Presentation (CTI)

OBJECTIVES: 1View is an artificial intelligence and augmented reality-based application that generates three-dimensional (3D) geometric models from preoperative imaging to support interventional neuroradiologists during mechanical thrombectomy (MT) procedures. These models can be aligned to live, intra-operative two-dimensional (2D) imaging, which is presented to the operator by means of an additional graphical display. By providing enhanced 3D anatomical guidance, 1View aims to support more accurate navigation, shorten procedure duration and time under anaesthetic, and potentially reduce radiation exposure for both patients and the interventional team. The objective of this study was to evaluate the potential cost-consequences of implementing 1View across pre-operative planning and intra-operative guidance compared to standard MT practice, where clinicians rely primarily on 2D imaging.
METHODS: An early-stage economic evaluation was conducted using a hypothetical cost-consequence analysis framework from an NHS perspective. Model inputs were derived from published literature and supplemented with expert-informed estimates. The analysis compared the current MT pathway with a 1View-supported pathway, capturing potential differences in healthcare resource use and procedural effectiveness and efficiency. Costs were sourced from NHS reference data. Scenario-based sensitivity analyses were undertaken to explore uncertainty in key parameters.
RESULTS: The analysis suggests that the use of 1View may be associated with improvements in pre-procedural preparation and procedural efficiency, which were identified as key drivers of potential cost savings. The scenario analysis highlighted the potential for further impacts associated with the technology and showed that the findings were sensitive to assumptions regarding pathway changes and resource use.
CONCLUSIONS: This early economic evaluation indicates that 1View has the potential to improve efficiency within the MT pathway and generate cost savings for the NHS. However, these findings are based on a hypothetical model and require validation through real-world data. Further research is needed to assess the cost-effectiveness and long-term impact of the technology.