Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance.

ABSTRACT:

Purpose

Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup.

Methods

An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (mos

SUBMITTER: Neylon J 

PROVIDER: S-EPMC10476980 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature