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 脑积水是脑脊液 (CSF) 在大脑深处的空腔（脑室）中积聚。 脑积水最常见的治疗方法是通过脑室腹膜 (VP) 分流术进行脑脊液分流。 据估计，美国每年有 30,000 例 VP 分流术，尽管这种情况很常见。 进行手术后，并发症发生率估计接近 24%，一份报告称并发症发生率为 22% 近 50% 因分流相关问题入院的患者需要住院五天或以上。 考虑到手术部位感染的发生率以及与分流探查和翻修相关的并发症， 对于许多神经外科医生来说，准确诊断分流故障仍然是一个至关重要的目标之一。 仅根据影像学诊断的困难在于缺乏标准化的稳健方法 最近，人们研究了测量心室大小的方法。 与埃文斯指数或额枕角比率相比，脑室大小被建议为 更准确和更好的工具来测量心室大小对分流的反应。 相关的人类努力以及观察者之间和观察者内部在分割心室方面的变异性阻碍了它的广泛应用 临床采用的另一个困难是确定脑室扩大或脑积水的诊断。 缺乏标准化、规范性的数据集，其中包含一系列被认为是“正常”的数据集 年龄范围，因为心室大小随着年龄的增长而增加。目前的文献缺乏可靠的规范数据集。 按年龄和性别划分的心室大小，直到最近才为儿科年龄生成这样的数据集 建立所有年龄段人群心室体积和形态的标准值。 迫切需要并将允许调查与脑积水相关的各种主题，并最终 协助脑积水和 VP 分流相关并发症或故障的检测和分类。 近年来，深度学习（DL）模型的快速发展对许多领域产生了巨大影响。 医学，尤其是利用深度学习的自动图像分析任务。 模型，该项目提出了两个目标：1）开发并验证稳健的心室 DL 模型 细分包括多模态支持和自动故障检测并建立规范数据库； 2）开发一个融合DL模型和规范值并适合临床的软件原型 基于图像的分流故障诊断工作流程最终将成为一种独特的软件产品。 开发并商业化以改善分流故障和脑积水的诊断并受益 为患者带来更好的手术效果并降低费用。

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。

• 发表时间: 2023-07
• 影响因子: 2
• 作者: Kellogg, Ryan T;Park, Min S;Snyder, M Harrison;Marino, Alexandria;Patel, Sohil;Feng, Xue;Vargas, Jan
• 通讯作者: Vargas, Jan