Advanced Colonoscopy Training Developed Through Manikin Sensorization and Computational Optimization Modeling

通过人体模型传感和计算优化建模开发的高级结肠镜检查培训

基本信息

  • 批准号:
    10719474
  • 负责人:
  • 金额:
    $ 47.19万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-07-01 至 2027-04-30
  • 项目状态:
    未结题

项目摘要

Project Summary/Abstract More than 19 million colonoscopies are performed in the US each year, with 60% of adults between 55-75 years having at least one within the last decade (1-3). These procedures provide physicians with minimally invasive access to diagnose and treat diseases such as colon cancer. While routine, colonoscopies are the most expensive screening test routinely performed on healthy Americans, with a mean cost of $2,125 (4). Furthering the cost is the wide variation in successful completion rates that occur based on the physician skill– While the acceptable physician success rate is only 90%, this modest target today is only achieved after physicians complete hundreds of patient cases (5-7). In light of this, the investigators have developed the Manikin Advanced Feedback Trainer (MAFT) to improve patient health and reduce healthcare costs through efficient and effective colonoscopy training. Efficient learning is produced using a high fidelity sensorized manikin trainer and a virtual “coach” that uses optimization-based feedback developed through optimization modeling. MAFT is rigorously developed through 3 Aims below: Specific Aim 1: Develop and validate an extensive colon geometry dataset and a refined sensorization system for the MAFT. Colon geometry of 959 patient virtual colonoscopies from The Cancer Imaging Archive (TCIA) will be computationally measured with a user-friendly automated organ geometry centerline program. In addition, this aim will advance MAFT through the refinement of sensor processing. Specific Aim 2: Develop, validate, and execute a 3D optimization model to solve for optimal endoscope steering control. For each specific colon geometry there exists an optimal set of endoscope steering controls to successfully insert an endoscope to reach the cecum in optimal time and with minimal trauma. An Optimization model will be developed to solve for this optimum, and then this program will be executed across the large colon dataset. Specific Aim 3: Develop and experimentally assess virtual coach to provide personalized and progressive learning for the Manikin Advanced Feedback Trainer. Hypothesis (H1): MAFT system of interactive feedback provided by a virtual coach will improve a physician’s cecal intubation rate (CIR), Adenoma Detection Rate (ADR) through improved visualizations, and reduce endoscope forces that can cause colon damage. The virtual coach will be developed and validated through Human Study 1 and 2 that utilizes both attending physicians and residents. In Human Study 3 MAFT will be implemented into the surgical residency program at Hershey Medical Center in Years 4 and 5, and residents’ skills at performing colonoscopy will be assessed based on the well-established metrics of CIR, ADR, and GAGES assessment to validate H1.
项目总结/摘要 每年在美国进行超过1900万次结肠镜检查,其中60%的成年人在55-75岁之间 在过去的十年中至少有一个(1-3)。这些程序为医生提供了最低限度的 诊断和治疗结肠癌等疾病的侵入性途径。结肠镜检查虽然是常规检查, 常规对健康美国人进行的昂贵的筛查测试,平均费用为2,125美元。促进 成本是成功完成率的广泛变化,这是基于医生的技能-而 可接受的医生成功率仅为90%,今天这个适度的目标只有在医生完成后才能实现 数百例患者病例(5-7例)。 鉴于此,研究人员开发了Manikin高级反馈训练器(MAFT),以提高 通过高效和有效的结肠镜检查培训,确保患者健康并降低医疗成本。高效学习 是使用高保真传感器假人教练和虚拟“教练”,使用基于优化的 通过优化建模开发的反馈。MAFT通过以下3个目标严格开发: 具体目标1:开发和验证广泛的结肠几何数据集和改进的 用于MAFT的传感系统。来自The Cancer的959例患者虚拟结肠镜检查的结肠几何结构 将使用用户友好的自动器官几何学对成像档案(TCIA)进行计算测量 中心线计划此外,这一目标将通过改进传感器处理来推进MAFT。 具体目标2:开发、验证和执行3D优化模型,以解决 内窥镜转向控制。对于每个特定的结肠几何形状,存在一组最佳的内窥镜 操纵控制,以成功插入内窥镜,在最佳时间到达盲肠, 外伤将开发一个优化模型来求解此最优值,然后将此程序 在大型冒号数据集上执行。 具体目标3:开发和实验评估虚拟教练,以提供个性化和 Manikin Advanced Feedback Trainer的渐进式学习。假设(H1):MAFT系统 由虚拟教练提供的交互式反馈将提高医生的盲肠插管率(CIR), 通过改善可视化提高腺瘤检出率(ADR),并减少可能导致 结肠损伤虚拟教练将通过人体研究1和2进行开发和验证, 包括主治医生和住院医生在人体研究3中,MAFT将在外科手术中实施。 好时医疗中心第4年和第5年的住院医师项目,以及住院医师进行结肠镜检查的技能 将根据CIR、ADR和GAGES评估的成熟指标进行评估,以确认H1。

项目成果

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Scarlett Miller其他文献

Scarlett Miller的其他文献

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{{ truncateString('Scarlett Miller', 18)}}的其他基金

Dynamic Haptic Robotic Training for Central Venous Catheter Insertion
中心静脉导管插入的动态触觉机器人训练
  • 批准号:
    10449320
  • 财政年份:
    2015
  • 资助金额:
    $ 47.19万
  • 项目类别:
Dynamic Haptic Robotic Training for Central Venous Catheter Insertion
中心静脉导管插入的动态触觉机器人训练
  • 批准号:
    10240321
  • 财政年份:
    2015
  • 资助金额:
    $ 47.19万
  • 项目类别:
Dynamic Haptic Robotic Training for Central Venous Catheter Insertion
中心静脉导管插入的动态触觉机器人训练
  • 批准号:
    9038433
  • 财政年份:
    2015
  • 资助金额:
    $ 47.19万
  • 项目类别:
Dynamic Haptic Robotic Training for Central Venous Catheter Insertion
中心静脉导管插入的动态触觉机器人训练
  • 批准号:
    9816965
  • 财政年份:
    2015
  • 资助金额:
    $ 47.19万
  • 项目类别:

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