A Collaborative Digital Pathology System for Multi‐Touch Mobile and Desktop Computing Platforms

Jeong, Won-Ki; Schneider, Jens; Hansen, Anne; Lee, M.; Turney, Stephen G.; Faulkner-Jones, Beverly E.; Hecht, Jonathon L.; Najarían, Robert M.; Yee, Eric U.; Lichtman, Jeff W.; Pfister, Hanspeter

doi:10.1111/cgf.12137

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…SmartPhones represent the new frontier in telemedicine. There have been a number of studies on the use of mobile devices and phones for teleradiology, telepathology and other image-based specialties [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], but these are not especially relevant to RT teleconsultations between patients and healthcare providers using these types of devices (this does not include the use of mobile apps as this does not really involve consulting or the use of cameras in a clinical encounter). There need to be more studies done to validate the reliability and validity of using mobile devices for teleconsulting applications and perhaps guidelines established for safe and effective use.…”

Section: Non-clinic-based Teleconsultationsmentioning

confidence: 99%

Telemedicine Workplace Environments: Designing for Success

Krupiński

2014

Healthcare

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When designing a facility for telemedicine, there are several things to consider from a human factors point of view, as well as from a practicality point of view. Although the future practice of telemedicine is likely to be more of a mobile-based practice and centered more in the home than it is now, it is still very important to consider ways to optimize the design of clinic-based telemedicine facilities. This is true on both ends of a consultation—where the patient is and where the consultant is. On the patient side, the first thing to realize is that most telemedicine clinics are not going to be newly designed and built. In all likelihood they will be existing rooms converted to telemedicine clinic rooms. Quite often the former room will not even have been used for clinical purposes, but may have simply been a storage area cleared out for telemedicine use. Therefore, design is often a challenge but there are a few basic principles that can be followed to create a workable clinical space. This paper will review some of the basic human factors principles to take into account when designing a working telemedicine environment.

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Section: Non-clinic-based Teleconsultationsmentioning

confidence: 99%

Telemedicine Workplace Environments: Designing for Success

Krupiński

2014

Healthcare

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“…These contain specific tool configurations, view positions, or zoom levels, allowing users to work in a “you see what I see” manner. Synchronous collaboration mode where the content is shared and dynamically synchronized between multiple users is supported less often [RL21; MRS*16; JSH*13; PZD*19]. Some tools also support storytelling by storing annotated view positions with text descriptions [HRM*20; Med; JKW*22].…”

Section: Related Workmentioning

confidence: 99%

xOpat: eXplainable Open Pathology Analysis Tool

Horák,

Furmanová,

Kozlíková

et al. 2023

Computer Graphics Forum

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Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open‐source, browser‐based WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists' annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol‐agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.

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“…Complimentary research explores visualization techniques Chapter 5. Tangible Tensors for 3D tensors [Jeong et al, 2013]. Properly implemented 3D tensor visualization results in models that are truly depictive of that the data that they represent.…”

Section: Related Workmentioning

confidence: 99%

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Manshaei¹

2021

Preprint

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Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

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A Collaborative Digital Pathology System for Multi‐Touch Mobile and Desktop Computing Platforms

Cited by 9 publications

References 22 publications

Telemedicine Workplace Environments: Designing for Success

Telemedicine Workplace Environments: Designing for Success

xOpat: eXplainable Open Pathology Analysis Tool

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

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