Mingqing Wang

Implementation methods of medical image sharing for collaborative health care based on IHE XDS-I profile

Abstract. 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.

Medical imaging document sharing solutions for various kinds of healthcare services based on IHE XDS/XDS-I profiles

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.

Design of e-Science platform for biomedical imaging research cross multiple academic institutions and hospitals

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.

Preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning convolutional neural network

The benign and malignant differential diagnosis of small pulmonary nodules (diameter < 20 mm) found in lung CT images is big challenges for most of radiologists. Here, we presented our preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning Convolutional Neural Network (CNN). The 921 cases with small benign and malignant pulmonary nodules confirmed by pathology were collected from three data sources and were used to train and validate the CNN. The preliminary results of AUCs of ROC curves for differentiating benign and malignant pulmonary small nodules with various types and sizes of solid, semi-solid and ground glass nodules were presented and discussed.

Real-time teleconsultation for difficult diseases with high resolution and large volume medical images in regional collaborative healthcare

Online peer to peer medical consultation between doctors such as physicians and specialists in China has a broad market demand and has been continuously accepted. For some difficult diseases, electronic medical records with medical images are required to present to both sides at same time during the consultation so that both sides can manipulate the records interactively to understand the medical meanings of the records, especially images. Here, we presented design of a teleconsultation system integrated with a cloud-based collaborative image sharing network to provide online peer-to-peer medical consultation for difficult cases with multi-media medical records including DICOM images. The presented teleconsultation system provides bidirectional interactive manipulations on images presented to peer-to-peer sides and has been used for small lung nodule diagnosis services between Huadong hospital in Shanghai and Jiaxing First Hospital in Zhejiang Province through Internet.

Cloud-based image sharing network for collaborative imaging diagnosis and consultation

In this presentation, we presented a new approach to design cloud-based image sharing network for collaborative imaging diagnosis and consultation through Internet, which can enable radiologists, specialists and physicians locating in different sites collaboratively and interactively to do imaging diagnosis or consultation for difficult or emergency cases. The designed network combined a regional RIS, grid-based image distribution management, an integrated video conferencing system and multi-platform interactive image display devices together with secured messaging and data communication. There are three kinds of components in the network: edge server, grid-based imaging documents registry and repository, and multi-platform display devices. This network has been deployed in a public cloud platform of Alibaba through Internet since March 2017 and used for small lung nodule or early staging lung cancer diagnosis services between Radiology departments of Huadong hospital in Shanghai and the First Hospital of Jiaxing in Zhejiang Province.

Three-Dimensional Visual Patient Based on Electronic Medical Diagnostic Records

Objective: an innovative concept and method is introduced to use a 3-D anatomical graphic pattern called visual patient (VP) visually to index, represent, and render the medical diagnostic records (MDRs) of a patient, so that a doctor can quickly learn the current and historical medical status of the patient by manipulating VP. The MDRs can be imaging diagnostic reports and DICOM images, laboratory reports and clinical summaries which can have clinical information relating to medical status of human organs or body parts. Methods: the concept and method included three steps. First, a VP data model called visual index object (VIO) and a VP graphic model called visual anatomic object (VAO) were introduced. Second, a series of processing methods of parsing and extracting key information from MDRs were used to fill the attributes of the VIO model of a patient. Third, a VP system (VPS) was designed to map VIO to VAO, to create a VP instance for each patient. Results: a prototype VPS has been implemented in a simulated hospital PACS/RIS integrated environment. Two evaluation results showed that more than 70% participating radiologists would like to use the VPS in their radiological imaging tasks, and the efficiency of using VPS to review the tested patients’ MDRs was 2.24 times higher than that of using PACS/RIS, while the average accuracy <Pac> by using PACS/RIS was better than that by using VPS; however, this difference was only about 4%. Conclusion: the developed VPS can show the medical status of patient organs/sub-organs with 3-D anatomical graphic pattern and will be welcomed by radiologists with better efficiency in reviewing the patients’ MDRs and with acceptable accuracy. Significance: the VP introduces a new way for medical professionals to access and interact with a huge amount of patient records with better efficiency in the big data era.

Principle and engineering implementation of 3D visual representation and indexing of medical diagnostic records (Conference Presentation)

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.

Clinical experiences of collaborative imaging diagnosis in Shanghai district healthcare services

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.

Building high dimensional imaging database for content based image search

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.