Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis

使用机器学习实时支气管镜定位来改善肺癌诊断

• 批准号: 10676966
• 负责人: Inbar Fried
• 金额: $ 4.59万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676966
• 关键词: 3-Dimensional; 3D Print; Accounting; Address; Algorithms; Anatomy; Area; Biopsy; Bronchoscopes; Bronchoscopy; Cancer Etiology; Cessation of life; Clinical; Clinical Skills; Complication; Computational algorithm; Computer Simulation; Computer Vision Systems; Coupled; Data; Data Set; Devices; Diagnosis; Diagnostic; Diameter; Disease; Distal; Drops; Early Diagnosis; Electromagnetics; Ensure; Environment; Equipment; Family suidae; Freedom; Frequencies; Future; Health Benefit; Hour; Human; Individual; Lesion; Localized Disease; Localized Lesion; Location; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Medical; Medicine; Mentors; Minor; Modeling; Morbidity - disease rate; Motion; Needles; Neoplasm Metastasis; Neural Network Simulation; Nodule; North Carolina; Operative Surgical Procedures; Patient observation; Patients; Physicians; Positioning Attribute; Preparation; Procedures; Psyche structure; Public Health; Puncture procedure; Radial; Reproducibility; Research; Robot; Robotics; Scientist; Secondary to; Site; Survival Rate; System; Techniques; Technology; Time; Tissues; Trachea; Training; United States; Universities; Woman; Work; X-Ray Computed Tomography; cancer diagnosis; career; chest computed tomography; computer science; data-driven model; deep neural network; experience; experimental study; follow-up; improved; in vivo; laboratory experiment; large datasets; lung cancer screening; lung lesion; machine learning model; men; millimeter; minimally invasive; mortality; real time model; screening guidelines; simulation; skills; software development; success; tool; ultrasound; virtual; virtual human

Project Summary/Abstract Lung cancer 5-year survival rates drop from 61% for early stage diagnosis to just 6% for late stage diagnosis. Currently, fewer than 1 in 5 cases are diagnosed at an early stage. The increasing frequency of chest CT scans and changes in lung cancer screening guidelines are expected to increase the number of incidentally discovered lung lesions, representing an opportunity for earlier lung cancer diagnosis. Bronchoscopy is currently the safest, least invasive, and least expensive diagnostic option, but its poor diagnostic yield greatly limits its procedural benefit. Even when advanced techniques like radial endobronchial ultrasound and electromagnetic navigation are used, the diagnostic yield is just 50-60%. This is primarily due to challenges with intraoperative localization of the bronchoscope prior to needle deployment. Additionally, access to these techniques is limited because they require expensive equipment and unique expertise. Efforts relying on the bronchoscope's built-in camera require no additional equipment or specialization, but have struggled with generalizability across individuals in part due to limited data availability and assumptions about airway features. The objective of this proposal is to improve the success rate of traditional bronchoscopes by addressing limita- tions in intraoperative localization using a data-driven model that is robust to differences in human anatomy. This work has potential for significant public health benefit by (1) increasing early lung cancer detection, (2) reducing morbidity and mortality by reducing the number of invasive procedures, and (3) making minimally invasive bron- choscopy more accessible in areas without expert bronchoscopists. The proposed work will be accomplished via two Specific Aims. In Aim 1, a dataset will be generated of virtual and real bronchoscopy videos with video-frame matched six degrees-of-freedom poses (position and orientation in three-dimensions) of the bronchoscope's dis- tal tip. This data will be made publicly available as the first large dataset of its kind to promote future research and reproducibility. In Aim 2, a real-time bronchoscope localization model will be developed using advances in ma- chine learning, including deep neural networks, that have shown success in camera localization for non-medical applications. These models will regress the pose of the bronchoscope's distal tip using current and past video frames of the bronchoscope's built-in camera. The clinical utility of the system will be evaluated in simulation, 3D printed lung phantoms, and ex-vivo porcine lung experiments. The research, tightly coupled clinical experience, and associated training plan will provide a unique interdisciplinary skill-set in computer science, medical robotics, and procedural medicine. The outstanding research and clinical environment for this training at the University of North Carolina at Chapel Hill ensures exceptional preparation for a career conducting cutting-edge research as a physician-scientist in medical robotics.

项目摘要/摘要 肺癌5年生存率从早期诊断的61％下降到后期诊断的6％。 目前，在早期诊断出5例中不到1例。胸部CT扫描的频率增加 预计肺癌筛查指南的变化将增加偶然发现的数量 肺部病变，代表了早期肺癌诊断的机会。支气管镜检查目前是最安全的， 至少侵入性且最不昂贵的诊断选择，但其诊断不佳会极大地限制了其程序性 好处。即使径向支气管超声和电磁导航等高级技术 使用，诊断产量仅为50-60％。这主要是由于术中定位的挑战 针头部署之前的支气管镜。此外，访问这些技术是有限的，因为它们 需要昂贵的设备和独特的专业知识。依靠支气管镜的内置相机的努力需要 没有其他设备或专业化 有限的数据可用性和关于气道功能的假设。 该提案的目的是通过解决极限来提高传统支气管镜的成功率 使用数据驱动的模型在术中定位中的特性，该模型可与人体解剖结构的差异相差。这 工作有可能通过（1）增加肺癌检测来实现重要的公共卫生利益；（2）减少 通过减少侵入性程序的数量来发病和死亡率，（3） Choscopy在没有专业支气管镜医生的地区更容易获得。拟议的工作将通过 两个特定目标。在AIM 1中，将通过视频框架生成虚拟和真实支气管镜镜检查视频的数据集 匹配支气管镜的六个自由度位置（三维位置和方向） TAL提示。这些数据将作为此类数据公开提供，以促进未来的研究和 可重复性。在AIM 2中，将使用MA-的进步开发实时支气管镜定位模型 Chine学习，包括深神经网络，在非医学方面显示了相机定位的成功 申请。这些模型将使用当前和过去的视频来回归支气管镜的独特尖端的姿势 支气管镜的内置相机的框架。该系统的临床实用性将在模拟中评估3D 印刷的肺幻像和前猪肺肺实验。这项研究，紧密结合的临床经验， 相关的培训计划将在计算机科学，医学机器人技术中提供独特的跨学科技能， 和程序医学。该大学培训的杰出研究和临床环境 北卡罗来纳州教堂山（Chapel Hill 医学机器人技术的身体科学家。