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Department of Medical Imaging and Radiation Sciences using AI for radiotherapy education

​The University of Johannesburg has joined the VERT family. The addition of a Virtual Environment for Radiotherapy Training (VERT) system at the Department of Medical Imaging and Radiation Sciences, has enabled the department to join a powerful community of educators and clinical staff, sharing one mission – to enhance best practice. VERT aims to help innovate and improve the education of radiotherapy students across the world.

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The VERT is an Artificial Intelligence software tool. VERT provides a realistic virtual environment of a radiotherapy treatment room, in the form of a Linear Accelerator (Linac), with accessories and a virtual patient. VERT has been described as the “flight Simulator” of the Linac. A life-sized model of a linear accelerator is projected onto a high-resolution screen. The user is then able to walk around the “Linac room” wearing 3D glasses that allow them to be immersed in a 3D image. A hand pendant then enables the student to control the software.

The VERT system is a virtual environment of a radiotherapy treatment room, just the like real thing but in a safe and non-pressured environment.This AI software has been specifically designed as an educational and skills training tool that generates a virtual clinical environment to augment teaching and learning strategies in radiation therapy that will help as a tool. It is fully interactive and helps to bridge the gap between theory and practice.

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This system will assist mainly in the learning of complex concepts such treatment planning and dosimetry where students need to understand the interaction of ionizing radiation in matter (the patient’s body) and how the radiation beam is positioned in relation to the internal anatomy of the body. Learning will be more effective and enjoyable for students using the VERT.

Furthermore, it allows the students to gain competency in 21st century clinical applications not available in the workplace. The workplace based learning practices affiliated to the University of Johannesburg are inadequately equipped with the variety of linear accelerators required for effective teaching and learning of these concepts. This AI tool will also allow students to critically evaluate, practice and explore radiotherapy treatment planning and delivery without placing training demands on the clinical environment and placing danger to the patient.

Students can gain confidence in the classroom to make clinically based decisions with scenario-based learning and enhance this skillset early within their training. They can also practice quality assurance procedures required for daily clinical practice and underpin the key principles of radiation therapy and dosimetry.


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