A Context-Sensitive Teleconsultation Infrastructure

上下文敏感的远程会诊基础设施

• 批准号: 6725819
• 负责人: HOOSHANG KANGARLOO
• 金额: $ 38.33万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-30 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6725819
• 关键词: anatomy; clinical research; computer assisted diagnosis; computer assisted medical decision making; computer assisted patient care; computer data analysis; computer graphics /printing; computer system design /evaluation; diagnosis quality /standard; health care quality; health care referral /consultation; human data; image enhancement; image processing; magnetic resonance imaging; musculoskeletal disorder; nervous system disorder; statistics /biometry; telemedicine

DESCRIPTION (provided by applicant): Consultation with appropriate specialists improves the quality of healthcare, particularly in patients with complicated cases or chronic illnesses. And for the majority of such patients, specialists use imaging studies (e.g., MR, CT) to objectively document the disease process (e.g., a cancer patient on chemotherapy). However, specialists are generally not available in all communities, tending to be concentrated in academic/specialty centers. Thus, to facilitate the routine use of teleconsultations for patients when specialists are not locally present: 1) the images captured to document the patient's condition must be incorporated into the medical record to enable proper review; and 2) the remote consultant should only receive pertinent parts of the medical record to streamline the consultation process. This proposal is focused on developing and testing a "context-sensitive" telehealth infrastructure based on: 1) automated incorporation of clinical context (patient presentation and referring physician hypothesis) to focus the consultation process; 2) a knowledge-base derived from data mining of natural language processing (NLP) results, mapping patient presentation to select an appropriate imaging study based on anatomical region and imaging parameters; and 3) automated selection of key anatomical structures in the acquired imaging study through the use of a contrast-customizable atlas and rigid body/deformable registration algorithms. Collectively, these technologies will allow context-sensitive, automated summarization of medical records for telehealth in a real-world environment. The proposed technologies will be implemented for neurological and musculoskeletal domains, two areas that are MR imaging intensive. Technical evaluation will be performed with experts serving as the reference standard and will focus on measuring: 1) the accuracy of the corpus based, NLP-guided knowledge-base in selecting relevant anatomical structures; and 2) the accuracy of anatomical structure delineation using the customizable atlas registration methods. Clinical evaluation will be conducted in a real-world teleconsultation environment in a before/after study design using two performance metrics: 1) the time required for consultations; and 2) the effect on the quality of the consultations.

描述（由申请人提供）：咨询适当的专家，提高医疗质量，特别是在复杂的情况下或慢性疾病的患者。对于大多数这样的患者，专家使用成像研究（例如，MR、CT）客观记录疾病过程（例如，正在接受化疗的癌症患者）。然而，并非所有社区都有专家，他们往往集中在学术/专业中心。因此，为了在专家不在本地时促进对患者的远程咨询的常规使用：1）为了记录患者的状况而捕获的图像必须被并入到医疗记录中以使得能够进行适当的审查;以及2）远程咨询者应当仅接收医疗记录的相关部分以简化咨询过程。这项建议的重点是开发和测试一个“上下文敏感”的远程医疗基础设施的基础上：1）自动纳入临床背景（患者介绍和转诊医生假设），以集中咨询过程; 2）从自然语言处理（NLP）结果的数据挖掘导出的知识库，基于解剖区域和成像参数映射患者表现以选择适当的成像研究;以及3）通过使用对比度可定制的图谱和刚体/可变形配准算法，在所采集的成像研究中自动选择关键解剖结构。总的来说，这些技术将允许在现实环境中对远程医疗的医疗记录进行上下文敏感的自动总结。拟议的技术将用于神经和肌肉骨骼领域，这两个领域是磁共振成像密集型的。 将以专家作为参考标准进行技术评价，并将重点测量：1）基于语料库的NLP引导知识库在选择相关解剖结构时的准确性; 2）使用可定制图谱配准方法进行解剖结构描绘的准确性。临床评价将在真实世界远程会诊环境中进行，采用研究前/后设计，使用两个性能指标：1）会诊所需的时间; 2）对会诊质量的影响。