Results of an AI-Based Image Review System to Detect Patient Misalignment Errors in a Multi-Institutional Database of CBCT-Guided Radiotherapy Treatments
Present knowledge of patient setup and alignment errors in image-guided radiotherapy (IGRT) relies on voluntary reporting, which is thought to underestimate error frequencies. A manual retrospective patient-setup misalignment error search is infeasible due to the bulk of cases to be reviewed. We applied a deep learning-based misalignment error detection algorithm (EDA) to perform a fully-automated retrospective error search of clinical IGRT databases and determine an absolute gross patient misalignment error rate.
Source: International Journal of Radiation Oncology * Biology * Physics - Category: Radiology Authors: Dishane C. Luximon, Jack Neylon, Timothy Ritter, Nzhde Agazaryan, John V. Hegde, Michael L. Steinberg, Daniel A. Low, James M. Lamb Tags: Physics Contribution Source Type: research
More News: Biology | Cancer & Oncology | Databases & Libraries | Learning | Physics | Radiology | Universities & Medical Training