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Ling H, Chen B, Guan R, Xiao Y, Yan H, Chen Q, Bi L, Chen J, Feng X, Pang H, Song C. Deep Learning Model for Coronary Angiography. J Cardiovasc Transl Res 2023; 16:896-904. [PMID: 36928587 DOI: 10.1007/s12265-023-10368-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 03/02/2023] [Indexed: 03/18/2023]
Abstract
The visual inspection of coronary artery stenosis is known to be significantly affected by variation, due to the presence of other tissues, camera movements, and uneven illumination. More accurate and intelligent coronary angiography diagnostic models are necessary for improving the above problems. In this study, 2980 medical images from 949 patients are collected and a novel deep learning-based coronary angiography (DLCAG) diagnose system is proposed. Firstly, we design a module of coronary classification. Then, we introduce RetinaNet to balance positive and negative samples and improve the recognition accuracy. Additionally, DLCAG adopts instance segmentation to segment the stenosis of vessels and depict the degree of the stenosis vessels. Our DLCAG is available at http://101.132.120.184:8077/ . When doctors use our system, all they need to do is login to the system, upload the coronary angiography videos. Then, a diagnose report is automatically generated.
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Affiliation(s)
- Hao Ling
- Department of Cardiology, Second Hospital of Jilin University, Changchun, 130012, China
| | - Biqian Chen
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Renchu Guan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Yu Xiao
- Department of Cardiology, Second Hospital of Jilin University, Changchun, 130012, China
| | - Hui Yan
- Department of Cardiology, Second Hospital of Jilin University, Changchun, 130012, China
| | - Qingyu Chen
- Department of Cardiology, Sixth People's Hospital, Shanghai Jiaotong University, Shanghai, 200233, China
| | - Lianru Bi
- Department of Cardiology, the Eighth Affiliated Hospital of Sun Yat Sen University, Shenzhen, 518033, China
| | - Jingbo Chen
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Xiaoyue Feng
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Haoyu Pang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Chunli Song
- Department of Cardiology, Second Hospital of Jilin University, Changchun, 130012, China.
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Liu L, Chen W, Nie M, Zhang F, Wang Y, He A, Wang X, Yan G. iMAGE cloud: medical image processing as a service for regional healthcare in a hybrid cloud environment. Environ Health Prev Med 2016; 21:563-571. [PMID: 27783315 DOI: 10.1007/s12199-016-0582-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 10/09/2016] [Indexed: 10/20/2022] Open
Abstract
OBJECTIVES To handle the emergence of the regional healthcare ecosystem, physicians and surgeons in various departments and healthcare institutions must process medical images securely, conveniently, and efficiently, and must integrate them with electronic medical records (EMRs). In this manuscript, we propose a software as a service (SaaS) cloud called the iMAGE cloud. METHODS A three-layer hybrid cloud was created to provide medical image processing services in the smart city of Wuxi, China, in April 2015. In the first step, medical images and EMR data were received and integrated via the hybrid regional healthcare network. Then, traditional and advanced image processing functions were proposed and computed in a unified manner in the high-performance cloud units. Finally, the image processing results were delivered to regional users using the virtual desktop infrastructure (VDI) technology. Security infrastructure was also taken into consideration. RESULTS Integrated information query and many advanced medical image processing functions-such as coronary extraction, pulmonary reconstruction, vascular extraction, intelligent detection of pulmonary nodules, image fusion, and 3D printing-were available to local physicians and surgeons in various departments and healthcare institutions. CONCLUSIONS Implementation results indicate that the iMAGE cloud can provide convenient, efficient, compatible, and secure medical image processing services in regional healthcare networks. The iMAGE cloud has been proven to be valuable in applications in the regional healthcare system, and it could have a promising future in the healthcare system worldwide.
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Affiliation(s)
- Li Liu
- Information Center, Affiliated Hospital of Jiangnan University, No.200, Huihe Road, Wuxi, 214062, China
| | - Weiping Chen
- Wuxi Municipal Commission of Health and Family Planning, Wuxi, 214023, China
| | - Min Nie
- AccuRad Healthcare Network Co.,Ltd. Xi'an, Ascendas Innovation Hub B, No.38 Gao Xin 6 Road, Xi'an Hi-tech Industrial Development Zone, Xi'an, 710075, China
| | - Fengjuan Zhang
- Information Center, Affiliated Hospital of Jiangnan University, No.200, Huihe Road, Wuxi, 214062, China
| | - Yu Wang
- AccuRad Healthcare Network Co.,Ltd. Xi'an, Ascendas Innovation Hub B, No.38 Gao Xin 6 Road, Xi'an Hi-tech Industrial Development Zone, Xi'an, 710075, China
| | - Ailing He
- Information Center, Affiliated Hospital of Jiangnan University, No.200, Huihe Road, Wuxi, 214062, China
| | - Xiaonan Wang
- Comprehensive Health Care Department, Hangzhou First People's Hospital, Hangzhou, 310006, China
| | - Gen Yan
- Department of Radiology, Affiliated Hospital of Jiangnan University, No.200, Huihe Road, Wuxi, 214062, China.
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La Cruz A, Medina R, Vega F, Perez W, Ochoa B, Saquicela V, Espinoza M, Solano-Quinde L, Vidal ME. Mobile teleradiology system suitable for m-health services supporting content and semantic based image retrieval on a grid infrastructure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:5380-5383. [PMID: 28269475 DOI: 10.1109/embc.2016.7591943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Teleradiology systems tackle the problem of transferring radiological images between medical image workstations for facilitating different medical activities, e.g., diagnosis, treatment and follow up a patient, medical training, or consulting second opinion. Nowadays, m-Health (aka mobile health) is becoming popular because of high quality of mobile displays, although remains a work in progress. In this paper a mobile teleradiology system is reported, which main contribution is the development of a platform: (1) supported by a Grid infrastructure, (2) using biomedical ontologies for adding semantic annotations on medical images, and (3) supporting semantic and content-based image retrieval. Images are located physically in different repositories like; hospitals and diagnostic imaging centers. All these features make the system ubiquitous, portable, and suitable for m-Health services.
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Hindia MN, Rahman TA, Ojukwu H, Hanafi EB, Fattouh A. Enabling Remote Health-Caring Utilizing IoT Concept over LTE-Femtocell Networks. PLoS One 2016; 11:e0155077. [PMID: 27152423 PMCID: PMC4859479 DOI: 10.1371/journal.pone.0155077] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/24/2016] [Indexed: 11/22/2022] Open
Abstract
As the enterprise of the "Internet of Things" is rapidly gaining widespread acceptance, sensors are being deployed in an unrestrained manner around the world to make efficient use of this new technological evolution. A recent survey has shown that sensor deployments over the past decade have increased significantly and has predicted an upsurge in the future growth rate. In health-care services, for instance, sensors are used as a key technology to enable Internet of Things oriented health-care monitoring systems. In this paper, we have proposed a two-stage fundamental approach to facilitate the implementation of such a system. In the first stage, sensors promptly gather together the particle measurements of an android application. Then, in the second stage, the collected data are sent over a Femto-LTE network following a new scheduling technique. The proposed scheduling strategy is used to send the data according to the application's priority. The efficiency of the proposed technique is demonstrated by comparing it with that of well-known algorithms, namely, proportional fairness and exponential proportional fairness.
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Affiliation(s)
- M. N. Hindia
- Wireless Communication Centre, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
| | - T. A. Rahman
- Wireless Communication Centre, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
| | - H. Ojukwu
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - E. B. Hanafi
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - A. Fattouh
- Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Triantafyllopoulos D, Korvesis P, Mporas I, Megalooikonomou V. Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients. J Med Syst 2015; 40:45. [DOI: 10.1007/s10916-015-0403-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 11/09/2015] [Indexed: 10/22/2022]
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Shen Q, Liang X, Shen X, Lin X, Luo HY. Exploiting geo-distributed clouds for a e-health monitoring system with minimum service delay and privacy preservation. IEEE J Biomed Health Inform 2014; 18:430-9. [PMID: 24608048 DOI: 10.1109/jbhi.2013.2292829] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this paper, we propose an e-health monitoring system with minimum service delay and privacy preservation by exploiting geo-distributed clouds. In the system, the resource allocation scheme enables the distributed cloud servers to cooperatively assign the servers to the requested users under the load balance condition. Thus, the service delay for users is minimized. In addition, a traffic-shaping algorithm is proposed. The traffic-shaping algorithm converts the user health data traffic to the nonhealth data traffic such that the capability of traffic analysis attacks is largely reduced. Through the numerical analysis, we show the efficiency of the proposed traffic-shaping algorithm in terms of service delay and privacy preservation. Furthermore, through the simulations, we demonstrate that the proposed resource allocation scheme significantly reduces the service delay compared to two other alternatives using jointly the short queue and distributed control law.
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Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med 2014; 48:133-49. [DOI: 10.1016/j.compbiomed.2014.02.012] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Revised: 02/15/2014] [Accepted: 02/17/2014] [Indexed: 10/25/2022]
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Pang Z, Zheng L, Tian J, Kao-Walter S, Dubrova E, Chen Q. Design of a terminal solution for integration of in-home health care devices and services towards the Internet-of-Things. ENTERP INF SYST-UK 2013. [DOI: 10.1080/17517575.2013.776118] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Constantinescu L, Kim J, Kumar A, Haraguchi D, Wen L, Feng D. A patient-centric distribution architecture for medical image sharing. Health Inf Sci Syst 2013; 1:3. [PMID: 25825655 PMCID: PMC4336110 DOI: 10.1186/2047-2501-1-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Accepted: 09/26/2012] [Indexed: 11/21/2022] Open
Abstract
Over the past decade, rapid development of imaging technologies has resulted in the introduction of improved imaging devices, such as multi-modality scanners that produce combined positron emission tomography-computed tomography (PET-CT) images. The adoption of picture archiving and communication systems (PACS) in hospitals have dramatically improved the ability to digitally share medical image studies via portable storage, mobile devices and the Internet. This has in turn led to increased productivity, greater flexibility, and improved communication between hospital staff, referring physicians, and outpatients. However, many of these sharing and viewing capabilities are limited to proprietary vendor-specific applications. Furthermore, there are still interoperability and deployment issues which reduce the rate of adoption of such technologies, thus leaving many stakeholders, particularly outpatients and referring physicians, with access to only traditional still images with no ability to view or interpret the data in full. In this paper, we present a distribution architecture for medical image display across numerous devices and media, which uses a preprocessor and an in-built networking framework to improve compatibility and promote greater accessibility of medical data. Our INVOLVE2 system consists of three main software modules: 1) a preprocessor, which collates and converts imaging studies into a compressed and distributable format; 2) a PACS-compatible workflow for self-managing distribution of medical data, e.g. via CD USB, network etc; 3) support for potential mobile and web-based data access. The focus of this study was on cultivating patient-centric care, by allowing outpatient users to comfortably access and interpret their own data. As such, the image viewing software included on our cross-platform CDs was designed with a simple and intuitive user-interface (UI) for use by outpatients and referring physicians. Furthermore, digital image access via mobile devices or web-based access enables users to engage with their data in a convenient and user-friendly way. We evaluated the INVOLVE2 system using a pilot deployment in a hospital environment.
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Affiliation(s)
- Liviu Constantinescu
- School of Information Technologies, University of Sydney, Building J12, Sydney, Australia
| | - Jinman Kim
- School of Information Technologies, University of Sydney, Building J12, Sydney, Australia
| | - Ashnil Kumar
- School of Information Technologies, University of Sydney, Building J12, Sydney, Australia
| | - Daiki Haraguchi
- School of Information Technologies, University of Sydney, Building J12, Sydney, Australia
| | - Lingfeng Wen
- School of Information Technologies, University of Sydney, Building J12, Sydney, Australia ; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia
| | - Dagan Feng
- School of Information Technologies, University of Sydney, Building J12, Sydney, Australia
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