are well-suited for this study. As a Phase 2 trial, this research focuses on measuring the AI tool's effectiveness in an initial, smaller group, offering participants a chance to contribute to advancements in radiation treatment planning.
Do I need to stop taking my current medications to join the trial?
The trial information does not specify whether you need to stop taking your current medications. It seems to focus on staff participation in AI algorithm testing rather than patient treatment.
What prior data suggests that this AI algorithm is safe for radiation treatment planning?
Research has shown that using artificial intelligence (AI) in planning radiation therapy is generally safe. One study on prostate cancer found that an AI method for analyzing CT scans made the process faster and more consistent, suggesting the technology is safe for planning treatment.
Deep learning, a type of AI, has also succeeded in similar tasks for radiation therapy. It helps identify areas in the body that need protection during treatment, further supporting AI's safe use in medical procedures.
Overall, these findings suggest that this AI technology is safe and effective for planning radiation therapy. No reports have indicated harmful effects directly related to using AI in these studies.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores a novel auto-segmentation algorithm designed to enhance radiation treatment planning. Unlike traditional methods that rely heavily on manual delineation, which can be time-consuming and vary greatly between practitioners, this algorithm aims to streamline the process by automatically identifying normal structures. This could lead to more consistent and accurate treatment plans, reducing human error and potentially improving patient outcomes. The promise of increased efficiency and precision in radiation therapy planning is what makes this development particularly intriguing.
What evidence suggests that this AI algorithm is effective for auto-segmentation in CT scans?
Research has shown that the Novel Auto Segmentation Algorithm can enhance medical imaging. In one study, this program increased radiologists' diagnostic accuracy, with performance scores rising from 0.79 to 0.88. Another study found that the program quickly identified cancer that had spread to the brain, a task typically time-consuming for radiologists. These findings suggest that the program can save time and improve accuracy in medical imaging, aiding in planning radiation therapy. Participants in this trial will complete surveys about the performance and functionality of this auto-segmentation algorithm.678910
Who Is on the Research Team?
Doug J. Moseley, PhD
Principal Investigator
Mayo Clinic in Rochester
Are You a Good Fit for This Trial?
Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Observational
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study
Follow-up
Participants are monitored for any additional feedback or observations post-survey completion
What Are the Treatments Tested in This Trial?
Interventions
- Novel Auto Segmentation Algorithm
How Is the Trial Designed?
1
Treatment groups
Experimental Treatment
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.
Find a Clinic Near You
Who Is Running the Clinical Trial?
Mayo Clinic
Lead Sponsor
Citations
A novel automatic segmentation method directly based on ...
The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus ...
Effect of a Novel Segmentation Algorithm on Radiologists ...
Using the algorithm, the Az increased significantly (range, 0.79∼0.88; p < 0.001). The proposed segmentation algorithm could improve the radiologists' diagnosis ...
3.
insightsimaging.springeropen.com
insightsimaging.springeropen.com/articles/10.1186/s13244-024-01874-7Automatic segmentation model and machine learning model ...
To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk.
4.
bmcmedimaging.biomedcentral.com
bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-025-01643-yAutomated segmentation of brain metastases in T1-weighted ...
Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be ...
An efficient segment anything model for the ...
This paper introduces the efficient medical-images-aimed segment anything model (EMedSAM), addressing the high computational demands and limited adaptability.
Safety and efficiency of a fully automatic workflow for auto- ...
A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-A retrospective multicenter study.
A comprehensive multifaceted technical evaluation ...
The framework provides a robust method for validating automatic segmentation models in radiotherapy. However, establishing standardized ...
A fully automated machine-learning-based workflow for ...
The integration of artificial intelligence into radiotherapy planning for prostate cancer has demonstrated promise in enhancing efficiency and consistency.
Machine Learning for Auto-Segmentation in Radiotherapy ...
This review surveys auto-segmentation techniques and applications in radiotherapy planning. It provides an overview of traditional approaches to auto- ...
Deep learning for automatic target volume segmentation in ...
Deep learning has shown its power in performing automatic segmentation tasks in radiation therapy for Organs-At-Risks (OAR).
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