P. Vizza, P. Guzzi, P. Veltri, G. Cascini, R. Curia, Loredana Sisca
{"title":"GIDAC: A prototype for bioimages annotation and clinical data integration","authors":"P. Vizza, P. Guzzi, P. Veltri, G. Cascini, R. Curia, Loredana Sisca","doi":"10.1109/BIBM.2016.7822663","DOIUrl":null,"url":null,"abstract":"The analysis of bioimages and their correlated clinical patient information allows to investigate specific diseases and define the corresponding medical protocols. To perform a correct diagnosis and apply a precise therapy, bioimages must be collected and studied together with others relevant data as well as laboratory results, medical annotations and patient history. Today, the management of these data is performed by single systems inside hospital departments that often do not provide dedicated data integration platforms among different departments as well as different health structures to exchange of relevant clinical information. Also, images cannot be annotated or enriched by physicians to trace temporal studies for patients or even among patients with similar diseases. In this contribution, we report the results of a research project called GIDAC (standing for Gestione Integrata DAti Clinici) that aims to define a general purpose framework for the bioimages management and annotations as well as clinical data view and integration in a simple-to-use information system. The proposed framework does not substitute any existing clinical information system but is able in gathering and integrating data by using a XML-based module. The novelty also consists in allowing annotations on DICOM images by means of simple user-interface to take trace of changes intra images as well as comparisons among patients. This system supports oncologists in the management of DICOM images from different devices (e.g., ecograph or PACS) to extract relevant information necessary to query (annotate) images and study similar clinical cases.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2016.7822663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
The analysis of bioimages and their correlated clinical patient information allows to investigate specific diseases and define the corresponding medical protocols. To perform a correct diagnosis and apply a precise therapy, bioimages must be collected and studied together with others relevant data as well as laboratory results, medical annotations and patient history. Today, the management of these data is performed by single systems inside hospital departments that often do not provide dedicated data integration platforms among different departments as well as different health structures to exchange of relevant clinical information. Also, images cannot be annotated or enriched by physicians to trace temporal studies for patients or even among patients with similar diseases. In this contribution, we report the results of a research project called GIDAC (standing for Gestione Integrata DAti Clinici) that aims to define a general purpose framework for the bioimages management and annotations as well as clinical data view and integration in a simple-to-use information system. The proposed framework does not substitute any existing clinical information system but is able in gathering and integrating data by using a XML-based module. The novelty also consists in allowing annotations on DICOM images by means of simple user-interface to take trace of changes intra images as well as comparisons among patients. This system supports oncologists in the management of DICOM images from different devices (e.g., ecograph or PACS) to extract relevant information necessary to query (annotate) images and study similar clinical cases.
生物图像及其相关临床患者信息的分析允许调查特定疾病并确定相应的医疗方案。为了进行正确的诊断和应用精确的治疗,必须收集生物图像并与其他相关数据以及实验室结果、医学注释和患者病史一起研究。目前,这些数据的管理是由医院部门内部的单一系统完成的,这些系统往往没有在不同部门和不同医疗机构之间提供专用的数据集成平台来交换相关的临床信息。此外,医生无法对图像进行注释或丰富,以追踪患者甚至患有类似疾病的患者的时间研究。在这篇文章中,我们报告了一个名为GIDAC (Gestione Integrata DAti Clinici)的研究项目的结果,该项目旨在定义一个通用框架,用于生物图像管理和注释,以及临床数据视图和集成在一个简单易用的信息系统中。该框架不替代任何现有的临床信息系统,而是能够使用基于xml的模块收集和集成数据。其新颖之处还在于允许通过简单的用户界面对DICOM图像进行注释,以跟踪图像内的变化以及患者之间的比较。该系统支持肿瘤学家管理来自不同设备(如ecograph或PACS)的DICOM图像,以提取查询(注释)图像和研究类似临床病例所需的相关信息。