Enhancing CBTN digital pathology processing pipeline through the use AWS cloud-based services to enable automation, parallel processing, and rapid use of AI/ML analytics

通过使用 AWS 基于云的服务增强 CBTN 数字病理处理管道，以实现自动化、并行处理和快速使用 AI/ML 分析

• 批准号: 10827712
• 负责人: Robert J Carroll
• 金额: $ 28.22万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-26 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10827712
• 关键词: Acceleration; Adoption; Adult; Artificial Intelligence; Automation; Big Data; Biological; Brain Neoplasms; Cancer Etiology; Child; Childhood; Childhood Brain Neoplasm; Clinical Data; Cloud Computing; Cloud Service; Communities; Data; Data Analytics; Data Set; Development; Disease; Ecosystem; Environment; Etiology; Funding; Generations; Goals; Healthcare; Human Resources; Image; Infrastructure; Ingestion; Intake; Knowledge; Life Cycle Stages; Machine Learning; Malignant Childhood Neoplasm; Malignant Neoplasms; Manuals; Medical Imaging; Methods; Modality; Modernization; Molecular; Morphology; Pathology; Pathway interactions; Patient Care; Patients; Pediatric Research; Performance; Population; Preparation; Process; Property; Research; Resolution; Resources; Sampling; Scanning; Services; Site; Slide; Software Tools; Structural Congenital Anomalies; Technology; Testing; Translations; Tumor Biology; United States National Institutes of Health; Work; advanced analytics; childhood cancer mortality; clinical phenotype; cloud based; cloud platform; cloud storage; computing resources; cost; cost estimate; data exploration; data ingestion; data integration; data management; data portal; data repository; data resource; data sharing; digital; digital pathology; empowerment; feasibility testing; flexibility; genomic data; large scale data; machine learning method; multimodality; multiple omics; novel; open source; parallel processing; parallelization; personalized medicine; phenotypic data; precision medicine; programs; research study; software development; standard of care; tool; translational model; user-friendly; web services; years of life lost

PROJECT SUMMARY The Gabriella Miller Kids First pediatric research program is a collaborative initiative with the goal of understanding the etiology and drivers of pediatric diseases. Related efforts include (1) developing data-driven platforms, workflows and tools; (2) accelerating discovery of generic causes and shared biologic pathways within and across conditions; and (3) enabling rapid translation of scientific discoveries to personalized treatments. Through this program, some of the largest, multi-modal pediatric datasets have been generated, harmonized, and released for use by the research community. In parallel, developed software platforms have enabled streamlined, user-friendly workflows in cloud environments to empower data exploration to analysis. While this work has significantly advanced the use of integrated datasets, particularly for genomics and clinical data, related advancements for medical imaging data have been slower. In the present project, we focus on digital pathology data in research contexts and aim to enable the use of modern cloud infrastructure and processes for Kids First digital pathology slides. Specifically, we will explore, implement, optimize, and assess cloud platforms and services to reduce current challenges in data ingest and release, and preparation of digital slide images for downstream analytics. The selected cloud solutions are expected to enable workflow automation, scaling of computing resources, and full support of the entire data lifecycle. This should drastically reduce the operational and computational costs of utilizing Kids First digital pathology data across research contexts. Together, this project will bridge digital pathology data and tools with high-performance cloud workspaces to accelerate their use with advanced analytics and multi-modal integration for all Kids First pediatric data.

项目总结