A Context-Sensitive Teleconsultation Infrastructure

上下文敏感的远程会诊基础设施

• 批准号: 6802269
• 负责人: HOOSHANG KANGARLOO
• 金额: $ 38.78万
• 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/6802269
• 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）：与适当的专家进行咨询可以提高医疗保健质量，特别是对于患有复杂病例或慢性疾病的患者。对于大多数这样的患者，专家使用成像研究（例如，MR， CT）客观地记录疾病过程（例如，接受化疗的癌症患者）。然而，专家通常不是在所有社区都有，往往集中在学术/专业中心。因此，当专家不在现场时，为了方便患者常规使用远程会诊：1)为记录患者病情而捕获的图像必须纳入医疗记录，以便进行适当的审查；2)远程会诊者应该只接收病历的相关部分，以简化会诊过程。该提案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，其基础是：1)自动合并临床上下文（患者陈述和转诊医生假设），以集中咨询过程；2)基于自然语言处理（NLP）结果数据挖掘的知识库，基于解剖区域和成像参数映射患者表现以选择合适的成像研究；3)通过使用对比自定义图谱和刚体/可变形配准算法，在获得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世界环境中为远程医疗提供上下文敏感的自动医疗记录摘要。提出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。