Adaptive Ventilation in Hospital: Visualization, User Interaction and Real-Time Control

This presentation introduces an AI- and XR-based framework for real-time visualization and control of airflow in operating rooms. Developed within the HumanIC project, the approach integrates computational fluid dynamics (CFD) simulations with immersive virtual reality environments to improve awareness of airborne contamination and support clinical decision-making. The work addresses key limitations of current ventilation monitoring systems, which rely on retrospective analysis and lack real-time insight into infection risks. A novel pipeline combining 3D Gaussian Splatting, CFD data processing, and Unreal Engine-based visualization enables the creation of photorealistic digital twins of operating rooms. The system allows intuitive understanding of airflow behavior even for non-experts and supports future development toward real-time AI-driven adaptive control of ventilation systems. The solution contributes to reducing healthcare-associated infections (HAIs) and improving safety in surgical environments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-17
DOI
https://doi.org/10.5281/zenodo.19625255
Primary Topic
Infection Control and Ventilation
Type
article
Field-Weighted Citation Impact
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article

Adaptive Ventilation in Hospital: Visualization, User Interaction and Real-Time Control

Rahmat Rizal Andhi
Zenodo (CERN European Organization for Nuclear Research)
Infection Control and Ventilation
article

Adaptive Ventilation in Hospital: Visualization, User Interaction and Real-Time Control

Rahmat Rizal Andhi
article en

Abstract

This presentation introduces an AI- and XR-based framework for real-time visualization and control of airflow in operating rooms. Developed within the HumanIC project, the approach integrates computational fluid dynamics (CFD) simulations with immersive virtual reality environments to improve awareness of airborne contamination and support clinical decision-making. The work addresses key limitations of current ventilation monitoring systems, which rely on retrospective analysis and lack real-time insight into infection risks. A novel pipeline combining 3D Gaussian Splatting, CFD data processing, and Unreal Engine-based visualization enables the creation of photorealistic digital twins of operating rooms. The system allows intuitive understanding of airflow behavior even for non-experts and supports future development toward real-time AI-driven adaptive control of ventilation systems. The solution contributes to reducing healthcare-associated infections (HAIs) and improving safety in surgical environments.

Zenodo (CERN European Organization for Nuclear Research)
Norwegian University of Science and Technology (NO), St Olav's University Hospital (NO)
Openalex Percentile: Top 11%
Infection Control and Ventilation
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Adaptive Ventilation in Hospital: Visualization, User Interaction and Real-Time Control — Rahmat Rizal Andhi · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS