title: A novel method for radiotherapy patient identification using surface imaging.
abstract: Performing a procedure on the wrong patient or site is one of the greatest errors that can occur in medicine. The addition of automation has been shown to reduce errors in many processes. In this work we explore the use of an automated patient identification process using optical surface imaging for radiotherapy treatments. Surface imaging uses visible light to align the patient to a reference surface in the treatment room. It is possible to evaluate the similarity between a daily set-up surface image and the reference image using distance to agreement between the points on the two surfaces. The higher the percentage overlapping points within a defined distance, the more similar the surfaces. This similarity metric was used to intercompare 16 left-sided breast patients. The reference surface for each patient was compared to 10 daily treatment surfaces for the same patient, and 10 surfaces from each of the other 15 patients (for a total of 160 comparisons per patient), looking at the percent of points overlapping. For each patient, the minimum same-patient similarity score was higher than the maximum different-patient score. For the group as a whole a threshold was able to classify correct and incorrect patients with high levels of accuracy. A 10-fold cross-validation using linear discriminant analysis gave cross-validation loss of 0.0074. An automated process using surface imaging is a feasible option to provide nonharmful daily patient identification verification using currently available technology.
authors.affiliation: Cone Health Cancer Center. david.wiant@conehealth.com.
authors.forename: David B
authors.initials: DB
authors.lastname: Wiant
authors.forename: Quinton
authors.initials: Q
authors.lastname: Verchick
authors.forename: Percy
authors.initials: P
authors.lastname: Gates
authors.forename: Caroline L
authors.initials: CL
authors.lastname: Vanderstraeten
authors.forename: Jacqueline M
authors.initials: JM
authors.lastname: Maurer
authors.forename: T Lane
authors.initials: TL
authors.lastname: Hayes
authors.forename: Han
authors.initials: H
authors.lastname: Liu
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Sintay
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008875:Middle Aged; D010355:Patient Identification Systems; D018579:Patient Selection; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D061106:Radiotherapy Setup Errors / Q000517:prevention & control*; D050397:Radiotherapy, Intensity-Modulated; D012189:Retrospective Studies; D012984:Software