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: 10225327
• 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/10225327
• 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.

在计算，数据获取方面的最新进展中，向个性化医学迈进了 处理和解释，正在转变传统的诊断和介入医学 根据临床医生的经验，手工艺是依赖基于客观决策的学科 关于来自异质来源的多维和多模式数据的整合。电脑- 综合诊断和介入数据科学涵盖了处理，分析和解释 图像和信号以提高诊断或治疗目标的质量。改进结果 帮助临床医生更好地诊断性疾病，预测临床结果，更好的计划，提供和监测治疗，因为 以及提前培训和模拟。尽管在计算机集成诊断方面取得了进步 在过去的十年中，将大规模数据科学技术引入诊断和 特别是介入医学。尽管这些学科的出现已经改变了 数字成像（即组织学，病理学和显微镜），微型摄像机（即内窥镜和多摄像头） 模态医学成像，以“看到”人体内部，无缝的，广泛的计算机整合 - 作为常规诊断和手术环境的一部分的辅助工具速度很慢。这个延迟已经 归因于诊断和介入数据科学技术的有限可用性 处理经常实时操纵所获得的数据的大小，多样性和维度。 我实验室中正在进行的项目集中于基于图像的计算的开发和验证， 1）建模和可视化框架，可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）识别和规划最佳治疗途径，3）指南， 监测并在侵入性较小的情况下提供治疗。这些工具已经开发 主要在心脏应用，骨科，肺，大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。拟议程序的长期愿景是 通过继续开发，进一步推进了计算机集成的诊断和治疗数据科学 以及用于生物医学计算和可视化的新技术的验证。我们将利用我们的成功 并扩展我们现有的计算基础架构以在更广泛的数字数据范围内运行。他们的输出意愿 向临床医生提供必要的可视化，以进行诊断和治疗决策 不同的组织和器官。我们将使开发的技术可用于生物医学研究和 社区使用基于图像的建模，仿真和可视化进行的研究以及 可以促进其临床翻译的临床科学家。该研究计划将产生创新的 依靠标准生物医学数据并迎合A的生物医学计算和可视化工具 广泛的微创诊断和治疗应用。