Leveraging Neural Imaging for Automated Neonatal Infection Diagnosis

利用神经成像进行新生儿感染自动化诊断

• 批准号: 10066656
• 负责人: Mallory Rose Peterson
• 金额: $ 3.22万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10066656
• 关键词: Africa; Africa South of the Sahara; Anti-Bacterial Agents; Antiviral Agents; Artificial Intelligence; Blood; Blood specimen; Brain; Caregivers; Cauterize; Central Nervous System Infections; Cephalic; Cerebrospinal Fluid; Cessation of life; Characteristics; Childhood; Classification; Clinical; Cognitive; Communicable Diseases; Coupled; DNA sequencing; Data; Development; Diagnosis; Diagnostic; Disease Management; Doctor of Philosophy; Endocrine; Engineering; Environment; Evaluation; Failure; Functional disorder; Goals; Gold; Growth; Head; Healthcare Systems; Home environment; Human Resources; Hydrocephalus; Image; Image Analysis; Imagery; Imaging Techniques; Infant; Infection; Infectious Agent; Institution; Laboratories; Lead; Learning; Machine Learning; Mentorship; Microbiology; Monitor; Nature; Neonatal; Nervous system structure; Neuraxis; Operative Surgical Procedures; Output; Pathogenicity; Pathologic; Patients; Pattern; Phenotype; Physicians; Play; Prevention; Procedures; Protocols documentation; Recommendation; Research; Resources; Sampling; Scientist; Secondary to; Sepsis; Shunt Device; Spinal Puncture; Structure of choroid plexus; Survivors; Technology; Time; Training; Translational Research; Uganda; Ultrasonography; Ventriculostomy; Work; X-Ray Computed Tomography; base; cerebrospinal fluid flow; cohesion; cohort; design; diagnosis standard; disease classification; genome sequencing; hands-on learning; improved; innovation; intelligent algorithm; interest; machine learning algorithm; mortality; neonatal infection; neonatal sepsis; neonate; neurosurgery; non-invasive imaging; optimal treatments; pathogen; prevent; programs; relating to nervous system; septic; success; supervised learning; targeted treatment; transcriptome sequencing; treatment planning; treatment strategy

PROJECT SUMMARY/ABSTRACT Post-infectious hydrocephalus (PIH) is a leading cause of neonate mortality in the developing world, but there are limited resources in place for appropriately diagnosing and monitoring the infections that lead to hydrocephalus. There is often a lack of personnel and laboratory resources available for the gathering and processing of lumbar puncture and blood cultures, which are the gold-standard for diagnosing the infectious agents at play in sepsis and PIH. In order to overcome this obstacle, CSF and blood samples were taken from a cohort of septic neonates in Mbale, Uganda, as well as a cohort of neonates and infants who had already progressed to PIH. Cranial ultrasounds (CrUS) were taken from the cohort of septic neonates, and head CT scans were gathered from the PIH cohort. This proposal hypothesizes that the pathogens determined from RNA and DNA sequencing of the blood and CSF samples can be used to train supervised machine learning algorithms to recognize imaging phenotypes characteristic of the underlying pathogen. Therefore, PIH can be prevented by providing pathogen-specific diagnosis and targeted treatment recommendations at the bedside for septic neonates using CrUS. Furthermore, surgical treatment success for PIH can be optimized using CT for the purpose of identifying the underlying pathogen and providing management plan recommendations. This project provides an ideal training environment for a fellow interested in pediatric neurosurgery with a research emphasis on engineering and machine learning applied to image analysis. The interdisciplinary and global nature of the project encourages development of a collaborative and innovative research approach. The home institution of Penn State provides multiple clinical opportunities for growth in pediatric neurosurgery, the MD/PhD program is supportive of truly translational research efforts, and the sponsor and co-sponsor are more than adequately prepared to provide all aspects of training mentorship necessary to accomplish the aims of this project and develop a well-rounded physician-scientist.

项目总结/摘要 感染后脑积水（PIH）是发展中国家新生儿死亡的主要原因，但 用于适当诊断和监测感染的资源有限， 脑积水通常缺乏人员和实验室资源， 腰椎穿刺和血培养的处理，这是诊断感染性疾病的金标准。 在败血症和妊高征中起作用的药物。为了克服这一障碍，CSF和血液样品取自 乌干达姆巴莱的一组败血症新生儿，以及一组已经 进展为妊高征。从脓毒症新生儿队列中采集颅骨超声（CrUS）， 从PIH组群收集扫描。这一提议假设，从RNA确定的病原体 血液和CSF样本的DNA测序可用于训练监督机器学习算法 以识别潜在病原体的成像表型特征。因此，PIH是可以预防的。 通过在床边提供病原体特异性诊断和针对性治疗建议， 新生儿使用CrUS。此外，对于妊高征的手术治疗成功率，可以使用CT进行优化。 确定潜在病原体并提供管理计划建议的目的。 该项目为对小儿神经外科感兴趣的研究员提供了一个理想的培训环境， 研究重点是应用于图像分析的工程和机器学习。跨学科和 该项目的全球性质鼓励发展合作和创新的研究方法。的 宾夕法尼亚州立大学的家庭机构为儿科神经外科的发展提供了多种临床机会， MD/PhD计划支持真正的转化研究工作，赞助商和共同赞助商更多 比充分准备提供必要的培训辅导的各个方面，以实现这一目标， 项目和发展一个全面的医生科学家。