KEYWORDS: Video, Human-machine interfaces, Injuries, Data processing, Video processing, Information science, Data acquisition, Data storage, Databases, Imaging systems
Research in the area of sports science and performance enhancement revolves around collecting multimedia data (eg, Video, images, and waveforms), processing them, and quantifying the results, which gives insight to help athletes improve their technique. For example, in long jump in track and field, the processed output of video with force vector overlays and force calculations allow coaches to view specific stages of the hop, step, and jump, and identify how each stage can be improved to increase jump distance. Outputs also provide insight into how athletes can better maneuver to prevent injury. Currently, each data collection site collects and stores data with their own methods without a standard. Different research groups store multimedia files and quantified results in different kinds of formats, structures, and locations. Imaging informatics-based principles were adopted to develop a platform for multiple institutions that promotes the standardization of sports performance data. The system will provide user authentication and privacy as in clinical trials, with specific user access rights. Data collected from different field sites will be standardized into specified formats before database storage similar to field sites in clinical imaging-based trials. Quantified results from image-processing algorithms are stored similar to CAD algorithm results. The system will streamline the current sports performance data workflow and provide a user interface for different users including researchers, athletes, coaches to view results of individual collections and longitudinally across different collections. The developed data viewer will allow for easy access and review of data to improve sports performance and prevent injury.
KEYWORDS: Data storage, Video, Information science, Data processing, Injuries, Imaging systems, Standards development, Human-machine interfaces, Imaging informatics, Data acquisition
The main goal of sports science and performance enhancement is to collect video and image data, process them, and quantify the results, giving insight to help athletes improve technique. For long jump in track and field, the processed output of video with force vector overlays and force calculations allow coaches to view specific stages of the hop, step, and jump, and identify how each stage can be improved to increase jump distance. Outputs also provide insight into how athletes can better maneuver to prevent injury. Currently, each data collection site collects and stores data with their own methods. There is no standard for data collection, formats, or storage. Video files and quantified results are stored in different formats, structures, and locations such as Dropbox and hard drives. Using imaging informatics-based principles we can develop a platform for multiple institutions that promotes the standardization of sports performance data. In addition, the system will provide user authentication and privacy as in clinical trials, with specific user access rights. Long jump data collected from different field sites will be standardized into specified formats before database storage. Quantified results from image-processing algorithms are stored similar to CAD algorithm results. The system will streamline the current sports performance data workflow and provide a user interface for athletes and coaches to view results of individual collections and also longitudinally across different collections. This streamlined platform and interface is a tool for coaches and athletes to easily access and review data to improve sports performance and prevent injury.
The increasing incidence of diabetes mellitus (DM) in modern society has become a serious issue. DM can also lead to several secondary clinical complications. One of these complications is diabetic retinopathy (DR), which is the leading cause of new cases of blindness for adults in the United States. While DR can be treated if screened and caught early in progression, the only currently effective method to detect symptoms of DR in the eyes of DM patients is through the manual analysis of fundus images. Manual analysis of fundus images is time-consuming for ophthalmologists and can reduce access to DR screening in rural areas. Therefore, effective automatic prescreening tools on a cloud-based platform might be a potential solution to that problem. Recently, deep learning (DL) approaches have been shown to have state-of-the-art performance in image analysis tasks. In this study, we established a research PACS for fundus images to view DICOMized and anonymized fundus images. We prototyped a deep learning engine in the PACS server to perform prescreening classification of uploaded fundus images into DR grade. We fine-tuned a deep convolutional neural network (CNN) model pretrained on the ImageNet dataset by using over 30,000 labeled image samples from the public Kaggle Diabetic Retinopathy Detection fundus image dataset6. We linked the PACS repository with the DL engine and demonstrated the output predicted result of DR into the PACS worklist. The initial prescreened result was promising and such applications could have potential as a “second reader” with future CAD development for nextgeneration PACS.
KEYWORDS: Imaging systems, Clinical trials, Information science, Data modeling, Feature extraction, Data mining, Medical imaging, System integration, Databases, Optimization (mathematics), Statistical analysis, Data analysis, Knowledge discovery
Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.
KEYWORDS: Clinical trials, Imaging systems, Data modeling, Systems modeling, Databases, Imaging informatics, Information science, Data mining, Data analysis, Feature extraction, Knowledge discovery, Process modeling
Previously, we presented an ePR system to support imaging based stroke rehabilitation clinical trials. To facilitate the data analysis, we developed a generalized linear mixed effects model (GLMM) module to investigate correlation based on features extracted from textual database and imaging biomarkers. With the proposed module, the system is able to evaluate a variety of measurements including quantitative imaging features. Moreover, once an accurate GLMM model is identified from the clinical trial, the module can be used to predict outcomes for new patients based on their conditions and used as a decision support tool for optimizing the treatment plans.
Previously, we presented an Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) imaging informatics system that supports a large-scale phase III stroke rehabilitation trial. The ePR system is capable of displaying anonymized patient imaging studies and reports, and the system is accessible to multiple clinical trial sites and users across the United States via the web. However, the prior multicenter stroke rehabilitation trials lack any significant neuroimaging analysis infrastructure. In stroke related clinical trials, identification of the stroke lesion characteristics can be meaningful as recent research shows that lesion characteristics are related to stroke scale and functional recovery after stroke. To facilitate the stroke clinical trials, we hope to gain insight into specific lesion characteristics, such as vascular territory, for patients enrolled into large stroke rehabilitation trials. To enhance the system’s capability for data analysis and data reporting, we have integrated new features with the system: a digital brain template display, a lesion quantification tool and a digital case report form. The digital brain templates are compiled from published vascular territory templates at each of 5 angles of incidence. These templates were updated to include territories in the brainstem using a vascular territory atlas and the Medical Image Processing, Analysis and Visualization (MIPAV) tool. The digital templates are displayed for side-by-side comparisons and transparent template overlay onto patients’ images in the image viewer. The lesion quantification tool quantifies planimetric lesion area from user-defined contour. The digital case report form stores user input into a database, then displays contents in the interface to allow for reviewing, editing, and new inputs. In sum, the newly integrated system features provide the user with readily-accessible web-based tools to identify the vascular territory involved, estimate lesion area, and store these results in a web-based digital format.
KEYWORDS: Data analysis, Brain, Imaging systems, Computer aided design, Databases, Magnetic resonance imaging, Data mining, Medical imaging, Imaging informatics, Analytical research
In the past, we have developed and displayed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and disease tracking. This year, we have further developed the eFolder system to handle big data analysis and data mining in today’s medical imaging field. The database has been updated to allow data mining and data look-up from DICOM-SR lesion analysis contents. Longitudinal studies are tracked, and any changes in lesion volumes and brain parenchyma volumes are calculated and shown on the webbased user interface as graphical representations. Longitudinal lesion characteristic changes are compared with patients’ disease history, including treatments, symptom progressions, and any other changes in the disease profile. The image viewer is updated such that imaging studies can be viewed side-by-side to allow visual comparisons. We aim to use the web-based medical imaging informatics eFolder system to demonstrate big data analysis in medical imaging, and use the analysis results to predict MS disease trends and patterns in Hispanic and Caucasian populations in our pilot study. The discovery of disease patterns among the two ethnicities is a big data analysis result that will help lead to personalized patient care and treatment planning.
Clinical trials usually have a demand to collect, track and analyze multimedia data according to the workflow. Currently, the clinical trial data management requirements are normally addressed with custom-built systems. Challenges occur in the workflow design within different trials. The traditional pre-defined custom-built system is usually limited to a specific clinical trial and normally requires time-consuming and resource-intensive software development. To provide a solution, we present a user customizable imaging informatics-based intelligent workflow engine system for managing stroke rehabilitation clinical trials with intelligent workflow. The intelligent workflow engine provides flexibility in building and tailoring the workflow in various stages of clinical trials. By providing a solution to tailor and automate the workflow, the system will save time and reduce errors for clinical trials. Although our system is designed for clinical trials for rehabilitation, it may be extended to other imaging based clinical trials as well.
KEYWORDS: Multimedia, Data storage, Imaging systems, Video, Data modeling, Data centers, Data processing, Decision support systems, Medical imaging, Electromyography
With the rapid development of science and technology, large-scale rehabilitation centers and clinical rehabilitation trials usually involve significant volumes of multimedia data. Due to the global aging crisis, millions of new patients with age-related chronic diseases will produce huge amounts of data and contribute to soaring costs of medical care. Hence, a solution for effective data management and decision support will significantly reduce the expenditure and finally improve the patient life quality. Inspired from the concept of the electronic patient record (ePR), we developed a prototype system for the field of rehabilitation engineering. The system is subject or patient-oriented and customized for specific projects. The system components include data entry modules, multimedia data presentation and data retrieval. To process the multimedia data, the system includes a DICOM viewer with annotation tools and video/audio player. The system also serves as a platform for integrating decision-support tools and data mining tools. Based on the prototype system design, we developed two specific applications: 1) DOSE (a phase 1 randomized clinical trial to determine the optimal dose of therapy for rehabilitation of the arm and hand after stroke.); and 2) NEXUS project from the Rehabilitation Engineering Research Center(RERC, a NIDRR funded Rehabilitation Engineering Research Center). Currently, the system is being evaluated in the context of the DOSE trial with a projected enrollment of 60 participants over 5 years, and will be evaluated by the NEXUS project with 30 subjects. By applying the ePR concept, we developed a system in order to improve the current research workflow, reduce the cost of managing data, and provide a platform for the rapid development of future decision-support tools.
KEYWORDS: Data modeling, Video, Analytical research, Motion analysis, Databases, System integration, Data integration, Data analysis, Injuries, Imaging informatics
Patients confined to manual wheel-chairs are at an added risk of shoulder injury. There is a need for developing optimal
bio-mechanical techniques for wheel-chair propulsion through movement analysis. Data collected is diverse and in need
of normalization and integration. Current databases are ad-hoc and do not provide flexibility, extensibility and ease of
access. The need for an efficient means to retrieve specific trial data, display it and compare data from multiple trials is
unmet through lack of data association and synchronicity. We propose the development of a robust web-based ePR
system that will enhance workflow and facilitate efficient data management.
Stroke is one of the major causes of death and disability in America. After stroke, about 65% of survivors still suffer
from severe paresis, while rehabilitation treatment strategy after stroke plays an essential role in recovery. Currently,
there is a clinical trial (NIH award #HD065438) to determine the optimal dose of rehabilitation for persistent recovery of
arm and hand paresis. For DOSE (Dose Optimization Stroke Evaluation), laboratory-based measurements, such as the
Wolf Motor Function test, behavioral questionnaires (e.g. Motor Activity Log-MAL), and MR, DTI, and Transcranial
Magnetic Stimulation (TMS) imaging studies are planned. Current data collection processes are tedious and reside in
various standalone systems including hardcopy forms. In order to improve the efficiency of this clinical trial and
facilitate decision support, a web-based imaging informatics system has been implemented together with utilizing mobile
devices (eg, iPAD, tablet PC's, laptops) for collecting input data and integrating all multi-media data into a single
system. The system aims to provide clinical imaging informatics management and a platform to develop tools to predict
the treatment effect based on the imaging studies and the treatment dosage with mathematical models. Since there is a
large amount of information to be recorded within the DOSE project, the system provides clinical data entry through
mobile device applications thus allowing users to collect data at the point of patient interaction without typing into a
desktop computer, which is inconvenient. Imaging analysis tools will also be developed for structural MRI, DTI, and
TMS imaging studies that will be integrated within the system and correlated with the clinical and behavioral data. This
system provides a research platform for future development of mathematical models to evaluate the differences between
prediction and reality and thus improve and refine the models rapidly and efficiently.
Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae.
However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for
small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of
small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the
evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the
knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed.
The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key
structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of
the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical
environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and
matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.
Stroke is a major cause of adult disability. The Interdisciplinary Comprehensive Arm Rehabilitation Evaluation
(I-CARE) clinical trial aims to evaluate a therapy for arm rehabilitation after stroke. A primary outcome measure is
correlative analysis between stroke lesion characteristics and standard measures of rehabilitation progress, from data
collected at seven research facilities across the country. Sharing and communication of brain imaging and behavioral
data is thus a challenge for collaboration. A solution is proposed as a web-based system with tools supporting imaging
and informatics related data. In this system, users may upload anonymized brain images through a secure internet
connection and the system will sort the imaging data for storage in a centralized database. Users may utilize an
annotation tool to mark up images. In addition to imaging informatics, electronic data forms, for example, clinical data
forms, are also integrated. Clinical information is processed and stored in the database to enable future data mining
related development. Tele-consultation is facilitated through the development of a thin-client image viewing
application. For convenience, the system supports access through desktop PC, laptops, and iPAD. Thus, clinicians may
enter data directly into the system via iPAD while working with participants in the study. Overall, this comprehensive
imaging informatics system enables users to collect, organize and analyze stroke cases efficiently.
Acute intra-cranial hemorrhage (AIH) may result from traumatic brain injury (TBI). Successful management of AIH
depends heavily on the speed and accuracy of diagnosis. Timely diagnosis in emergency environments in both civilian
and military settings is difficult primarily due to severe time restraints and lack of resources. Often, diagnosis is
performed by emergency physicians rather than trained radiologists. As a result, added support in the form of computer-aided
detection (CAD) would greatly enhance the decision-making process and help in providing faster and more
accurate diagnosis of AIH. This paper discusses the implementation of a CAD system in an emergency environment, and
its efficacy in aiding in the detection of AIH.
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