Presented by Arundhati (Sheel) Sharma, BSRT, CMD, RT(T)

Awareness of AI-based contouring in radiation treatment planning is crucial for maximizing efficiency while maintaining accuracy. Traditionally, the process of manually delineating organs and tumors was time-consuming, often taking hours for a single patient. The advent of AI has dramatically reduced this workload, allowing experienced dosimetrists to complete initial contours in mere minutes. However, there are potential significant pitfalls such as over-reliance: dosimetrists might inadvertently skip the vital step of manually reviewing each automated contour, which could result in missed structures or incorrectly contoured organs. To leverage AI effectively, clinical workflows must integrate robust quality assurance protocols, requiring mandatory manual checks and verifications  to ensure the highest standard of patient safety and treatment precision.

Learner Outcomes:

1. Recognize the advantages and pitfalls of using AI-based contours
2. Discuss how to use AI-based contours to the dosimetrist’s advantage
3. Implement a “Human in the Loop” workflow
4. Integrate AI as a powerful starting template rather than a final solution

Educational Level: Intermediate

Presenter:
Arundhati (Sheel) Sharma, BSRT, CMD, RT(T) started her professional journey as a Radiology Technologist, then Radiation Therapist. Sheel passed her CMD exam in 2018. She graduated from the Radiology Technology Associate Program at Indiana University and then received a Radiation Therapy Diploma from Washburn University. She completed her Dosimetry Bachelors degree at John Patrick University.

Sheel has experience with various treatment planning systems, including Eclipse, Raystation, Brainlab, Precision, and Pinnacle. She has also used Radformation products including Autocontouring. Velocity for image fusion and contouring, Aria and Mosaiq record and verify systems.

ARRT CQR Credit Distribution
There is no credit distribution for this activity.