A novel method for radiotherapy patient identification using surface imaging

Autor: Caroline Vanderstraeten, Percy Gates, Quinton Verchick, J Maurer, Benjamin Sintay, David Wiant, Han Liu, T. Lane Hayes
Rok vydání: 2015
Předmět:
Surface (mathematics)
safety
medicine.medical_specialty
Patient Identification Systems
Similarity (geometry)
Image processing
Breast Neoplasms
Radiotherapy Setup Errors
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Imaging
Three-Dimensional
surface imaging
medicine
Image Processing
Computer-Assisted
Humans
Radiation Oncology Physics
Radiology
Nuclear Medicine and imaging
Computer Simulation
Instrumentation
Aged
Retrospective Studies
Aged
80 and over
patient identification
Radiation
business.industry
Patient Selection
Radiotherapy Planning
Computer-Assisted
Process (computing)
Pattern recognition
Radiotherapy Dosage
image‐guided radiotherapy
Middle Aged
Linear discriminant analysis
Surgery
AlignRT
030220 oncology & carcinogenesis
Reference surface
Female
Metric (unit)
Artificial intelligence
Radiotherapy
Intensity-Modulated
business
Software
Zdroj: Journal of Applied Clinical Medical Physics
ISSN: 1526-9914
Popis: 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. PACS number(s): 87.53.Jw, 87.55.N‐, 87.55.Qr, 87.57.N‐, 87.63.L‐
Databáze: OpenAIRE
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