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+TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease

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arXiv 2024年

作者： Li, Zuotian Liu, Xiang Tang, Ziyang Zhang, Pengyue Jin, Nanxin Eadon, Michael T. Song, Qianqian Chen, Yingjie Su, Jing Department of Biostatistics and Health Data Science Indiana University School of Medicine Indiana United States Department of Computer Graphics Technology Purdue University Indiana United States Department of Computer and Information Technology Purdue University Indiana United States Department of Medicine Indiana University School of Medicine Indiana United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Gerontology and Geriatric Medicine Wake Forest School of Medicine North Carolina United States

Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMR) for personalized precision management of chronic disease progression. Methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DEPOT is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of four panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases. © 2024, CC BY-NC-ND.

关键词： Diseases

Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data

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arXiv 2024年

作者： Heine, Lukas Hörst, Fabian Fragemann, Jana Luijten, Gijs Egger, Jan Bahnsen, Fin H. Sarfraz, M. Saquib Kleesiek, Jens Seibold, Constantin Institute for AI in Medicine University Hospital Essen Germany Mercedes-Benz Tech Innovation Germany Cancer Research Center Cologne Essen West German Cancer Center Essen University Hospital Essen Germany Institute of Computer Graphics and Vision Graz University of Technology Austria Department of Physics TU Dortmund University Germany

In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering patterns within large-scale corpora and understanding their semantic impact is critical, but depends on domain experts or resource-intensive manual reviews. In response, we introduce Spacewalker in this system demonstration paper, an interactive tool designed to analyze, explore, and annotate data across multiple modalities. It allows users to extract data representations, visualize them in low-dimensional spaces and traverse large datasets either exploratory or by querying regions of interest. We evaluated Spacewalker through extensive experiments and annotation studies, assessing its efficacy in improving data integrity verification and annotation. We show that Spacewalker reduces time and effort compared to traditional methods. The code of this work is available here. © 2024, CC BY.

关键词： Data integrity

Enabling and Assessing Trust when Cooperating with Robots in Disaster Response (EASIER)

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arXiv 2022年

作者： Frering, Laurent Eder, Matthias Kubicek, Bettina Albert, Dietrich Kalkofen, Denis Gschwandtner, Thomas Krajnz, Heimo Steinbauer-Wagner, Gerald The Institute of Software Technology Graz University of Technology Graz Austria The Institute of Psychology University of Graz Graz Austria The Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Rosenbauer International AG Linz Austria Professional Fire Brigade Graz Graz Austria

This paper presents a conceptual overview of the EASIER project and its scope. EASIER focuses on supporting emergency forces in disaster response scenarios with a semi-autonomous mobile manipulator. Specifically, we examine the operator's trust in the system and his/her cognitive load generated by its use. We plan to address different research topics, exploring how shared autonomy, interaction design, and transparency relate to trust and cognitive load. Another goal is to develop non-invasive methods to continuously measure trust and cognitive load in the context of disaster response using a multilevel approach. This project is conducted by multiple academic partners specializing in artificial intelligence, interaction design, and psychology, as well as an industrial partner for disaster response equipment and end-users for framing the project and the experiments in real use-cases. © 2022, CC BY.

关键词： Noninvasive medical procedures

Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective

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arXiv 2022年

作者： Hu, Qin Li, Feng Zou, Xukai Xiao, Yinhao Department of Computer and Information Science Indiana University-Purdue University IndianapolisIN United States Department of Computer Information and Graphics Technology Indiana University-Purdue University IndianapolisIN United States School of Information Science Guangdong University of Finance and Economics Guangzhou China

An emerging computational paradigm, named federated edge learning (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding whether to participate in FEL or not given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize mechanism design to enable truthful information solicitation. With the help of correlated equilibrium, we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

Modeling Pedestrian Situated Intent in Dynamic Driving Scenes from the Driver's Perspective

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SSRN

SSRN 2022年

作者： Zhang, Zhengming Tian, Renran Elahi, Fazle Md Luo, Xiao Domeyer, Josh Sherony, Rini School of Industrial Engineering Purdue University United States Department of Computer Information & Graphics Technology IUPUI United States Department of Electrical & Computer Engineering IUPUI United States Collaborative Safety Research Center Toyota Motor North America United States

One salient challenge for autonomous vehicles to perform the complete driving task is the difficulty of interacting with pedestrians in a social capacity. Pedestrian behavior studies typically model non-dynamic pedestrian intentions or surrogate measurements like activities and trajectories. However, pedestrian intention changes as the driving scene evolves, and earlier work has not covered this direct and dynamic view. This study proposes the concept of pedestrian situated intent to fill these research gaps and utilizes event-segmentation theories to study drivers’ scene processing behavior according to this new view of vehicle-pedestrian interactions. It investigates how drivers automatically encode and segment pedestrian encounters from continuous sensory input based on the dynamic changes of pedestrian intent. Video experiments of human drivers show that although pedestrian intent segmentation boundaries always exist for individual drivers, about 72% of individual intent segmentation boundaries are common across the group for about 70% of all the sampled cases. Existing common boundaries have a width of about 0.48 seconds and may cause increased or decreased consensus among drivers in estimated pedestrian intent. Further analysis shows the most important cues associated with these two types of event boundaries. Results from this study demonstrate how to directly measure the dynamic changes of pedestrian intent from the driver's perspective. The concept of temporal-dynamic pedestrian situated intent can encourage future studies to model vehicle-pedestrian interactions at the intention-exchanging level to support better vehicle behavior planning algorithm development. © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

Characteristics Analysis of Moving Conversations to Detect Events on Twitter

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Characteristics Analysis of Moving Conversations to Detect E...

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Hansi Senaratne Dominic Lehle Tobias Schreck Earth Observation Center German Aerospace Center Oberpfaffenhofen Germany Advanced Research & Technologies Avira Tettnang Germany Inst. of Computer Graphics & Knowledge Visualization Graz University of Technology Graz Austria

A conversation is an exchange of thoughts, news, or ideas about a particular topic between two or more people. On Twit-ter, hashtags allow its users to collate all conversations pertaining to a particular topic. The progressions that occur in such conversations through the geographic space, the time, or the thematic contexts, create trajectories of conversations on Twitter, and they can give us valuable insights into interesting events that take place around us. In this paper we develop an approach based on data analysis and visualisation, to (1) construct such conversation trajectories for chosen popular hashtags, (2) analyse the various geospatial- and content-characteristics of the conversation trajectories (e.g., distance variance, speed of propagation, topic diversity, or credibility) to determine co-located events, and (3) rank and sort the resulting conversation trajectories according to a user-defined interestingess-measure, to narrow down the search space for interesting conversation trajectories. Our approach is among the first to introduce the us-age of movement of conversations across geographic space and time for the exploratory detection and analysis of events, whereas most existing works use keyword-based text analysis to detect events on Twitter. All the three stages of the approach (construct, analyse, rank & sort) are presented in a visual-interactive interface that allows us to explore Twitter text data without extensive prior knowledge, and benefit from the pure exploratory capabilities of the tool. The usefulness of our approach is demonstrated as a proof-of-concept to detect sports-related events, where we were able to identify the outcome of a contest for Major League Baseball sportsmen on Twitter.

关键词： Knowledge engineering Visualization Text analysis Social networking (online) Blogs Linearity Oral communication

Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

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NeuroImage 2025年 314卷 121256-121256页

作者： Qin, Jianmei Wu, Haoting Wu, Chenqing Guo, Tao Zhou, Cheng Duanmu, Xiaojie Tan, Sijia Wen, Jiaqi Zheng, Qianshi Yuan, Weijin Zhu, Zihao Chen, Jingwen Wu, Jingjing He, Chenyu Ma, Yiran Liu, Chunlei Xu, Xiaojun Guan, Xiaojun Zhang, Minming Department of Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Joint Laboratory of Clinical Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory of Computer-aided Design & Computer Graphics Zhejiang University College of Computer Science and technology Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University College of Control Science and Engineering Hangzhou China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Helen Wills Neuroscience Institute University of California Berkeley CA United States

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), *** consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinso

关键词： Parkinson's disease Quantitative susceptibility mapping Resting-state functional connectivity Subcortical nuclei

Deep Reinforcement Learning for Localization of the Aortic Annulus in Patients with Aortic Dissection 2nd

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Deep Reinforcement Learning for Localization of the Aortic A...

2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and computer-Assisted Intervention Conference, MICCAI 2020

作者： Codari, Marina Pepe, Antonio Mistelbauer, Gabriel Mastrodicasa, Domenico Walters, Shannon Willemink, Martin J. Fleischmann, Dominik Department of Radiology Stanford University School of Medicine StanfordCA United States Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Department of Simulation and Graphics Otto-von-Guericke University Magdeburg Germany

ISBN: (纸本)9783030624682

Accurate localization of the aortic annulus is key to several imaging tasks, like cross-sectional aortic valve plane estimation, aortic root segmentation, and annulus diameter measurements. In this project, we propose an end-to-end trainable deep reinforcement learning (DRL) algorithm aimed at identification of the aortic annulus in patients with aortic dissection. We trained 5 different agents on a dataset of 75 CT scans from 66 patients following a sequential model-upgrading strategy. We evaluated the effect of performing different image preprocessing steps, adding batch normalization and regularization layers, and changing terminal state definition. At each step of this sequential process, the model performance has been evaluated on a validation sample composed of 24 CTA scans from 24 independent patients. Localization accuracy was defined as the Euclidean distance between estimated and target aortic annulus locations. Best model results show a median localization error equal to 2.98 mm with an interquartile range equal to [2.25, 3.81] mm, and a failure rate (i.e., percentage of samples with localization error in validation data. We proved the feasibility of DRL application for aortic annulus localization in CTA images of patients with aortic dissection, which are characterized by a large variability in aortic morphology and image quality. Nevertheless, further improvements are needed to reach expert-human level performance. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

Nuwa: An Authoring Tool for Graph Visualizations

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Nuwa: An Authoring Tool for Graph Visualizations

Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Dongming Han Haiyang Zhu Wei Chen Jiacheng Pan Ran Chen Xumeng Wang Zhen Wen Luoxuan Weng Minfeng Zhu Yingcai Wu Rüdiger Westermann State Key Lab of CAD&CG Zhejiang University China Hithink RoyalFlush Information Network Co. Ltd. China Wuchan Zhongda Digital Technology Co. Ltd. China Laboratory of Art and Archaeology Image (Zhejiang University) Ministry of Education China College of Computer Science Nankai University China Zhejiang University China Computer Graphics and Visualization Technical University of Munich Germany

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

关键词： Analytical models Authoring systems Costs Semantics Data visualization Prototypes Programming