Ximing Wang

Development of a novel imaging informatics-based system with an intelligent workflow engine (IWEIS) to support imaging-based clinical trials

Imaging based clinical trials can benefit from a solution to efficiently collect, analyze, and distribute multimedia data at various stages within the workflow. Currently, the data management needs of these trials are typically addressed with custom-built systems. However, software development of the custom-built systems for versatile workflows can be resource-consuming. To address these challenges, we present a system with a workflow engine for imaging based clinical trials. The system enables a project coordinator to build a data collection and management system specifically related to study protocol workflow without programming. Web Access to DICOM Objects (WADO) module with novel features is integrated to further facilitate imaging related study. The system was initially evaluated by an imaging based rehabilitation clinical trial. The evaluation shows that the cost of the development of system can be much reduced compared to the custom-built system. By providing a solution to customize a system and automate the workflow, the system will save on development time and reduce errors especially for imaging clinical trials.

Stroke Lesions in a Large Upper Limb Rehabilitation Trial Cohort Rarely Match Lesions in Common Preclinical Models

Background. Stroke patients with mild-moderate upper extremity motor impairments and minimal sensory and cognitive deficits provide a useful model to study recovery and improve rehabilitation. Laboratory-based investigators use lesioning techniques for similar goals. Objective. To determine whether stroke lesions in an upper extremity rehabilitation trial cohort match lesions from the preclinical stroke recovery models used to drive translational research. Methods. Clinical neuroimages from 297 participants enrolled in the Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) study were reviewed. Images were characterized based on lesion type (ischemic or hemorrhagic), volume, vascular territory, depth (cortical gray matter, cortical white matter, subcortical), old strokes, and leukoaraiosis. Lesions were compared with those of preclinical stroke models commonly used to study upper limb recovery. Results. Among the ischemic stroke participants, median infarct volume was 1.8 mL, with most lesions confined to subcortical structures (61%) including the anterior choroidal artery territory (30%) and the pons (23%). Of ICARE participants, <1% had lesions resembling proximal middle cerebral artery or surface vessel occlusion models. Preclinical models of subcortical white matter injury best resembled the ICARE population (33%). Intracranial hemorrhage participants had small (median 12.5 mL) lesions that best matched the capsular hematoma preclinical model. Conclusions. ICARE subjects are not representative of all stroke patients, but they represent a clinically and scientifically important subgroup. Compared with lesions in general stroke populations and widely studied animal models of recovery, ICARE participants had smaller, more subcortically based strokes. Improved preclinical-clinical translational efforts may require better alignment of lesions between preclinical and human stroke recovery models.

Development of a user customizable imaging informatics-based intelligent workflow engine system to enhance rehabilitation clinical trials

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.

An imaging informatics-based ePR (electronic patient record) system for providing decision support in evaluating dose optimization in stroke rehabilitation

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 PCs, 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.

Imaging informatics-based multimedia ePR system for data management and decision support in rehabilitation research

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.

A Multimedia Comprehensive Informatics System with Decision Support Tools for a Multi-site Collaboration Research of Stroke Rehabilitation

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.

The development of an imaging informatics-based multi-institutional platform to support sports performance and injury prevention in track and field

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 application of deep learning for diabetic retinopathy prescreening in research eye-PACS

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.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

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.

Development of a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials

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.