Matching Items (17)
Description
Accurate quantitative information of tumor/lesion volume plays a critical role
in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
ContributorsXue, Wenzhe (Author) / Kaufman, David (Thesis advisor) / Mitchell, J. Ross (Thesis advisor) / Johnson, William (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
Description
Integrating behavioral and physical health is the key to value-based care. Little is known about data sharing preferences and consent practices for individuals with behavioral health conditions. This study focuses on identifying behavioral health provider perceptions about patient data sharing practices, preferences and perceived impact on care resulting from enhanced patient control of record types during consent for data sharing.
ContributorsHiestand, Megan (Author) / Grando, Adela (Thesis director) / Murcko, Anita (Committee member) / Sharp, Richard (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Prescription opioid abuse has become a serious national problem. To respond to the opioid epidemic, states have created prescription drug monitoring programs (PDMPs) to monitor and reduce opioids use. We conducted a systematic literature review to better understand metrics used to quantify the effect that PDMPs have had on reducing opioid abuse, and solutions and challenges related to the integration of PDMPs with EHRs. Lessons learned can help guide federal and state-based efforts to better respond to the current opioid crisis.
ContributorsPonnapalli, Aditya Somayajulu (Author) / Murcko, Anita (Thesis director) / Grando, Adela (Committee member) / Wertheim, Pete (Committee member) / Biomedical Informatics Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The proliferation of interconnected and networked medical devices has resulted in the development of innovative Medical Cyber-Physical Systems (MCPS). MCPS are life-critical, distributed systems that are utilized to monitor and control healthcare organizations in order to provide a more coordinated, cohesive care-continuum focused on the whole patient resulting in better outcomes, and a happier, healthier patient. Medical Cyber Physical (MCPS) systems are life-critical, networked systems used to monitor and control healthcare and medical devices in order to provide more coordinated and cohesive care for the patient. Cyber-securing MCPS is difficult due to their complex and interconnected nature, and this project sets about analyzing current security requirements for MCPS using an ontology and exploration techniques, and developing a risk assessment and monitoring framework to better secure such systems.
ContributorsLamp, Josephine Ann (Author) / Ahn, Gail-Joon (Thesis director) / Rubio-Medrano, Carlos (Committee member) / School of Film, Dance and Theatre (Contributor) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Title: A Mobile Health Application for Tracking Patients' Health Record Abstract Background: Mobile Health (mHealth) has recently been adopted and used in rural communities in developing countries to improve the quality of healthcare in those areas. Some organizations use mHealth application to track pregnancy and provide routine checkups for pregnant women. Other organizations use mHelath application to provide treatment and counseling services to HIV/AIDs patients, and others are using it to provide treatment and other health care services to the general populations in rural communities. One organization that is using mobile health to bring primary care for the first time in some of the rural communities of Liberia is Last Mile Health. Since 2015, the organization has trained community health assistants (CHAs) to use a mobile health platform called Data Collection Tools (DCTs). The CHAs use the DCT to collect health data, diagnose and treat patients, provide counseling and educational services to their communities, and for referring patients for further care. While it is true that the DCT has many great features, it currently has many limitations such as data storage, data processing, and many others. Objectives: The goals of this study was to 1. Explore some of the mobile health initiatives in developing countries and outline some of the important features of those initiatives. 2. Design a mobile health application (a new version of the Last Mile Health's DCT) that incorporates some of those features that were outlined in objective 1. Method: A comprehensive literature search in PubMed and Arizona State University (ASU) Library databases was conducted to retrieve publications between 2014 and 2017 that contained phrases like "mHealth design", "mHealth implementation" or "mHealth validation". For a publication to refer to mHealth, the publication had to contain the term "mHealth," or contains both the term "health" and one of the following terms: mobile phone, cellular phone, mobile device, text message device, mobile technology, mobile telemedicine, mobile monitoring device, interactive voice response device, or disease management device. Results: The search yielded a total of 1407 publications. Of those, 11 publications met the inclusion criteria and were therefore included in the study. All of the features described in the selected articles were important to the Last Mile Health, but due to issues such as internet accessibility and cellular coverage, only five of those features were selected to be incorporated in the new version of the Last Mile's mobile health system. Using a software called Configure.it, the new version of the Last Mile's mobile health system was built. This new system incorporated features such as user logs, QR code, reminder, simple API, and other features that were identified in the study. The new system also helps to address problems such as data storage and processing that are currently faced by the Last Mile Health organization.
ContributorsKarway, George K. (Author) / Scotch, Matthew (Thesis director) / Kaufman, David (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Phylogenetic analyses that were conducted in the past didn't have the ability or functionality to inform and implement useful public health decisions while using clustering. Models can be constructed to conduct any further analyses for the result of meaningful data to be used in the future of public health informatics. A phylogenetic tree is considered one of the best ways for researchers to visualize and analyze the evolutionary history of a certain virus. The focus of this study was to research HIV phylodynamic and phylogenetic methods. This involved identifying the fast growing HIV transmission clusters and rates for certain risk groups in the US. In order to achieve these results an HIV database was required to retrieve real-time data for implementation, alignment software for multiple sequence alignment, Bayesian analysis software for the development and manipulation of models, and graphical tools for visualizing the output from the models created. This study began by conducting a literature review on HIV phylogeographies and phylodynamics. Sequence data was then obtained from a sequence database to be run in a multiple alignment software. The sequence that was obtained was unaligned which is why the alignment was required. Once the alignment was performed, the same file was loaded into a Bayesian analysis software for model creation of a phylogenetic tree. When the model was created, the tree was edited in a tree visualization software for the user to easily interpret. From this study the output of the tree resulted the way it did, due to a distant homology or the mixing of certain parameters. For a further continuation of this study, it would be interesting to use the same aligned sequence and use different model parameter selections for the initial creation of the model to see how the output changes. This is because one small change for the model parameter could greatly affect the output of the phylogenetic tree.
ContributorsNandan, Meghana (Author) / Scotch, Matthew (Thesis director) / Liu, Li (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Background: Consumer eHealth tools play an increasingly important role in engaging patients as participants in managing their health and seeking health information. However, there is a documented gap between the skill and knowledge demands of eHealth systems and user competencies to benefit from these tools.
Objective: This research aims to reveal the knowledge- and skill-related barriers to effective use of eHealth tools. Methods: We used a micro-analytic framework for characterizing the different cognitive dimensions of eHealth literacy to classify task demands and barriers that 20 participants experienced while performing online information-seeking and decision-making tasks.
Results: Participants ranged widely in their task performance across all 6 tasks as measured by task scores and types of barriers encountered. The highest performing participant experienced only 14 barriers whereas the lowest scoring one experienced 153. A more detailed analysis of two tasks revealed that the highest number of incorrect answers and experienced barriers were caused by tasks requiring: (a) Media literacy and Science literacy at high cognitive complexity levels and (b) a combination of Numeracy and Information literacy at different cognitive complexity levels.
Conclusions: Applying this type of analysis enabled us to characterize task demands by literacy type and by cognitive complexity. Mapping barriers to literacy types provided insight into the interaction between users and eHealth tasks. Although the gap between eHealth tools, users’ skills, and knowledge can be difficult to bridge, an understanding of the cognitive complexity and literacy demands can serve to reduce the gap between designer and consumer.
ContributorsChan, Connie V. (Author) / Mirkovic, Jelena (Author) / Furniss, Stephanie (Author) / Kaufman, David (Author) / College of Health Solutions (Contributor)
Created2015-12