Development and Validation of an Artificial Intelligence-Based Clinical Decision Support Tool for Videofluoroscopic Swallowing Studies

用于视频透视吞咽研究的基于人工智能的临床决策支持工具的开发和验证

• 批准号: 10679097
• 负责人: Bryan Patrick Bednarz
• 金额: $ 22.66万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-08 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10679097
• 关键词: 3D Print; Affect; Age Years; Algorithms; Anatomy; Artificial Intelligence; Barium; Biomechanics; Bolus Infusion; Classification; Clinic; Clinical; Computer software; Consumption; Data; Data Set; Deglutition; Deglutition Disorders; Dehydration; Development; Diagnosis; Diagnostic Procedure; Elderly; Environment; Etiology; Functional disorder; Future; Goals; Head and Neck Cancer; Head and neck structure; Health; Health care facility; Human; Image; Impairment; Inpatients; Left; Length of Stay; Lung; Malnutrition; Manuals; Masks; Measures; Medical; Methods; Morphologic artifacts; Nature; Network-based; Neurodegenerative Disorders; Oral cavity; Outcome; Output; Patient imaging; Patients; Pharyngeal structure; Physiology; Pneumonia; Prevalence; Procedures; Quality of life; Reference Values; Research; Resources; Retrospective cohort; Speed; Stroke; Structure; Techniques; Time; United States; Validation; Visualization; Work; acute care; artificial intelligence method; artificial neural network; aspirate; automated segmentation; catalyst; clinical decision support; clinical decision-making; clinical practice; clinically relevant; contrast enhanced; convolutional neural network; cost; design; experience; hospital readmission; image processing; improved; interest; mortality; novel; radiological imaging; segmentation algorithm; support tools; tool

ABSTRACT Dysphagia (swallowing dysfunction) is highly prevalent in a variety of medical conditions and prevalence increases with advancing age. If incorrectly diagnosed or left untreated, dysphagia can lead to serious health consequences, including malnutrition, dehydration, and pneumonia. The most commonly used procedure to diagnose dysphagia is the videofluoroscopic swallow (VFS) study. A VFS study utilizes barium to provide a contrast enhanced fluoroscopic procedure that allows for visualization of anatomy and physiology relevant to swallowing as well as identification of swallowing biomechanical impairments. Current VFS analysis methods used clinically are primarily qualitative in nature and subject to issues with reliability. Quantitative methods to support VFS clinical interpretation do exist but are primarily found in the research environment due to the time- consuming nature of frame by frame analysis required. The overall objective of this application is to develop and validate an artificial neural network-based software that will segment and track clinically important swallowing structures on a frame-by-frame basis within swallowing videos. Segmentation and tracking will automatically occur post acquisition with no needed input or video editing. Frame by frame auto-segmentation of regions of interest will allow for quantitative metrics to be determined algorithmically. To accomplish this objective, two specific aims are proposed: 1) to develop and validate an AI based auto-segmentation algorithm that accurately segments swallowing anatomy and bolus flow in VFS studies from a retrospective cohort of stroke and mixed etiology patients and 2) to apply the auto-segmentation algorithm to derive a variety of clinically relevant metrics in VFS studies and compare to manually derived reference values. To accomplish the first aim, pre-processing techniques will be established to improve image quality and reduce image artifacts using a novel 3D printed anthropomorphic head & neck phantom. Using a robust existing dataset of VFS images, we will then develop a Mask R-Convolutional Neural Network for automatic segmentation of a variety of clinically relevant features on VFS studies and will validate the auto-segmentation against manually derived segmentation. For the second aim, the auto-segmentation algorithm will be applied to derive important swallowing measures and associated metrics from the VFS images. The output of the algorithm will be validated against measures and metrics manually derived from the VFS images by experienced raters with established reliability. Tools developed through this project will reduce the subjectivity of human interpretation of VFS images, which will improve consistency and reliability of dysphagia diagnosis and treatment.

抽象的 吞咽困难（吞咽功能障碍）在各种医疗状况下都很常见，而且患病率很高 随着年龄的增长而增加。如果诊断不正确或不及时治疗，吞咽困难可能会导致严重的健康问题 后果，包括营养不良、脱水和肺炎。最常用的程序是 诊断吞咽困难的方法是电视透视吞咽 (VFS) 研究。 VFS 研究利用钡来提供 对比增强荧光镜检查程序，可实现与相关解剖学和生理学的可视化 吞咽以及吞咽生物力学损伤的识别。当前的VFS分析方法 临床上使用的主要是定性的，并且存在可靠性问题。定量方法 支持 VFS 临床解释确实存在，但由于时间原因主要在研究环境中发现 需要逐帧分析的消耗性质。该应用程序的总体目标是开发 并验证基于人工神经网络的软件，该软件将分割和跟踪临床上重要的内容 吞咽视频中逐帧吞咽结构。细分和跟踪将 采集后自动发生，无需输入或视频编辑。逐帧自动分割 感兴趣区域的数量将允许通过算法确定定量指标。为了实现这一点 为了实现这一目标，提出了两个具体目标：1）开发并验证基于人工智能的自动分割算法 在 VFS 研究中从回顾性队列中准确分割吞咽解剖结构和推注流量 中风和混合病因患者，2) 应用自动分割算法得出各种 VFS 研究中的临床相关指标，并与手动得出的参考值进行比较。为了完成 第一个目标是建立预处理技术来提高图像质量并减少图像伪影 使用新颖的 3D 打印拟人头颈模型。使用强大的现有 VFS 数据集 图像，然后我们将开发一个 Mask R 卷积神经网络，用于自动分割各种图像 VFS 研究的临床相关特征，并将根据手动得出的结果验证自动分割 分割。对于第二个目标，将应用自动分割算法来导出重要的 来自 VFS 图像的吞咽测量和相关指标。算法的输出将是 由经验丰富的评估者根据从 VFS 图像手动得出的度量和指标进行验证 建立的可靠性。通过该项目开发的工具将减少人类解释的主观性 VFS 图像，这将提高吞咽困难诊断和治疗的一致性和可靠性。