CranioRate: An imaging-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis

CranioRate：基于成像的深度表型分析工具集、存储库和在线临床医生界面，用于颅缝早闭

• 批准号: 10568654
• 负责人: Jesse Aaron Goldstein
• 金额: $ 71.67万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10568654
• 关键词: 3-Dimensional; Address; Adopted; Adverse effects; Affect; Age; Algorithms; Applications Grants; Area; Blindness; Cephalic; Childhood; Clinical; Clinical Data; Collaborations; Collection; Compensation; Complex; Counseling; Craniosynostosis; Data; Data Set; Databases; Decision Making; Development; Diagnosis; Disease; Dysmorphology; Ensure; Evaluation; FaceBase; Family; Future; Goals; Grant; Growth; Head; Human; Image; Impairment; Infant; Intervention; Intracranial Hypertension; Investigation; Joint structure of suture of skull; Lead; Left; Machine Learning; Measures; Morphology; Neurocognition; Neurocognitive; Neurocognitive Deficit; Operative Surgical Procedures; Outcome; Pathology; Patient Care; Patient imaging; Patients; Pattern; Phenotype; Photography; Physicians; Pilot Projects; Population; Postoperative Period; Process; Recommendation; Reconstructive Surgical Procedures; Reporting; Research; Research Design; Research Personnel; Research Project Grants; Risk; Scanning; Security; Severities; Shapes; Socialization; Statistical Models; Surface; Surgeon; System; Systems Analysis; Techniques; Technology; Three-dimensional analysis; United States; Variant; Vision; Visual; Work; X-Ray Computed Tomography; clinical center; clinically relevant; cloud based; cranium; data integrity; design; human imaging; imaging modality; improved; individual patient; infancy; insight; machine learning algorithm; machine learning method; machine learning model; novel; patient population; point of care; premature; prevent; prospective; prototype; repository; risk stratification; self esteem; shape analysis; standard of care; suture fusion; tool; web portal

PROJECT SUMMARY Title: CranioRate™: An image-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis. The purpose of this research grant application is to build on the advanced machine learning (ML) tool developed as part of a pilot study (R21EB026061) that objectively quantifies cranial dysmorphology, or deep phenotypes, in patients with metopic craniosynostosis (MC). Abnormal cranial suture fusion (craniosynostosis) occurs in one of every 2500 infants born in the US, resulting in disrupted regional skull growth and an increased risk of elevated intracranial pressure, neurocognitive impairment and visual disturbances including blindness. Impaired skull growth along the fused suture and subsequent growth compensation in other areas of the skull lead to predictable head shape patterns in patients with craniosynostosis; surgery is recommended early in childhood to restore normal head shape and prevent neurocognitive sequelae. In our work to date, our team has developed an ML/statistical shape analysis system utilizing computed tomography (CT) scans of patients with MC. We have demonstrated that our deep ML algorithm is as effective as expert clinician ratings in assessing severity and more effective than standard craniometric tools. We have expanded our processes to include the analysis of 3D photography to increase accessibility and study post-operative head shape. Thus far, we have demonstrated equivalent severity ratings between 3D photographs and CT scans when obtained on the same patients. Finally, we have designed and implemented an online head shape portal (CranioRate™) that automates preprocessing and analysis such that users can upload their own patient images, where the resulting data contributes to clinical patient care as well as research endeavors. To date, over 30 clinicians have contributed almost 400 MC CT scans to our portal, making our metopic craniosynostosis imaging collection the largest reported. In the proposed work, we will refine our processing pipeline and shape analysis technologies, while expanding our capabilities to encompass all forms of craniosynostosis and a wider array of imaging modalities, and improve the functionality and security of the CranioRate™ portal. To pursue these aims, we will bring together a robust consortium of collaborators to contribute imaging and clinical data, empanel a scientific advisory board to ensure data integrity, and establish an open access human craniosynostosis image bank to allow further collaborations through FaceBase. Specific goals for the current project are to: 1) Further develop a set of robust, general morphological quantification technologies and cloud-based implementations that result in effective scientific and clinical tools; 2) Establish a shared-access, well-curated dataset that will leverage our multicenter collaborative network and partnership with FaceBase; 3) Identify and collect pertinent clinical data to extend the utility of our shape analysis tool and shared-access database. The results of this study will significantly improve the understanding of the phenotypic variation in patients with craniosynostosis and will pave the way for more substantial imaging-based research in this understudied population.

项目摘要 标题：Cranierate™：基于图像的，深层型分析分析工具集，存储库和在线 颅突的临床接口。 本研究赠款申请的目的是建立高级机器学习（ML）工具 作为试点研究（R21EB026061）的一部分，该研究客观地量化颅骨畸形或 深度表型，在静态颅突变病（MC）的患者中。异常颅骨缝合融合 （颅突）发生在美国出生的每2500名婴儿之一中发生，导致区域中断 头骨生长以及颅内压升高，神经认知障碍和 视觉障碍包括失明。沿融合缝合线和随后的颅骨生长受损 颅骨其他区域的增长补偿导致患者的可预测的头形模式 颅突变；建议在儿童早期进行手术以恢复正常的头部形状和 预防神经认知后遗症。 在迄今 MC患者的断层扫描（CT）扫描。我们已经证明了我们的深ML算法是 作为评估严重程度的专家临床评级有效，比标准颅骨测定法 工具。我们已经扩展了流程以包括对3D摄影的分析以增加 可访问性和研究后的头部形状。那遥远，我们已经证明了等效的严重性 在同一患者获得3D照片和CT扫描之间的评分。最后，我们有 设计并实施了一个在线头形门户（Craniorate™），该门户可自动进行预处理 并分析以使用户可以上传自己的患者图像，从而在其中贡献了数据 进行临床患者护理以及研究工作。迄今为止，超过30位临床医生贡献了 几乎400 mc ct扫描到我们的门户网站，使我们 报告的最大。 在拟议的工作中，我们将重新确定处理管道和形状分析技术，而 扩展我们的能力，以涵盖所有形式的颅突变和更广泛的想象力 模式，并提高颅质™门户的功能和安全性。追求这些目标， 我们将汇集一个强大的合作者联盟，以贡献成像和临床数据， Empanel是科学咨询委员会，以确保数据完整性并建立开放访问人士 颅骨突变图像库，可以通过面孔进行进一步的合作。特定目标 当前项目是：1）进一步开发一组健壮的一般形态学定量 导致有效的科学和临床工具的技术和基于云的实施； 2） 建立一个共享的访问，曲线良好的数据集，该数据集将利用我们的多中心协作网络 与面孔的合作关系； 3）识别并收集相关的临床数据以扩展我们的效用 形状分析工具和共享访问数据库。这项研究的结果将显着改善 了解颅突变患者的表型变异，并为 在这一知识的人群中，基于成像的研究更为实质。