Clinical Assessment in Neuropsychiatric Image Databases

描述(由申请人提供)：该项目旨在实现一个数据库 神经精神病学PET图像以及用户友好的网络集成工具 支持神经精神病学临床诊断和知识发现(KDD)。 在第一阶段，我们寻求为三个中介性和强制性问题提供解决方案 需要。首先，为了做出完全知情的诊断和预后，现在临床医生 需要快速、轻松地访问标准数据库以进行解释， 常规目视检查，对个别患者进行脑功能成像。 第二，敏感和高效的搜索，有能力控制 人口统计变量，需要正常受试者和 病人。神经影像数据库通常不包含筛查对象 关于神经精神疾病的详细信息。这样的筛查是必不可少的，因为32%的人 终生精神疾病患病率--导致长期-- 术语结构和功能cn序列。第三，改进和增强实力 数据库管理系统(DBMS)和专门的计算机接口必须 支持这样的数据集及其查询。 我们提出了以下具体目标：1)实现关系数据库， 可互操作的网络分布式数据库，包含PET图像数据、程序 和结果，以及可视化和分析的通用工具。2)至 将我们现有的数据(大约6,000次PET扫描)合并到数据库中 用于归档、存储、管理和知识发现；以及获取(用于临床 应用)50个标准葡萄糖代谢扫描(FDG)。3)开发一个 临床医生-S界面；4)评估I期结果a)通过特定 在阿尔茨海默病？S病(AD)诊断中的应用 各设施的参考数据集的概括性；以及 关于临床随访和尸检的交叉验证。 该项目应提供必要的基础设施和验证 进入第二阶段，迈向Web分布式数据库联合 包含控制和神经精神病学数据，以支持临床护理和 研究。

DESCRIPTION (provided by applicant): This project aims to implement a database of neuropsychiatric PET images along with user-friendly, web-integrated tools to support neuropsychiatric clinical diagnosis and knowledge discovery (KDD). In Phase I, we seek to provide solutions to three mediate and compelling needs. First, to make fully informed diagnoses and prognoses, clinicians now require fast and easy access to normative databases to interpret, beyond routine visual inspection, functional brain images of individual patients. Second, sensitive and efficient searches, that have the power to control for demographic variables, call for large datasets of both normal subjects and patients. Neuroimaging databases do not typically contain subjects screened in detail for neuropsychiatric disease. Such screening is essential given the 32% lifetime prevalence of mental illnesses -- resulting in long-\ term structural and functional cnsequences. Third, improved and more powerful database management systems (DBMS) and specialized computer interface must support such datasets and their query. We propose the following Specific Aims: 1) To implement a relational, interoperable, web-distributed database containing PET image data, procedures, and results, along with general tools for visualization and analysis. 2) To incorporate into the database our extant data (approximately 6,000 PET scans) for archiving, storage, curation, and KDD; and to acquire (for clinical application) fifty normative glucose metabolic scans (FDG). 3) To develop a clinician?s interface; and 4) To evaluate Phase I results a) by specific application to the diagnosis of Alzheimer?s disease (AD); by testing the generalizability of reference datasets across facilities; and by crossvalidating with respect to clinical follow-up and autopsy. This project should provide the necessary infrastructure and validation to proceed in Phase II toward a web-distributed federation of databases containing control and neuropsychiatric data to support clinical care and research.