TARDIS: An updated artificial intelligence model to predict linear accelerator machine parameters at treatment delivery
Publication
, Journal Article
Lay, LM; Chuang, KC; Giles, W; Adamson, J
Published in: SoftwareX
July 1, 2022
We present an open-source artificial intelligence (AI) model that predicts machine parameters at treatment delivery using trajectory files from prior patients. Predictive models for IMRT and VMAT utilized a boosted and bagged tree, respectively, and predicted MLC errors with a high degree of accuracy (IMRT R2=0.99 and 0.98 for high and low-resolution respectively; VMAT R2=0.97 and 0.90). Residual error for constructed cases was <0.01 mm with R2 ranging from 0.84 – 0.99. The updated AI model is now made available to predict error in machine parameters at treatment delivery for a new DICOM-RT plan.
Duke Scholars
Published In
SoftwareX
DOI
EISSN
2352-7110
Publication Date
July 1, 2022
Volume
19
Related Subject Headings
- 0803 Computer Software
Citation
APA
Chicago
ICMJE
MLA
NLM
Lay, L. M., Chuang, K. C., Giles, W., & Adamson, J. (2022). TARDIS: An updated artificial intelligence model to predict linear accelerator machine parameters at treatment delivery. SoftwareX, 19. https://doi.org/10.1016/j.softx.2022.101146
Lay, L. M., K. C. Chuang, W. Giles, and J. Adamson. “TARDIS: An updated artificial intelligence model to predict linear accelerator machine parameters at treatment delivery.” SoftwareX 19 (July 1, 2022). https://doi.org/10.1016/j.softx.2022.101146.
Lay LM, Chuang KC, Giles W, Adamson J. TARDIS: An updated artificial intelligence model to predict linear accelerator machine parameters at treatment delivery. SoftwareX. 2022 Jul 1;19.
Lay, L. M., et al. “TARDIS: An updated artificial intelligence model to predict linear accelerator machine parameters at treatment delivery.” SoftwareX, vol. 19, July 2022. Scopus, doi:10.1016/j.softx.2022.101146.
Lay LM, Chuang KC, Giles W, Adamson J. TARDIS: An updated artificial intelligence model to predict linear accelerator machine parameters at treatment delivery. SoftwareX. 2022 Jul 1;19.
Published In
SoftwareX
DOI
EISSN
2352-7110
Publication Date
July 1, 2022
Volume
19
Related Subject Headings
- 0803 Computer Software