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.

摘要 吞咽困难(吞咽功能障碍)在各种医疗条件和流行率中非常普遍。 随着年龄的增长而增加。如果诊断错误或不治疗，吞咽困难会导致严重的健康问题。 后果，包括营养不良、脱水和肺炎。最常用的过程是 吞咽困难的诊断是视频透视吞咽(VFS)研究。一项VFS研究利用钡来提供 对比度增强的透视程序，允许可视化相关的解剖和生理学 吞咽以及吞咽生物力学损伤的鉴定。当前的VFS分析方法 临床使用主要是定性的，并受到可靠性问题的影响。定量的方法来 支持VFS临床解释确实存在，但主要是在研究环境中发现的，因为时间- 需要逐帧分析的消耗特性。这个应用程序的总体目标是开发 并验证一种基于人工神经网络的软件，该软件将对临床上重要的疾病进行分段和跟踪 在吞咽视频中逐帧吞咽结构。细分和跟踪将 自动执行采集后操作，无需输入或编辑视频。逐帧自动分割 感兴趣区域的范围将允许通过算法确定定量指标。要做到这一点 目的：1)开发并验证一种基于人工智能的自动分割算法 准确地分割VFS研究中的吞咽解剖和推注血流 中风和混合病因患者和2)应用自动分割算法推导出各种 VFS研究中与临床相关的指标，并与手动得出的参考值进行比较。要完成 第一个目标是建立预处理技术，以提高图像质量并减少图像伪影 使用了一种新的3D打印拟人化的头颈部模型。使用健壮的现有VFS数据集 然后，我们将开发一种MASK R-卷积神经网络来自动分割各种 VFS研究的临床相关特征，并将对照手动得出的数据验证自动分割 分段。对于第二个目标，将应用自动分割算法来提取重要的 接受来自VFS映像的度量和关联指标。该算法的输出将为 对照由经验丰富的评分员手动从VFS映像中得出的衡量标准和指标进行验证 已确定的可靠性。通过该项目开发的工具将减少人工解释的主观性 这将提高吞咽困难诊断和治疗的一致性和可靠性。