Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy

Pillai, Malvika; Adapa, Karthik; Das, Shiva K.; Mazur, Łukasz; Dooley, John; Marks, Lawrence B.; Thompson, Reid F.; Chera, Bhishamjit S.

doi:10.1016/j.jacr.2019.06.001

Cited by 36 publications

(27 citation statements)

References 54 publications

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“…In assessing the status of automation tool development, it seems likely that lower dimensional problems, such as treatment parameter comparison, can be easily handled by scripts/programs. Higher dimensional problems in physician order error, including disease staging and treatment modality decision, may be taken care of by machine learning, such as a k‐means clustering algorithm, 8 random forest methods, 37 or Bayesian networks as proposed by Kalet et al 38 and further developed by Luk et al 31 As Kalet et al 39 and Pallai et al 40 pointed out, machine learning still faces many challenges and must be quality assured before introduction into the clinic. The breakthrough of automation tools or machine learning beyond low‐level checks will take some time.…”

Section: Discussionmentioning

confidence: 99%

“…Auto_UMMS_Exp can help to achieve 6%, 18%, and 46% in corresponding reductions. forest methods, 37 or Bayesian networks as proposed by Kalet et al 38 and further developed by Luk et al 31 As Kalet et al 39 and Pallai et al 40 pointed out, machine learning still faces many challenges and must be quality assured before introduction into the clinic. The breakthrough of automation tools or machine learning beyond low-level checks will take some time.…”

Section: Photon Categoriesmentioning

confidence: 99%

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Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Zhang

Guerrero

et al. 2021

J Applied Clin Med Phys

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Purpose: The recently published AAPM TG-275 and the public review version of TG-315 list new recommendations for comprehensive and minimum physics initial chart checks, respectively. This article addresses the potential development and benefit of initial chart check automation when these recommendations are implemented for clinical photon/electron EBRT. Methods: Eight board-certified physicists with 2-20 years of clinical experience performed initial chart checks using checklists from TG-275 and TG-315. Manual check times were estimated for three types of plans (IMRT/VMAT, 3D, and 2D) and for prostate, whole pelvis, lung, breast, head and neck, and brain cancers. An expert development team of three physicists re-evaluated the automation feasibility of TG-275 checklist based on their experience of developing and implementing the in-house and the commercial automation tools in our institution. Three levels of initial chart check automation were simulated: (1) Auto_UMMS_tool (which consists of in-house program and commercially available software); (2) Auto_TG275 (with full and partial automation as indicated in TG-275); and (3) Auto_UMMS_exp (with full and partial automation as determined by our experts' re-evaluation). Results: With no automation of initial chart checks, the ranges of manual check times were 29-56 min (full TG-315 list) and 102-163 min (full TG-275 list), which varied significantly with physicists but varied little at different tumor sites. The 69 of 71 checks which were considered as "not fully automated" in TG-275 were reevaluated with more automation feasibility. Compared to no automation, the higher levels of automation yielded a great reduction in both manual check times (by 44%-98%) and potentially residual detectable errors (by 15-85%). Conclusion: The initial chart check automation greatly improves the practicality and efficiency of implementing the new TG recommendations. Revisiting the TG reports with new technology/practice updates may help develop and utilize more automation clinically.

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Section: Discussionmentioning

confidence: 99%

Section: Photon Categoriesmentioning

confidence: 99%

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Zhang

Guerrero

et al. 2021

J Applied Clin Med Phys

View full text Add to dashboard Cite

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“…), AI has the potential to foresee stray behaviors with high selectivity allowing efficient triage for problem solving as well as pre-emptive actions. This will improve machine uptime, reliability and congruence between planned and delivered treatment [138]. One source of investigation includes the use of AI models to predict deviations in multileaf collimators (MLCs) positions to perform maintenance accordingly [139,140].…”

Section: Machine Qamentioning

confidence: 99%

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Vandewinckele

Claessens

Dinkla

et al. 2020

Radiotherapy and Oncology

180

116

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This work commenced during the 3rd European Society for Therapeutic Radiology and Oncology (ESTRO) physics workshop on 'Implementation/commissioning/QA of artificial intelligence techniques' in Budapest (2019) Radiotherapy and Oncology xxx (xxxx) xxx Contents lists available at ScienceDirect Radiotherapy and Oncology j o u r n a l h o m e p a g e : w w w. t h e g r e e n j o u r n a l. c o m Please cite this article as: L. Vandewinckele, M. Claessens, A. Dinkla et al., Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance, Radiotherapy and Oncology,

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“…These are all procedures that, when replaced by AI, will increase efficiency and reduce the time spent on planning and treatment. The arrival of new commercial software products [26] already indicate that automation of workflow is something we will face in the very near future.…”

Section: Will Ai Replace Professionals In Radiation Oncology?mentioning

confidence: 99%

The changing role of radiation oncology professionals in a world of AI – Just jobs lost – Or a solution to the under-provision of radiotherapy?

Korreman

Eriksen

Grau

2021

Clinical and Translational Radiation Oncology

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Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy

Cited by 36 publications

References 54 publications

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

The changing role of radiation oncology professionals in a world of AI – Just jobs lost – Or a solution to the under-provision of radiotherapy?

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