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Leveraging Neural Imaging for Automated Neonatal Infection Diagnosis

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
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2024-08-31
关键词:
AfricaAfrica South of the SaharaAnti-Bacterial AgentsAntiviral AgentsArtificial IntelligenceBloodBlood specimenBrainCaregiversCauterizeCentral Nervous System InfectionsCephalicCerebrospinal FluidCessation of lifeCharacteristicsChildhoodClassificationClinicalCognitiveCommunicable DiseasesCoupledDNA sequencingDataDevelopmentDiagnosisDiagnosticDisease ManagementDoctor of PhilosophyEndocrineEngineeringEnvironmentEvaluationFailureFunctional disorderGoalsGoldGrowthHeadHealthcare SystemsHome environmentHuman ResourcesHydrocephalusImageImage AnalysisImageryImaging TechniquesInfantInfectionInfectious AgentInstitutionLaboratoriesLeadLearningMachine LearningMentorshipMicrobiologyMonitorNatureNeonatalNervous system structureNeuraxisOperative Surgical ProceduresOutputPathogenicityPathologicPatientsPatternPhenotypePhysiciansPlayPreventionProceduresProtocols documentationRecommendationResearchResourcesSamplingScientistSecondary toSepsisShunt DeviceSpinal PunctureStructure of choroid plexusSurvivorsTechnologyTimeTrainingTranslational ResearchUgandaUltrasonographyVentriculostomyWorkX-Ray Computed Tomographybasecerebrospinal fluid flowcohesioncohortdesigndiagnosis standarddisease classificationgenome sequencinghands-on learningimprovedinnovationintelligent algorithminterestmachine learning algorithmmortalityneonatal infectionneonatal sepsisneonateneurosurgerynon-invasive imagingoptimal treatmentspathogenpreventprogramsrelating to nervous systemsepticsuccesssupervised learningtargeted treatmenttranscriptome sequencingtreatment planningtreatment strategy

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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.
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Leveraging Neural Imaging for Automated Neonatal Infection Diagnosis
Leveraging Neural Imaging for Automated Neonatal Infection Diagnosis
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