Rapid Progress in Intelligent Radiotherapy and Future Implementation

Digital technologies to unlock safe and sustainable opportunities for medical device and healthcare sectors with a focus on the combined use of digital twin and extended reality applications: A review

Medical devices have increased in complexity where there is a pressing need to consider design thinking and specialist training for manufacturers, healthcare and sterilization providers, and regulators. Appropriately addressing this consideration will positively inform end-to-end supply chain and logistics, production, processing, sterilization, safety, regulation, education, sustainability and circularity. There are significant opportunities to innovate and to develop appropriate digital tools to help unlock efficiencies in these important areas. This constitutes the first paper to create an awareness of and to define different digital technologies for informing and enabling medical device production from a holistic end-to-end life cycle perspective. It describes the added-value of using digital innovations to meet emerging opportunities for many disposable and reusable medical devices. It addresses the value of accessing and using integrated multi-actor HUBs that combine academia, industry, healthcare, regulators and society to help meet these opportunities. Such as cost-effective access to specialist pilot facilities and expertise that converges digital innovation, material science, biocompatibility, sterility assurance, business model and sustainability. It highlights the marked gap in academic R&D activities (PRISMA review of best publications conducted between January 2010 and January 2024) and the actual list of U.S. FDA's approved and marketed artificial intelligence/machine learning (AI/ML), and augmented reality/virtual reality (AR/VR) enabled-medical devices for different healthcare applications. Bespoke examples of benefits underlying future use of digital tools includes potential implementation of machine learning for supporting and enabling parametric release of sterilized products through efficient monitoring of critical process data (complying with ISO 11135:2014) that would benefit stakeholders. This paper also focuses on the transformative potential of combining digital twin with extended reality innovations to inform efficiencies in medical device design thinking, supply chain and training to inform patient safety, circularity and sustainability.

Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks

Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment with scope to adjust dosing for optimal target and non-target tissue doses is consistent with striving for improved the outcomes of precision medicine. The combination of artificial intelligence and production of digital twins could provide an avenue for an individualised therapy doses and enhanced outcomes in theranostics. While there are barriers to overcome, the maturity of individual technologies (i.e. radiation dosimetry, artificial intelligence, theranostics and digital twins) places these approaches within reach.

Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer

In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relatively large, curated radiotherapy datasets which are highly reflective of the contemporary standard of care. However, little has been previously published describing technical infrastructure, recommendations, methods or standards for radiotherapy dataset curation in a holistic fashion. Our radiation oncology department has recently embarked on a large-scale project in partnership with an external partner to develop deep-learning-based tools to assist with our radiotherapy workflow, beginning with autosegmentation of organs-at-risk. This project will require thousands of carefully curated radiotherapy datasets comprising all body sites we routinely treat with radiotherapy. Given such a large project scope, we have approached the need for dataset curation rigorously, with an aim towards building infrastructure that is compatible with efficiency, automation and scalability. Focusing on our first use-case pertaining to head and neck cancer, we describe our developed infrastructure and novel methods applied to radiotherapy dataset curation, inclusive of personnel and workflow organization, dataset selection, expert organ-at-risk segmentation, quality assurance, patient de-identification, data archival and transfer. Over the course of approximately 13 months, our expert multidisciplinary team generated 490 curated head and neck radiotherapy datasets. This task required approximately 6000 human-expert hours in total (not including planning and infrastructure development time). This infrastructure continues to evolve and will support ongoing and future project efforts.