Biomedical Computing and Visualization Tools for Computer-integrated Diagnostic and Therapeutic Data Science

用于计算机集成诊断和治疗数据科学的生物医学计算和可视化工具

• 批准号: 9980421
• 负责人: Cristian A Linte
• 金额: $ 35.77万
• 依托单位: ROCHESTER INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980421
• 关键词: Biomedical Computing; Biomedical Research; Brain; Cardiac; Collaborations; Communities; Computer Assisted; Computers; Data; Data Science; Decision Making; Development; Diagnosis; Diagnostic; Dimensions; Discipline; Disease; Disease Progression; Endoscopy; Environment; Felis catus; Goals; Health; Histology; Human body; Image; Image Analysis; Infrastructure; Intervention; Lung; Medical Imaging; Medicine; Methodology; Microscopy; Modality; Modeling; Monitor; Operative Surgical Procedures; Organ; Orthopedics; Output; Pathology; Research; Route; Scientist; Signal Transduction; Source; Techniques; Therapeutic; Time; Tissues; Training; Validation; Vertebral column; Vision; Visualization; Visualization software; base; clinical translation; data acquisition; digital; digital imaging; disease diagnosis; experience; imaging biomarker; improved; industry partner; innovation; large scale data; minimally invasive; models and simulation; multidimensional data; multimodal data; multimodality; novel; personalized medicine; predict clinical outcome; programs; simulation; standard of care; success; tool

The move toward personalized medicine, in concert with the recent advances in computing, data acquisition, processing and interpretation, is transforming diagnostic and interventional medicine from a traditional artisanal craft based on clinicians’ experience into a discipline that relies on objective decision-making based on the integration of multi-dimension and multi-modal data from heterogeneous sources. Computer- integrated diagnostic and interventional data science encompasses the processing, analysis, and interpretation of images and signals to improve the quality of a diagnostic or therapeutic goal. Improvements result from helping clinicians better diagnose disease, predict clinical outcome, better plan, deliver and monitor therapy, as well as advance training and simulation. Despite advances in computer-integrated diagnosis the therapy during the past decade, there has been a delay in introducing large-scale data science techniques into diagnostic, and especially interventional medicine. Although these disciplines have been transformed by the emergence of digital imaging (i.e., histology, pathology, and microscopy), miniature cameras (i.e., endoscopy, and multi- modality medical imaging to “see” inside the human body, the seamless, wide-spread integration of computer- aided tools as part of the routine diagnostic and surgical environment has been slow. This delay has been attributed to the limited availability of diagnostic and interventional data science techniques that can robustly handle the size, diversity and dimensionality of the acquired data that must be manipulated, often in real time. Ongoing projects in my lab have focused on the development and validation of image-based computing, modeling, and visualization frameworks that 1) help clinicians quantify and track imaging biomarkers to diagnose and monitor disease progression, 2) identify and plan optimal therapeutic routes, and 3) guide, monitor, and deliver therapy under less invasive conditions. These tools have been developed and demonstrated primarily in the context of cardiac applications, orthopedic, lung, brain, and spine applications, in close collaborations with clinicians and industry partners. The long-term vision of the proposed program is to further advance computer-integrated diagnostic and therapeutic data science by continuing the development and validation of new techniques for biomedical computing and visualization. We will leverage our successes and extend our existing computing infrastructure to operate on a wider range of digital data. Their output will supply clinicians with the necessary visualization for diagnostic and therapeutic decision making across different tissues and organs. We will make the developed techniques available to the biomedical research and community whose research necessitates using image-based modeling, simulation, and visualization, as well as to clinician scientists who can promote their clinical translation. This research program will yield innovative biomedical computing and visualization tools that rely on standard-of-care biomedical data and cater to a broad range of minimally invasive diagnosis and therapy applications.

个人化医疗的发展，与计算机、数据采集、 处理和解释，正在改变诊断和介入医学从传统的 将基于临床医生经验的手工艺转变为依赖于基于客观决策的学科 集成来自异构源的多维和多模态数据。电脑- 综合诊断和介入数据科学包括处理、分析和解释 以提高诊断或治疗目标的质量。改进源于 帮助临床医生更好地诊断疾病，预测临床结果，更好地计划，提供和监测治疗， 以及高级培训和模拟。尽管计算机集成诊断取得了进展， 在过去的十年中，在将大规模数据科学技术引入诊断方面出现了延迟， 尤其是介入医学。虽然这些学科已经被改变的出现， 数字成像（即，组织学、病理学和显微镜），微型照相机（即，内窥镜检查和多- 模态医学成像“看到”人体内部，计算机的无缝，广泛的集成， 辅助工具作为常规诊断和外科手术环境的一部分一直进展缓慢。这一拖延已 这归因于诊断和介入数据科学技术的可用性有限， 处理所获取的数据的大小、多样性和维度，这些数据必须被处理，通常是真实的时间。 我实验室正在进行的项目主要集中在基于图像的计算的开发和验证， 建模和可视化框架，1）帮助临床医生量化和跟踪成像生物标志物， 诊断和监测疾病进展，2）识别和计划最佳治疗途径，和3）指导， 监测，并在侵入性较小的条件下提供治疗。这些工具已经开发出来， 主要在心脏应用、整形外科、肺、脑和脊柱应用的背景下证明， 与临床医生和行业合作伙伴密切合作。该计划的长期愿景是 通过继续开发，进一步推进计算机集成诊断和治疗数据科学 以及验证生物医学计算和可视化的新技术。我们将利用我们的成功 并扩展我们现有的计算基础设施，以处理更广泛的数字数据。其产量将 为临床医生提供诊断和治疗决策所需的可视化， 不同的组织和器官。我们将把开发的技术用于生物医学研究， 社区，其研究需要使用基于图像的建模，仿真和可视化，以及 临床科学家可以促进他们的临床翻译。这项研究计划将产生创新 生物医学计算和可视化工具，依赖于护理标准生物医学数据，并满足 广泛的微创诊断和治疗应用。