Improved Diagnosis of Shunt Malfunction with Automatic Quantification of Ventricular Space

通过心室空间自动量化改进分流故障的诊断

• 批准号: 10384590
• 负责人: Xue Feng
• 金额: $ 34万
• 依托单位: CARINA MEDICAL, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10384590
• 关键词: Adoption; Adult; Age; Anatomy; Area; Brain; Cerebrospinal Fluid; Childhood; Clinical; Complication; Computer software; Creation of ventriculo-peritoneal shunt; Data; Data Collection; Data Set; Databases; Detection; Development; Diagnosis; Digital Imaging and Communications in Medicine; Environment; Evaluation; Failure; Feedback; Gender; Goals; Horns; Human; Hydrocephalus; Image; Image Analysis; Institution; Intraobserver Variability; Investigation; Literature; Magnetic Resonance Imaging; Measures; Medicine; Methods; Modeling; Morphology; Neurosurgeon; Normal Range; Operative Surgical Procedures; Pathologic; Patients; Pattern; Performance; Phase; Population; Privatization; Process; Reporting; Scanning; Shunt Device; Software Framework; Standardization; Surgical Wound Infection; Testing; Training; Triage; Uncertainty; United States; Validation; Ventricular; X-Ray Computed Tomography; accurate diagnosis; age group; aging population; base; clinical practice; cloud based; common treatment; cost; data management; deep learning; deep learning model; experience; human-in-the-loop; improved; indexing; innovation; multimodality; prototype; response; shape analysis; surgery outcome; task analysis; tool

ABSTRACT Hydrocephalus is the buildup of cerebrospinal fluid (CSF) in the cavities (ventricles) deep within the brain. The most common treatment for hydrocephalus is CSF diversion via ventriculoperitoneal (VP) shunting. Over 30,000 VP shunts are placed per year in the United States by some estimates. Despite how commonly this surgery is performed, the complication rate has been estimated at almost 24%, with one report citing a 22% rate of revision. Nearly 50% of patients admitted with shunt related issues require a stay of five or more days. Given the rate of surgical site infections and complications associated with shunt explorations and revisions, accurate diagnosis of a shunt malfunction remains a critical, if elusive, goal for many neurosurgeons. One of the difficulties in establishing a diagnosis based on imaging alone is the lack of standardized robust methods of measuring ventricular size. Recently volumetric analyses have been studied as a method for measuring ventricular size, as compared to the Evans’ Index or frontal-occipital horn ratios and have been suggested is more accurate and a better tool for measuring response of ventricular size to shunting. However, the associated human efforts and inter- and intra-observer variability in segmenting the ventricles prohibits its wide clinical adoption. The other difficulty with establishing a diagnosis of ventriculomegaly or hydrocephalus, involves a lack of a standardized, normative dataset with a range of what is considered "normal" for various age ranges as the ventricle size increases with age. Current literature lacks a robust normative dataset of ventricular size by age and gender and only recently has such a dataset been produced for the pediatric age range. Establishment of normative values for ventricular volume and morphology across all age population is sorely needed and will allow for the investigation of a variety of topics related to hydrocephalus and ultimately assisting in the detection and triage of hydrocephalus and VP shunt related complications or malfunctions. In recent years, the rapid development of deep learning (DL) models has led to great impact on many areas of medicine, especially for automatic image analysis tasks including segmentation. Taking advantage of DL models, two aims are proposed in this project: 1) develop and validate a robust DL model for ventricle segmentation including multi-modality support and automatic failure detection and build a normative database; 2) develop a software prototype that incorporates the DL model and normative values and fits the clinical workflow for image-based diagnosis of shunt malfunction. Ultimately, a unique software product will be developed and commercialized to improve the diagnosis of shunt malfunction and hydrocephalus and benefit the patients with better surgical outcome and reduced cost.

抽象的 脑积水是大脑深处的腔（心室）中脑脊液（CSF）的积聚。这 脑积水最常见的治疗方法是通过心室（VP）Shhunting通过CSF转移。超过 据估计，每年在美国将30,000个VP分流器放置。尽管这是多么普遍 进行手术，并发症发生率估计为近24％，其中一份报告为22％ 修订率。接纳与分流有关的问题的患者中，近50％需要停留五天或以上。 考虑到与分流探索和修订相关的手术部位感染和并发症的速度， 对于许多神经外科医生而言，对分流故障的准确诊断仍然是关键的，即使难以捉摸的目标。之一 仅基于成像建立诊断的困难是缺乏标准化的鲁棒方法 测量心室大小。最近的体积分析已被研究为测量的方法 与埃文斯的指数或额叶角喇叭比相比，心室大小是 更准确和更好的工具，用于测量心室大小对分流的响应。但是， 相关的人类努力以及分割心室的跨性别和观察者的变异性禁止其广泛 临床采用。建立诊断心室肿瘤或脑积水的其他困难， 涉及缺乏标准化的正常数据集，并且对于各种 随着通风尺寸随着年龄的增长而增加的年龄范围。当前文献缺乏强大的正常数据集 划分年龄和性别的心室大小，直到最近才有这样的数据集 范围。在所有年龄人群中建立心室体积和形态的正常值是 非常需要，将允许对与脑积水有关的各种主题进行投资 协助检测和分类脑积水和VP分流并发症或故障。在 近年来，深度学习（DL）模型的快速发展导致了许多领域的影响 药物，特别是用于自动图像分析任务，包括细分。利用DL 模型，在此项目中提出了两个目标：1）开发和验证可通风的强大DL模型 细分包括多模式支持和自动故障检测并构建正常数据库； 2）开发一个包含DL模型和正常值的软件原型，并适合临床 基于图像的分流故障诊断的工作流程。最终，独特的软件产品将是 开发和商业化，以改善分流功能和脑积水的诊断并受益 手术结局更好和成本降低的患者。

项目成果

Establishment of Age- and Sex-Specific Reference Cerebral Ventricle Volumes.

建立特定年龄和性别的参考脑室体积。

• DOI: 10.1016/j.wneu.2023.04.055
• 发表时间: 2023
• 期刊: World neurosurgery
• 影响因子: 2
• 作者: Kellogg,RyanT;Park,MinS;Snyder,MHarrison;Marino,Alexandria;Patel,Sohil;Feng,Xue;Vargas,Jan
• 通讯作者: Vargas,Jan