Software for stent failure prevention using intravascular OCT images

使用血管内 OCT 图像预防支架失效的软件

• 批准号: 10685081
• 负责人: Ronny Shalev
• 金额: $ 28.86万
• 依托单位: DYAD MEDICAL, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10685081
• 关键词: Address; Adoption; Affect; Agreement; Algorithms; Atherectomy; Benchmarking; Blood Vessels; Blood coagulation; Blood flow; Calcium; Cardiac; Cardiac Catheterization Procedures; Cause of Death; Cicatrix; Classification; Clinical; Computer software; Consumption; Coronary; Coronary Arteriosclerosis; Coronary artery; Data; Data Set; Decision Making; Deposition; Development; Disease; Dissection; Environment; Equipment; Event; External Elastic Membrane; Failure; Goals; Health Care Costs; Hospitals; Image; Image Analysis; Intelligence; Intervention; Interventional Imaging; Israel; Label; Learning; Legal patent; Lesion by Morphology; Link; Lipids; Location; Manuals; Maps; Marketing; Measurement; Medical; Medical Imaging; Medical center; Metals; Methods; Modernization; Modification; Morbidity - disease rate; Morphologic artifacts; Morphology; Optical Coherence Tomography; Outcome; Patient-Focused Outcomes; Patients; Performance; Phase; Prevention; Procedures; Reader; Resolution; Risk Factors; Sensitivity and Specificity; Site; Software Tools; Stents; System; Techniques; Testing; Thrombosis; Time; Tissues; Training; Validation; Visual Pattern Recognition; Visualization; Visualization software; automated analysis; clinical decision-making; clinical imaging; clinical practice; cloud platform; cohort; deep learning; deep learning algorithm; design; disability; heart imaging; image guided; imaging software; implantation; improved; in vivo; innovation; interest; machine learning algorithm; mortality; novel; novel strategies; percutaneous coronary intervention; point of care; real-time images; restenosis; success; tool; ultrasound; validation studies

Summary We will develop an automated software engine for plaque characterization and EEM segmentation using intra- vascular OCT (IVOCT) images, creating a powerful tool in order to efficiently guide stent implantation during PCI. Coronary artery disease (CAD) and its clinical complications are a leading cause of death, disability, and escalating healthcare costs worldwide. Contemporary CAD treatment frequently involves percutaneous coro- nary intervention (PCI) with metal stents in order to resolve obstructive blockages that impede coronary blood flow. While PCI has proven to be effective, subsequent stent failure from restenosis (scar tissue) and throm- bosis (blood clotting) limit the durability of PCI results and are associated with significant morbidity and mortali- ty. Moreover, stent failure has been specifically linked to inadequate stent expansion, incomplete stent cover- age of diseased segments, and untreated dissections at the stent edges. Recently, high-resolution intravascular imaging guidance during PCI has been demonstrated to reduce ad- verse cardiac events by optimizing stent implantation and mitigating structural risks factors for stent failure. However, despite improved patient outcomes, intravascular imaging using ultrasound (IVUS) and optical co- herence tomography (IVOCT) remain severely underutilized in clinical practice. In part, intravascular imaging adoption has been hampered by the need for operators with variable proficiency in image interpretation to per- form manual image analysis on a large volume of data in real-time during the PCI procedure. This creates a scenario where difficulty in image interpretation and an overload of image data (270-500 image frames in a single pull-back) may lead to clinical decision making that relies on incomplete information. We will build upon significant preliminary results and create robust, highly automated methods for identify- ing calcium and lipid deposits in IVOCT image pullbacks, as well as true vessel sizing by automatically deter- mining the location of the EEM. We will: (1) Acquire and label a large, unique dataset of in-vivo IVOCT image volumes. (2) Develop modern machine-learning algorithms for plaque classification and compare against car- diologist readers. (3) Conduct a retrospective validation study to determine how IVOCT with plaque visualiza- tion might affect clinical interventions. (4) Deploy our solution on our cloud platform, LibbyTM, making the soft- ware accessible world-wide, facilitating multinational usage and on-going validation and refinement. We antici- pate that our software will: (1) determine significant lipid and calcium deposits as good as, or better than, ex- pert analysts; (2) incorporate the generated data efficiently into clinical workflow by enhancing pre-PCI imaging to comprehensively map plaque morphology and define appropriate stent landing zones; (3) inform operators on the need for specialized plaque modification techniques such as cutting/scoring balloons or atherectomy; and (4) reliably automate stent sizing by EEM measurements to streamline equipment selection.

总结 我们将开发一个自动化的软件引擎，用于斑块表征和EEM分割， 血管OCT（IVOCT）图像，创建了一个强大的工具，以便在手术期间有效引导支架植入。 PCI。冠状动脉疾病（CAD）及其临床并发症是导致死亡、残疾和死亡的主要原因。 全球医疗保健成本不断上升。现代CAD治疗经常涉及经皮科罗- 使用金属支架进行无创介入（PCI），以解决阻碍冠状动脉血液的阻塞性阻塞 流虽然经皮冠状动脉介入治疗已被证明是有效的，随后的支架失败的再狭窄（疤痕组织）和throm- 血栓（血液凝固）限制了PCI结果的持久性，并与显著的发病率和死亡率相关， 泰此外，支架失效与支架扩张不充分、支架覆盖不完整- 病变节段的年龄和支架边缘未治疗的夹层。 最近，经皮冠状动脉介入治疗期间的高分辨率血管内成像引导已被证明可以减少ad- 通过优化支架植入和减轻支架失效的结构性风险因素， 然而，尽管改善了患者的预后，但使用超声（IVUS）和光学共聚焦的血管内成像仍然是一种新的方法。 相干断层扫描（IVOCT）在临床实践中仍然严重未得到充分利用。血管内成像 由于需要操作员在图像判读方面具有不同的熟练程度， 在PCI过程中实时对大量数据进行手动图像分析。这将创建一个 图像解释困难和图像数据过载（270-500个图像帧， 单回撤）可能导致依赖于不完整信息的临床决策。 我们将建立在重要的初步结果，并创建强大的，高度自动化的方法来识别- IVOCT图像回撤中的钙和脂质沉积，以及通过自动确定 挖掘电磁场的位置我们将：（1）采集并标记体内IVOCT图像的大型唯一数据集 卷(2)开发用于斑块分类的现代机器学习算法，并与汽车进行比较， 生物学家读者(3)进行一项回顾性验证研究，以确定IVOCT与斑块可视化- 可能影响临床干预。(4)在我们的云平台LibbyTM上部署我们的解决方案， 软件可在全球范围内使用，便于多国使用和不断验证和完善。我们反对- 我们的软件将：（1）确定显著的脂质和钙沉积，与前一种一样好或更好， 专家分析师;（2）通过增强PCI前成像，将生成的数据有效地纳入临床工作流程 全面绘制斑块形态并定义适当的支架着陆区;（3）告知操作者 需要专门的斑块修饰技术，如切割/刻痕球囊或斑块切除术; 和（4）通过EEM测量可靠地自动化支架尺寸以简化设备选择。