Matching Items (47)
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
The apolipoprotein E (APOE) e4 genotype is the most prevalent known genetic risk factor for Alzheimer's disease (AD). In this paper, we examined the longitudinal effect of APOE e4 on hippocampal morphometry in Alzheimer's Disease Neuroimaging Initiative (ADNI). Generally, atrophy of hippocampus has more chance occurs in AD patients who carrying the APOE e4 allele than those who are APOE e4 noncarriers. Also, brain structure and function depend on APOE genotype not just for Alzheimer's disease patients but also in health elderly individuals, so APOE genotyping is considered critical in clinical trials of Alzheimer's disease. We used a large sample of elderly participants, with the help of a new automated surface registration system based on surface conformal parameterization with holomorphic 1-forms and surface fluid registration. In this system, we automatically segmented and constructed hippocampal surfaces from MR images at many different time points, such as 6 months, 1- and 2-year follow up. Between the two different hippocampal surfaces, we did the high-order correspondences, using a novel inverse consistent surface fluid registration method. At each time point, using Hotelling's T^2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the non-demented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes.
ContributorsLi, Bolun (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2015
Description
Detecting anatomical structures, such as the carina, the pulmonary trunk and the aortic arch, is an important step in designing a CAD system of detection Pulmonary Embolism. The presented CAD system gets rid of the high-level prior defined knowledge to become a system which can easily extend to detect other anatomic structures. The system is based on a machine learning algorithm --- AdaBoost and a general feature --- Haar. This study emphasizes on off-line and on-line AdaBoost learning. And in on-line AdaBoost, the thesis further deals with extremely imbalanced condition. The thesis first reviews several knowledge-based detection methods, which are relied on human being's understanding of the relationship between anatomic structures. Then the thesis introduces a classic off-line AdaBoost learning. The thesis applies different cascading scheme, namely multi-exit cascading scheme. The comparison between the two methods will be provided and discussed. Both of the off-line AdaBoost methods have problems in memory usage and time consuming. Off-line AdaBoost methods need to store all the training samples and the dataset need to be set before training. The dataset cannot be enlarged dynamically. Different training dataset requires retraining the whole process. The retraining is very time consuming and even not realistic. To deal with the shortcomings of off-line learning, the study exploited on-line AdaBoost learning approach. The thesis proposed a novel pool based on-line method with Kalman filters and histogram to better represent the distribution of the samples' weight. Analysis of the performance, the stability and the computational complexity will be provided in the thesis. Furthermore, the original on-line AdaBoost performs badly in imbalanced conditions, which occur frequently in medical image processing. In image dataset, positive samples are limited and negative samples are countless. A novel Self-Adaptive Asymmetric On-line Boosting method is presented. The method utilized a new asymmetric loss criterion with self-adaptability according to the ratio of exposed positive and negative samples and it has an advanced rule to update sample's importance weight taking account of both classification result and sample's label. Compared to traditional on-line AdaBoost Learning method, the new method can achieve far more accuracy in imbalanced conditions.
ContributorsWu, Hong (Author) / Liang, Jianming (Thesis advisor) / Farin, Gerald (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well.
ContributorsVankipuram, Mithra (Author) / Greenes, Robert A (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Petitti, Diana B. (Committee member) / Dinu, Valentin (Committee member) / Smith, Marshall L. (Committee member) / Arizona State University (Publisher)
Created2012
Description
The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation.
ContributorsSubbiah, Naveen Kumar (Author) / Patel, Vimla L. (Thesis advisor) / Hsiao, Sharon (Thesis advisor) / Sen, Ayan (Committee member) / Atkinson, Robert K (Committee member) / Arizona State University (Publisher)
Created2018
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
The analysis of clinical workflow offers many challenges to clinical stakeholders and researchers, especially in environments characterized by dynamic and concurrent processes. Workflow analysis in such environments is essential for monitoring performance and finding bottlenecks and sources of error. Clinical workflow analysis has been enhanced with the inclusion of modern technologies. One such intervention is automated location tracking which is a system that detects the movement of clinicians and equipment. Utilizing the data produced from automated location tracking technologies can lead to the development of novel workflow analytics that can be used to complement more traditional approaches such as ethnography and grounded-theory based qualitative methods. The goals of this research are to: (i) develop a series of analytic techniques to derive deeper workflow-related insight in an emergency department setting, (ii) overlay data from disparate sources (quantitative and qualitative) to develop strategies that facilitate workflow redesign, and (iii) incorporate visual analytics methods to improve the targeted visual feedback received by providers based on the findings. The overarching purpose is to create a framework to demonstrate the utility of automated location tracking data used in conjunction with clinical data like EHR logs and its vital role in the future of clinical workflow analysis/analytics. This document is categorized based on two primary aims of the research. The first aim deals with the use of automated location tracking data to develop a novel methodological/exploratory framework for clinical workflow. The second aim is to overlay the quantitative data generated from the previous aim on data from qualitative observation and shadowing studies (mixed methods) to develop a deeper view of clinical workflow that can be used to facilitate workflow redesign. The final sections of the document speculate on the direction of this work where the potential of this research in the creation of fully integrated clinical environments i.e. environments with state-of-the-art location tracking and other data collection mechanisms, is discussed. The main purpose of this research is to demonstrate ways by which clinical processes can be continuously monitored allowing for proactive adaptations in the face of technological and process changes to minimize any negative impact on the quality of patient care and provider satisfaction.
ContributorsVankipuram, Akshay (Author) / Patel, Vimla L. (Thesis advisor) / Wang, Dongwen (Thesis advisor) / Shortliffe, Edward H (Committee member) / Kaufman, David R. (Committee member) / Traub, Stephen J (Committee member) / Arizona State University (Publisher)
Created2018
Description
Measurements of different molecular species from single cells have the potential to reveal cell-to-cell variations, which are precluded by population-based measurements. An increasing percentage of researches have been focused on proteins, for its central roles in biological processes. Immunofluorescence (IF) has been a well-established protein analysis platform. To gain comprehensive insights into cell biology and diagnostic pathology, a crucial direction would be to increase the multiplexity of current single cell protein analysis technologies.
An azide-based chemical cleavable linker has been introduced to design and synthesis novel fluorescent probes. These probes allow cyclic immunofluorescence staining which leads to the feasibility of highly multiplexed single cell in situ protein profiling. These highly multiplexed imaging-based platforms have the potential to quantify more than 100 protein targets in cultured cells and more than 50 protein targets in single cells in tissues.
This approach has been successfully applied in formalin-fixed paraffin-embedded (FFPE) brain tissues. Multiplexed protein expression level results reveal neuronal heterogeneity in the human hippocampus.
An azide-based chemical cleavable linker has been introduced to design and synthesis novel fluorescent probes. These probes allow cyclic immunofluorescence staining which leads to the feasibility of highly multiplexed single cell in situ protein profiling. These highly multiplexed imaging-based platforms have the potential to quantify more than 100 protein targets in cultured cells and more than 50 protein targets in single cells in tissues.
This approach has been successfully applied in formalin-fixed paraffin-embedded (FFPE) brain tissues. Multiplexed protein expression level results reveal neuronal heterogeneity in the human hippocampus.
ContributorsLiao, Renjie (Author) / Guo, Jia (Thesis advisor) / Borges, Chad (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Single-cell proteomics and transcriptomics analysis are crucial to gain insights of
healthy physiology and disease pathogenesis. The comprehensive profiling of biomolecules in individual cells of a heterogeneous system can provide deep insights into many important biological questions, such as the distinct cellular compositions or regulation of inter- and intracellular signaling pathways of healthy and diseased tissues. With multidimensional molecular imaging of many different biomarkers in patient biopsies, diseases can be accurately diagnosed to guide the selection of the ideal treatment.
As an urgent need to advance single-cell analysis, imaging-based technologies have been developed to detect and quantify multiple DNA, RNA and protein molecules in single cell in situ. Novel fluorescent probes have been designed and synthesized, which targets specifically either their nucleic acid counterpart or protein epitopes. These highly multiplexed imaging-based platforms have the potential to detect and quantify 100 different protein molecules and 1000 different nucleic acids in a single cell.
Using novel fluorescent probes, a large number of biomolecules have been detected and quantified in formalin-fixed paraffin-embedded (FFPE) brain tissue at single-cell resolution. By studying protein expression levels, neuronal heterogeneity has been revealed in distinct subregions of human hippocampus.
healthy physiology and disease pathogenesis. The comprehensive profiling of biomolecules in individual cells of a heterogeneous system can provide deep insights into many important biological questions, such as the distinct cellular compositions or regulation of inter- and intracellular signaling pathways of healthy and diseased tissues. With multidimensional molecular imaging of many different biomarkers in patient biopsies, diseases can be accurately diagnosed to guide the selection of the ideal treatment.
As an urgent need to advance single-cell analysis, imaging-based technologies have been developed to detect and quantify multiple DNA, RNA and protein molecules in single cell in situ. Novel fluorescent probes have been designed and synthesized, which targets specifically either their nucleic acid counterpart or protein epitopes. These highly multiplexed imaging-based platforms have the potential to detect and quantify 100 different protein molecules and 1000 different nucleic acids in a single cell.
Using novel fluorescent probes, a large number of biomolecules have been detected and quantified in formalin-fixed paraffin-embedded (FFPE) brain tissue at single-cell resolution. By studying protein expression levels, neuronal heterogeneity has been revealed in distinct subregions of human hippocampus.
ContributorsMondal, Manas (Author) / Guo, Jia (Thesis advisor) / Gould, Ian (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
Description
The understanding of normal human physiology and disease pathogenesis shows great promise for progress with increasing ability to profile genomic loci and transcripts in single cells in situ. Using biorthogonal cleavable fluorescent oligonucleotides, a highly multiplexed single-cell in situ RNA and DNA analysis is reported. In this report, azide-based cleavable linker connects oligonucleotides to fluorophores to show nucleic acids through in situ hybridization. Post-imaging, the fluorophores are effectively cleaved off in half an hour without loss of RNA or DNA integrity. Through multiple cycles of hybridization, imaging, and cleavage this approach proves to quantify thousands of different RNA species or genomic loci because of single-molecule sensitivity in single cells in situ. Different nucleic acids can be imaged by shown by multi-color staining in each hybridization cycle, and that multiple hybridization cycles can be run on the same specimen. It is shown that in situ analysis of DNA, RNA and protein can be accomplished using both cleavable fluorescent antibodies and oligonucleotides. The highly multiplexed imaging platforms will have the potential for wide applications in both systems biology and biomedical research. Thus, proving to be cost effective and time effective.
ContributorsSamuel, Adam David (Author) / Guo, Jia (Thesis director) / Liu, Wei (Committee member) / Wang, Xu (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05