Dataset Information

Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.

ABSTRACT: The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.

SUBMITTER: Lau CW

PROVIDER: S-EPMC9256599 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.

Lau Chng Wei CW Qu Zhonglin Z Draper Daniel D Quan Rosa R Braytee Ali A Bluff Andrew A Zhang Dongmo D Johnston Andrew A Kennedy Paul J PJ Simoff Simeon S Nguyen Quang Vinh QV Catchpoole Daniel D

Scientific reports 20220705 1

The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic p ...[more]

PMID: 35790803

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Project description:Spatial navigation is influenced by landmarks, which are prominent visual features in the environment. Although previous research has focused on finding advantages of landmarks on wayfinding via experimentation; however, less attention has been given to identifying the key attributes of landmarks that facilitate wayfinding, including the study of neural correlates (involving electroencephalogram, EEG analyses). In this paper, we combine behavioral measures, virtual environment, and EEG signal-processing to provide a holistic investigation about the influence of landmarks on performance during navigation in a maze-like environment. In an experiment, participants were randomly divided into two conditions, Landmark-enriched (LM+; N = 17) and Landmark-devoid (LM-; N = 18), and asked to navigate from an initial location to a goal location in a maze. In the LM+ condition, there were landmarks placed at certain locations, which participants could use for wayfinding in the maze. However, in the LM- condition, such landmarks were not present. Beyond behavioral analyses of data, analyses were carried out of the EEG data collected using a 64-channel device. Results revealed that participants took less time and committed fewer errors in navigating the maze in the LM+ condition compared to the LM- condition. EEG analyses of the data revealed that the left-hemispheric activation was more prominent in the LM+ condition compared to the LM- condition. The event-related desynchronization/synchronization (ERD/ERS) of the theta frequency band, revealed activation in the left posterior inferior and superior regions in the LM+ condition compared to the LM- condition, suggesting an occurrence of an object-location binding in the LM+ condition along with spatial transformation between representations. Moreover, directed transfer function method, which measures information flow between two regions, showed a higher number of active channels in the LM- condition compared to the LM+ condition, exhibiting additional wiring cost associated with the cognitive demands when no landmark was available. These findings reveal pivotal role of the left-hemispheric region (especially, parietal cortex), which indicates the integration of available sensory cues and current memory requirements to encode contextual information of landmarks. Overall, this research helps to understand the role of brain regions and processes that are utilized when people use landmarks in navigating maze-like environments.

Dataset Information

Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.

Publications

Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.

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