1
|
Ng J, Arness D, Gronowski A, Qu Z, Lau CW, Catchpoole D, Nguyen QV. Exocentric and Egocentric Views for Biomedical Data Analytics in Virtual Environments-A Usability Study. J Imaging 2023; 10:3. [PMID: 38248988 PMCID: PMC10817309 DOI: 10.3390/jimaging10010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Biomedical datasets are usually large and complex, containing biological information about a disease. Computational analytics and the interactive visualisation of such data are essential decision-making tools for disease diagnosis and treatment. Oncology data models were observed in a virtual reality environment to analyse gene expression and clinical data from a cohort of cancer patients. The technology enables a new way to view information from the outside in (exocentric view) and the inside out (egocentric view), which is otherwise not possible on ordinary displays. This paper presents a usability study on the exocentric and egocentric views of biomedical data visualisation in virtual reality and their impact on usability on human behaviour and perception. Our study revealed that the performance time was faster in the exocentric view than in the egocentric view. The exocentric view also received higher ease-of-use scores than the egocentric view. However, the influence of usability on time performance was only evident in the egocentric view. The findings of this study could be used to guide future development and refinement of visualisation tools in virtual reality.
Collapse
Affiliation(s)
- Jing Ng
- School of Psychology, Western Sydney University, Penrith, NSW 2750, Australia; (J.N.); (D.A.); (A.G.)
| | - David Arness
- School of Psychology, Western Sydney University, Penrith, NSW 2750, Australia; (J.N.); (D.A.); (A.G.)
| | - Ashlee Gronowski
- School of Psychology, Western Sydney University, Penrith, NSW 2750, Australia; (J.N.); (D.A.); (A.G.)
| | - Zhonglin Qu
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW 2751, Australia; (Z.Q.); (C.W.L.)
| | - Chng Wei Lau
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW 2751, Australia; (Z.Q.); (C.W.L.)
| | - Daniel Catchpoole
- Tumour Bank, Children’s Cancer Research Unit, Kids Research, The Children’s Hospital at Westmead, Westmead, NSW 2145, Australia;
- School of Computer Science, Faculty of Engineering and IT, The University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Quang Vinh Nguyen
- School of Computer, Data and Mathematical Sciences and MARCS Institute, Western Sydney University, Penrith, NSW 2751, Australia
| |
Collapse
|
2
|
Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts. Sci Rep 2022; 12:11337. [PMID: 35790803 PMCID: PMC9256599 DOI: 10.1038/s41598-022-15548-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/24/2022] [Indexed: 11/08/2022] Open
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.
Collapse
|
3
|
Belciug S. Pathologist at work. Artif Intell Cancer 2020. [DOI: 10.1016/b978-0-12-820201-2.00003-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
|
4
|
Qu Z, Lau CW, Nguyen QV, Zhou Y, Catchpoole DR. Visual Analytics of Genomic and Cancer Data: A Systematic Review. Cancer Inform 2019; 18:1176935119835546. [PMID: 30890859 PMCID: PMC6416684 DOI: 10.1177/1176935119835546] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 01/29/2019] [Indexed: 12/12/2022] Open
Abstract
Visual analytics and visualisation can leverage the human perceptual system to
interpret and uncover hidden patterns in big data. The advent of next-generation
sequencing technologies has allowed the rapid production of massive amounts of
genomic data and created a corresponding need for new tools and methods for
visualising and interpreting these data. Visualising genomic data requires not
only simply plotting of data but should also offer a decision or a choice about
what the message should be conveyed in the particular plot; which methodologies
should be used to represent the results must provide an easy, clear, and
accurate way to the clinicians, experts, or researchers to interact with the
data. Genomic data visual analytics is rapidly evolving in parallel with
advances in high-throughput technologies such as artificial intelligence (AI)
and virtual reality (VR). Personalised medicine requires new genomic
visualisation tools, which can efficiently extract knowledge from the genomic
data and speed up expert decisions about the best treatment of individual
patient’s needs. However, meaningful visual analytics of such large genomic data
remains a serious challenge. This article provides a comprehensive systematic
review and discussion on the tools, methods, and trends for visual analytics of
cancer-related genomic data. We reviewed methods for genomic data visualisation
including traditional approaches such as scatter plots, heatmaps, coordinates,
and networks, as well as emerging technologies using AI and VR. We also
demonstrate the development of genomic data visualisation tools over time and
analyse the evolution of visualising genomic data.
Collapse
Affiliation(s)
- Zhonglin Qu
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Chng Wei Lau
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Quang Vinh Nguyen
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia.,The MARCS Institute, Western Sydney University, Penrith, NSW, Australia
| | - Yi Zhou
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Daniel R Catchpoole
- The Tumour Bank, Children's Cancer Research Unit, Kids Research, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Discipline of Paediatrics and Child Health, Faculty of Medicine, The University of Sydney, Sydney, NSW, Australia.,Faculty of Information Technology, The University of Technology Sydney, Ultimo, NSW, Australia
| |
Collapse
|