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

• 批准号: 10719474
• 负责人: Scarlett Miller
• 金额: $ 47.19万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10719474
• 关键词: 3-Dimensional; Adult; American; Anatomy; Benchmarking; Cancer Detection; Cecum; Characteristics; Colon; Colon Carcinoma; Colonoscopy; Colorectal Cancer; Complex; Computed Tomographic Colonography; Computer Simulation; Data; Data Set; Detection; Development; Device or Instrument Development; Diagnosis; Disease; Education; Endoscopes; Evaluation; Feedback; Friction; Geometry; Goals; Health; Health Care Costs; Hospitalization; Intubation; Knowledge; Learning; Light; Malignant Neoplasms; Manikins; Measures; Medical; Medical Education; Medical Residency; Medical center; Methods; Modeling; Operative Surgical Procedures; Organ; Patients; Physicians; Polyps; Procedures; Research Personnel; Residencies; System; Technical Expertise; Techniques; Testing; The Cancer Imaging Archive; Time; Tissues; Trainers Training; Training; Training Programs; Training Technics; Trauma; Validation; Variant; Visualization; Work; adenoma; aged; cost; design; health care quality; human study; improved; innovation; instrument; interactive feedback; iterative design; large datasets; minimally invasive; novel; patient safety; personalized learning; programs; screening; sensor; simulation; skills; success; user-friendly; virtual assessment; virtual coach; virtual patient

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.

项目概要/摘要 美国每年进行超过 1900 万例结肠镜检查，其中 60% 的成年人年龄在 55 岁至 75 岁之间 过去十年内至少有一次 (1-3) 这些手术为医生提供了最低限度的治疗。 结肠镜检查是诊断和治疗结肠癌等疾病的常规手段。 通常对健康的美国人进行昂贵的筛查测试，平均成本为 2,125 美元 (4)。 成本是根据医生的技能而发生的成功完成率的巨大差异——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 数百个患者病例（5-7）。 有鉴于此，研究人员开发了人体模型高级反馈训练器（MAFT）来改善 通过高效的结肠镜检查培训，改善患者健康并降低医疗成本。 使用高保真传感模型训练器和使用基于优化的虚拟“教练”制作 通过优化建模开发的反馈是通过以下 3 个目标严格开发的： 具体目标 1：开发并验证广泛的结肠几何数据集和完善的结肠几何数据集 用于来自 The Cancer 的 959 名患者虚拟结肠镜检查的 MAFT 结肠几何形状的传​​感系统。 成像档案 (TCIA) 将通过用户友好的自动化器官几何形状进行计算测量 此外，该目标还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 优化模型以求解最优 对于每个特定的结肠几何形状，都存在一组最佳的内窥镜控制。 转向控制，以在最佳时间和最少的时间内成功插入内窥镜到达盲肠 将开发一个优化模型来解决这个最优问题，然后该程序将被开发。 在大型冒号数据集上执行。 具体目标 3：开发并通过实验评估虚拟教练，以提供个性化和 模型高级反馈训练器的渐进式学习假设 (H1)：MAFT 系统。 虚拟教练提供的交互式反馈将提高医生的盲肠插管率（CIR）， 通过改进可视化来提高腺瘤检出率 (ADR)，并减少可能导致腺瘤检出率 (ADR) 的内窥镜力 虚拟教练将通过人体研究 1 和 2 进行开发和验证。 在人类研究 3 中，主治医生和住院医师将在手术中实施 MAFT。 好时医疗中心第四年和第五年的住院医师计划，以及住院医师进行结肠镜检查的技能 将根据 CIR、ADR 和 GAGES 评估的既定指标进行评估，以验证 H1。