Consensus on the use of artificial intelligence in the management and measurement of vestibular schwannomas: A...

The assessment of vestibular schwannomas (VS) requires a standardized approach as growth is a key element in defining treatment strategy. Volumetric measurements offer higher sensitivity and precision, but existing methods of image segmentation, are labour-intensive and prone to variability. Artificial intelligence (AI) frameworks to segment VS using magnetic resonance imaging (MRI) achieving state-of-the-art capability can fully automate the detection and segmentation of VS. These tools can be used for automating the extraction process of various linear and volumetric measurements. A consistent approach to recording data, facilitated by AI, will allow the accumulation and comparison of evidence to identify the most effective treatments for patients with VS.

This protocol aims to develop a Delphi consensus for the assessment of VS and deployment of AI-based image analysis as a tool for use in VS management.

A three-phase consensus study will be undertaken; Phase 1: systematic review (PROSPERO registration number CRD42024604452) of trials and observational studies reporting the measurement of VS to identify a list of candidate indicators; Phase 2: refinement of this list and development of a set of questionnaire questions performed by our local steering committee; and Phase 3: a two-round Delphi questionnaire and consensus meeting with expert stakeholders from the British Skull Base Society (BSBS), European Skull Base Society (ESBS) and European Society of Head and Neck Radiology (ESHNR).

Participants will be recruited through professional bodies. The core reporting set will be disseminated through peer-reviewed publication, co-production with journal editors, research funders and professional bodies, and presentation at national conferences.

Not applicable.