Development of computerised scoring system for Chiari-like malformation associated pain and syringomyelia.

开发用于 Chiari 样畸形相关疼痛和脊髓空洞症的计算机评分系统。

• 批准号: 2377477
• 金额: --
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2377477
• 关键词: Development; computerised; scoring; system; Chiari

The aim of this new study is to build on the our current collaborative work to investigate the specific research question of what, andwhat degree of, external conformational features are associated with problematic internal morphology (e.g. deep stop,short muzzle, domed head, forward facing oelarge? eyes in shallow orbits and low set ears). This will be addressedvia the following objectives: 1.Develop a Machine learning-based regression model for CM and SM in the CKCS.Previously we used a classifying model on 2D midline sagittal MRI. We will expand this model to a 3D analysis of featuresdetermined from high-resolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo(MPRAGE) and CT. 2.Develop a ML image assessment model to predict and score risk of CM-P and SM-S from diagnosticMRI and CT. Use Correlated Correlation Analysis to understand how combinations of correlated breeding-related features(short muzzle, domed forehead, depth of stop, wide palpebral apertures) externally correlate with problematic internalmorphology. 3.Expand the model to other brachycephalic breeds including the Chihuahua and Griffon Bruxellois.

这项新研究的目的是在我们目前的合作工作的基础上，调查具体的研究问题，即外部构象特征与有问题的内部形态（例如深止、短口吻、圆顶头、向前的大眼睛、浅眼眶和位置低的耳朵）相关以及什么程度。这将通过以下目标来解决： 1. 为 CKCS 中的 CM 和 SM 开发基于机器学习的回归模型。之前我们在 2D 中线矢状 MRI 上使用分类模型。我们将该模型扩展到对通过梯度回波 (MPRAGE) 和 CT 的高分辨率三维 (3D) 磁化准备快速采集确定的特征进行 3D 分析。 2.开发ML图像评估模型，通过诊断MRI和CT来预测和评分CM-P和SM-S的风险。使用相关相关分析来了解相关的繁殖相关特征（短口吻、圆顶前额、深度深度、宽眼睑孔径）的组合如何在外部与有问题的内部形态相关联。 3.将该模型扩展到其他短头颅品种，包括吉娃娃犬和布鲁塞尔狮鹫。