EZ-AR: A universal AR application for multi-model visualization and real-time tracking in healthcare settings.

Purpose: Augmented Reality (AR) offers significant potential to enhance surgical precision, improve clinical training, and support intraoperative decision-making. However, developing personalized AR applications for head-mounted displays such as Microsoft HoloLens 2 typically requires advanced technical expertise. Additionally, identifying a robust and accessible method for registering virtual models to the real world remains an open challenge. These limitations hinder the widespread adoption of AR in healthcare, particularly among non-technical clinical users. Methods: This work presents EZ-AR, a user-friendly AR application for Microsoft HoloLens 2 that automates complex tasks such as configuration and deployment, while ensuring low latency, high reliability, and smooth interaction. Ultimately, the system enables non-expert users to independently load and visualize personalized 3D models anchored to physical reference markers. The application accesses files from Google Drive in seconds and displays them in the physical environment using one of two integrated tracking methods: Vuforia or QR code detection. Once configured, the application no longer requires Internet access. Results: The system's usability was assessed in terms of detection range and AR projection accuracy under varying surface inclinations and viewing angles. Results show a mean projection error of 3.3 ± 2.2 mm with QR code tracking and 3.9 ± 2.7 mm with Vuforia, both measured at a working distance compatible with typical surgical ergonomics. Further analysis revealed that accuracy is more influenced by viewing angle and surface inclination than by the tracking method itself. Conclusion: Overall, EZ-AR provides a robust and accessible AR framework to democratize AR adoption in clinical environments. The files presented in this repository contain the supplementary data that complements the article: Alicia Pose-Díez-de-la-Lastra, Gemma Arce-Alonso, José-Antonio Calvo-Haro, Rubén Pérez-Mañanes, Javier Pascau. EZ-AR: A universal AR application for multi-model visualization and real-time tracking in healthcare settings. 2026. Should you have any questions, please do not hesitate to contact us at [email protected].