KEYWORDS: Picture Archiving and Communication System, 3D modeling, Visualization, Visual process modeling, Artificial intelligence, Telecommunications, 3D visualizations, Data modeling, Image processing, 3D image processing
This paper proposed a new generation PACS (Picture Archiving and Communication System) based on artificial intelligent visualization. It is developed from our GRIDPACS (patent number: US8805890), which combined with IHE XDS-I profile, to implement images communication, storage and display. It also uses 3D anatomical visualization model to extract multi-source data from PACS/RIS/HIS/EMR, to express patient disease location, size and severity, which was introduced as Visual Patent (VP) at previous SPIE Medical Imaging (SPIE MI 2018). It can integrate the training model of AI Imaging Diagnosis, to mark the focus and display the disease trends. The system not only has the original PACS functions, but also realizes the man-machine interaction (images and electronic medical record information between radiologist and patient) in a personalized, fast, comprehensive, quantitative and easy-to-understand way. It can be used in various medical institutions, image diagnostic centers, and imaging cloud, to support the healthy development of imaging technology in China.
KEYWORDS: Medical imaging, Artificial intelligence, Clouds, Data communications, Internet, Picture Archiving and Communication System, Image processing, Mobile devices, Cancer, Document imaging
This paper proposed a new approach to design medical imaging-sharing service network based on professional medical imaging center (PMIC). PMIC is famous for advanced imaging modalities and expert resources. The network connects clinics, hospitals and PMICs to provide collaborative diagnosis, consultation, mobile expert consulting and medical imaging artificial intelligence (AI) analysis services through Internet. It allows patients to be registered in hospital and examined in PMIC. It provides to schedule and view patients exam from mobile devices. It also provides AI analysis for some specific kinds of medical images such as carotid plaque and mammary cancer, to help doctors get accurate conclusions. The network is flexible to use three layers architecture with secure messaging and data communication: data source, service cloud and service provider. It has been deployed in Guangzhou Huyun Medical Imaging Diagnosis Center since July 2018 to provide services for the First People’s Hospital of Guangzhou.
KEYWORDS: Electronic imaging, Diagnostics, Visualization, Virtual point source, 3D visualizations, Picture Archiving and Communication System, 3D displays, Medical diagnostics, Medicine, Radiology
Purpose:
Due to the generation of a large number of electronic imaging diagnostic records (IDR) year after year in a digital hospital, The IDR has become the main component of medical big data which brings huge values to healthcare services, professionals and administration. But a large volume of IDR presented in a hospital also brings new challenges to healthcare professionals and services as there may be too many IDRs for each patient so that it is difficult for a doctor to review all IDR of each patient in a limited appointed time slot. In this presentation, we presented an innovation method which uses an anatomical 3D structure object visually to represent and index historical medical status of each patient, which is called Visual Patient (VP) in this presentation, based on long term archived electronic IDR in a hospital, so that a doctor can quickly learn the historical medical status of the patient, quickly point and retrieve the IDR he or she interested in a limited appointed time slot.
Method:
The engineering implementation of VP was to build 3D Visual Representation and Index system called VP system (VPS) including components of natural language processing (NLP) for Chinese, Visual Index Creator (VIC), and 3D Visual Rendering Engine.There were three steps in this implementation: (1) an XML-based electronic anatomic structure of human body for each patient was created and used visually to index the all of abstract information of each IDR for each patient; (2)a number of specific designed IDR parsing processors were developed and used to extract various kinds of abstract information of IDRs retrieved from hospital information systems; (3) a 3D anatomic rendering object was introduced visually to represent and display the content of VIO for each patient.
Results:
The VPS was implemented in a simulated clinical environment including PACS/RIS to show VP instance to doctors. We setup two evaluation scenario in a hospital radiology department to evaluate whether radiologists accept the VPS and how the VP impact the radiologists’ efficiency and accuracy in reviewing historic medical records of the patients. We got a statistical results showing that more than 70% participated radiologist would like to use the VPS in their radiological imaging services. In comparison testing of using VPS and RIS/PACS in reviewing historic medical records of the patients, we got a statistical result showing that the efficiency of using VPS was higher than that of using PACS/RIS.
New Technologies and Results to be presented:
This presentation presented an innovation method to use an anatomical 3D structure object, called VP, visually to represent and index historical medical records such as IDR of each patient and a doctor can quickly learn the historical medical status of the patient through VPS. The evaluation results showed that VPS has better performance than RIS-integrated PACS in efficiency of reviewing historic medical records of the patients.
Conclusions:
In this presentation, we presented an innovation method called VP to use an anatomical 3D structure object visually to represent and index historical IDR of each patient and briefed an engineering implementation to build a VPS to implement the major features and functions of VP. We setup two evaluation scenarios in a hospital radiology department to evaluate VPS and achieved evaluation results showed that VPS has better performance than RIS-integrated PACS in efficiency of reviewing historic medical records of the patients.
KEYWORDS: Imaging systems, Medicine, Diagnostics, Data modeling, Medical imaging, Picture Archiving and Communication System, Surgery, Systems modeling, Data backup, Image processing
To improve healthcare service quality with balancing healthcare resources between large and
small hospitals, as well as reducing costs, each district health administration in Shanghai with more than 24 million citizens has built image-enabled electronic healthcare records (iEHR) system to share patient
medical records and encourage patients to visit small hospitals for initial evaluations and preliminary
diagnoses first, then go to large hospitals to have better specialists’ services. We implemented solution for
iEHR systems, based on the IHE XDS-I integration profile and installed the systems in more than 100
hospitals cross three districts in Shanghai and one city in Jiangsu Province in last few years. Here, we give operational results of these systems in these four districts and evaluated the performance of the
systems in servicing the regional collaborative imaging diagnosis.
Radiologists currently use a variety of terminologies and standards in most hospitals in China, and even there are multiple terminologies being used for different sections in one department. In this presentation, we introduce a medical semantic comprehension system (MedSCS) to extract semantic information about clinical findings and conclusion from free text radiology reports so that the reports can be classified correctly based on medical terms indexing standards such as Radlex or SONMED-CT. Our system (MedSCS) is based on both rule-based methods and statistics-based methods which improve the performance and the scalability of MedSCS. In order to evaluate the over all of the system and measure the accuracy of the outcomes, we developed computation methods to calculate the parameters of precision rate, recall rate, F-score and exact confidence interval.
In medical imaging informatics, content-based image retrieval (CBIR) techniques are employed to aid radiologists in the retrieval of images with similar image contents. CBIR uses visual contents, normally called as image features, to search images from large scale image databases according to users’ requests in the form of a query image. However, most of current CBIR systems require a distance computation of image character feature vectors to perform query, and the distance
computations can be time consuming when the number of image character features grows large, and thus this limits the
usability of the systems. In this presentation, we propose a novel framework which uses a high dimensional database to index the image character features to improve the accuracy and retrieval speed of a CBIR in integrated RIS/PACS.
IHE XDS-I profile proposes an architecture model for cross-enterprise medical image sharing, but there are only a few clinical implementations reported. Here, we investigate three pilot studies based on the IHE XDS-I profile to see whether we can use this architecture as a foundation for image sharing solutions in a variety of health-care settings. The first pilot study was image sharing for cross-enterprise health care with federated integration, which was implemented in Huadong Hospital and Shanghai Sixth People’s Hospital within the Shanghai Shen-Kang Hospital Management Center; the second pilot study was XDS-I–based patient-controlled image sharing solution, which was implemented by the Radiological Society of North America (RSNA) team in the USA; and the third pilot study was collaborative imaging diagnosis with electronic health-care record integration in regional health care, which was implemented in two districts in Shanghai. In order to support these pilot studies, we designed and developed new image access methods, components, and data models such as RAD-69/WADO hybrid image retrieval, RSNA clearinghouse, and extension of metadata definitions in both the submission set and the cross-enterprise document sharing (XDS) registry. We identified several key issues that impact the implementation of XDS-I in practical applications, and conclude that the IHE XDS-I profile is a theoretically good architecture and a useful foundation for medical image sharing solutions across multiple regional health-care providers.
We had designed a semantic searching engine (SSE) for radiological imaging to search both reports and images in RIS-integrated PACS environment. In this presentation, we present evaluation results of this SSE about how it impacting the radiologists’ behaviors in reporting for different kinds of examinations, and how it improving the performance of retrieval and usage of historical images in RIS-integrated PACS.
One key problem for continuity of patient care is identification of a proper method to share and exchange patient medical records among multiple hospitals and healthcare providers. This paper focuses in the imaging document component of medical record. The XDS-I (Cross- Enterprise Document Sharing – Image) Profile based on the IHE IT-Infrastructure extends and specializes XDS to support imaging “document” sharing in an affinity domain. We present three studies about image sharing solutions based on IHE XDS-I Profile. The first one is to adopt the IHE XDS-I profile as a technical guide to design image and report sharing mechanisms between hospitals for regional healthcare service in Shanghai. The second study is for collaborating image diagnosis in regional healthcare services. The latter study is to investigate the XDS-I based clearinghouse for patient controlled image sharing in the RSNA Image Sharing Network Project. We conclude that the IHE XDS/XDS-I profiles can be used as the foundation to design medical image document sharing for Various Healthcare Services.
KEYWORDS: Biomedical optics, Medical research, Medical imaging, Data modeling, Data acquisition, Image storage, Data storage, Image processing, Data centers, Internet
As there are urgent demands to bring medical imaging research and clinical service together more closely to solve the problems related to disease discover and medical research, a new imaging informatics infrastructure need to be developed to promote multiple disciplines of medical researchers and clinical physicians working together in a secured and efficient cooperative environment. In this presentation, we outline our work of building Biomedical Imaging Informatics “e-Science” platform integrated with high performance image sharing, collaborating and computing to support multi-disciplines translational biomedical imaging research in multiple affiliated hospitals and academic institutions in Shanghai.
KEYWORDS: Biomedical optics, Medical research, Medical imaging, Image transmission, Image storage, Data centers, Information science, Data modeling, Image retrieval, Biomedical engineering
More and more image informatics researchers and engineers are considering to re-construct imaging and informatics
infrastructure or to build new framework to enable multiple disciplines of medical researchers, clinical physicians
and biomedical engineers working together in a secured, efficient, and transparent cooperative environment. In this
presentation, we show an outline and our preliminary design work of building an e-Science platform for biomedical
imaging and informatics research and application in Shanghai. We will present our consideration and strategy on
designing this platform, and preliminary results. We also will discuss some challenges and solutions in building this
platform.
KEYWORDS: Medicine, Medical imaging, Imaging systems, Picture Archiving and Communication System, Image retrieval, Diagnostics, Document imaging, Data centers, Physics, Data backup
We designed the image-enabled EHR sharing solution (i-EHR) for cross-enterprise and cross-domain with
SOA architecture and combined the grid-based image management and distribution capability, which are
compliant with IHE XDS-I/XCA integration profiles. We selected one districts with four hospitals and two
hospital groups as image sharing pilot testing bed. Our approach presented in this presentation uses
peer-to-peer mode to share and exchange image data cross enterprise PACSs and domains, which provides
single point of services to local systems so it is easy to integrate with different vendor's PACS and easy to
deploy to different hospitals to implement the i-EHR.
One way to improve accuracy of diagnosis and provide better medical treatment to patients is to recall or find
records of previous patients with similar disease features from healthcare information systems which already have
confirmed diagnostic results. In most situations, features of disease may be described by other kinds of
information or data types such as numerical reports or a simple or complicated SR (Structure Reports) generated
from Ultrasound Information System (USIS) or from computer assisted detection (CAD) components, or
laboratory information system (LIS). In this presentation, we described a new approach to search and retrieve
numerical reports based on the contents of parameters from large database of numerical reports. We have tested
this approach by using numerical data from an ultrasound information system (USIS) and got desired results both
in accuracy and performance. The system can be wrapped as a web service and is being integrated into a USIS and
EMR for clinical evaluation without interrupting the normal operations of USIS/RIS/PACS. We give the design
architecture and implementation strategy of this novel framework to provide feature based case retrieval capability
in an integrated healthcare information system.
Medical imaging modalities generate huge amount of medical images daily, and there are urgent demands to search large-scale image databases in an RIS-integrated PACS environment to support medical research and diagnosis by using image visual content to find visually similar images. However, most of current content-based image retrieval
(CBIR) systems require distance computations to perform query by image content. Distance computations can be time consuming when image database grows large, and thus limits the usability of such systems. Furthermore, there is still a semantic gap between the low-level visual features automatically extracted and the high-level
concepts that users normally search for. To address these problems, we propose a novel framework that combines text retrieval and CBIR techniques in order to support searching large-scale medical image database while integrated RIS/PACS is in place. A prototype system for CBIR has been implemented, which can query similar
medical images both by their visual content and relevant semantic descriptions (symptoms and/or possible diagnosis). It also can be used as a decision support tool for radiology diagnosis and a learning tool for education.
KEYWORDS: Medicine, Picture Archiving and Communication System, Image retrieval, Image processing, Electronic design automation, Data modeling, Systems modeling, Data backup, Imaging systems, Physics
Shanghai is piloting to develop an EHR system to solve the problems of medical document sharing for
collaborative healthcare, the solution of which is considering to following IHE XDS (cross-enterprise document
sharing) and XCA (cross-community access) technical profiles as well as combined with grid storage for images.
The first phase of the project targets text and image documents sharing cross four local domains or communities,
each of which consists of multiple hospitals. The prototype system was designed and developed with
service-oriented architecture (SOA) and Event-Driven Architecture (EDA), basing on IHE XDS.b and XCA
profiles, and consists of four level components: one central city registry; the multiple domain registries, each of
which is for one local domain or community; the multiple repositories corresponding to multiple local domain
registries; and multiple document source agents, each of which is located in each hospital to provide the patient
healthcare information. The system was developed and tested for performance evaluation including data
publication, user query and image retrieval. The results are extremely positive and demonstrate that the designed
EHR solution based on SOA with grid concept can scale effectively to serve medical document sharing
cross-domain or community in a large city.
KEYWORDS: Computer security, Information security, Document imaging, Control systems, Databases, Computing systems, Network security, Medicine, System integration, Web services
The EHR is a secure, real-time, point-of-care, patient-centric information resource for healthcare providers. Many
countries and regional districts have set long-term goals to build EHRs, and most of EHRs are usually built based on the
integration of different information systems with different information models and platforms. A number of hospitals in
Shanghai are also piloting the development of an EHR solution based on IHE XDS/XDS-I profiles with a
service-oriented architecture (SOA). The first phase of the project targets the Diagnostic Imaging domain and allows
seamless sharing of images and reports across the multiple hospitals. To develop EHRs for regional coordinated
healthcare, some factors should be considered in designing architecture, one of which is security issue. In this paper, we
present some approaches and policies to improve and strengthen the security among the different hospitals' nodes, which
are compliant with the security requirements defined by IHE IT Infrastructure (ITI) Technical Framework. Our security
solution includes four components: Time Sync System (TSS), Digital Signature Manage System (DSMS), Data
Exchange Control Component (DECC) and Single Sign-On (SSO) System. We give a design method and
implementation strategy of these security components, and then evaluate the performance and overheads of the security
services or features by integrating the security components into an image-based EHR system.
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