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The Potential of E-Learning Interventions for AI-assisted Contouring Skills in Radiotherapy
Closed for proposals
Project Type
Project Code
E33046CRP
2329Approved Date
Status
Start Date
Expected End Date
Completed Date
26 August 2024Participating Countries
Description
In recent years, AI-algorithms, namely deep learning-based algorithms, have improved auto-segmentation drastically. It is generally believed that the use of such tools will lead to lowered inter-observer variation and time savings for clinical staff. A wide palette of commercial deep learning-based auto-segmentation solutions are emerging with the promise of leveraging the aforementioned benefits. The selection and contouring of target volumes and organs-at-risk (OARs) has become a key step in modern radiation oncology. Concepts and terms for definition of gross tumor volume, clinical target volume and OARs have been continuously evolving (e.g. through ICRU reports 50, 62, 78, 83) and have become widely disseminated and accepted by the European and international radiation oncology community. From previous research is clear that instructor-led guideline workshops are effective in reducing the inter-observer variation, however, it is unknown if and how the introduction the artificial intelligence based auto-segmentation modifies this causation.
Objectives
Investigating changes in inter-observer variation and bias after E-Learning in delineation guidelines and the use of deep learning-based auto-segmentation of organs-at-risk in head-and-neck cancer
Specific objectives
To train multidisciplinary teams to contribute to the goal of high-quality 3D radiotherapy
Impact
While there is a growing need to improve contouring skills for radiation oncologists worldwide, the task of contouring represents a time-consuming activity which affects an already often staff restricted profession due to the lack of sufficient human resources. The safe implementation of AI-assisted contouring tools is key and would result in resource sparing if applied appropriately. The study suggested that AI-assisted contouring is safe and beneficial to ROs working in LMICs. Prospective clinical trials on AI-assisted contouring should, however, be conducted upon clinical implementation to confirm the effects.
Relevance
AI-assisted contouring in combination with teaching of contouring guidelines is an effective strategy to reduce contouring time and conform contouring practices within and between radiotherapy departments located in LMIC.