Use of Deep Learning Algorithms to Enable Evaluation of the Determinants and Outcomes of Hepatic Steatosis, by HIV Status
使用深度学习算法根据 HIV 状态评估肝脂肪变性的决定因素和结果
基本信息
- 批准号:10677782
- 负责人:
- 金额:$ 16.79万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-09 至 2027-04-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAgingAlcohol abuseAlcohol consumptionAlgorithmsArtificial IntelligenceBioinformaticsBiometryCD4 Lymphocyte CountCardiovascular systemCaringCellsCirrhosisClassificationClinicalCohort StudiesComputed Tomography ScannersComputer AssistedComputerized Medical RecordDataDevelopmentDiabetes MellitusDiagnosisDyslipidemiasElectronic Health RecordEpidemiologyEvaluationFatty LiverFutureGeneral PopulationHIVHIV InfectionsHepaticHepatocyteHepatologyHypertensionImageInformaticsInterventionKnowledgeLiverLiver diseasesMagnetic Resonance ImagingManualsMeasurementMediatingMedicineMentorsMentorshipMetabolic dysfunctionMethodologyMethodsMorbidity - disease rateObesityOutcomeOutcome StudyPathway interactionsPatientsPersonsPopulationPrimary carcinoma of the liver cellsProspective, cohort studyResearchResearch PersonnelResourcesRiskRisk FactorsRisk ReductionSample SizeSamplingScanningScienceSelection BiasSelection for TreatmentsSeveritiesSurrogate MarkersTimeTrainingTriglyceridesUnited StatesVeteransVeterans Health AdministrationViralViral hepatitisViremiaWorkX-Ray Computed Tomographyabdominal CTantiretroviral therapyartificial intelligence methodclinical careco-infectioncohortdeep learningdeep learning algorithmelastographyfollow-upinterestlarge datasetsliver biopsyliver inflammationliver transplantationmortalitymultidisciplinarynovelobesogenicpreventpreventive interventionradiological imagingradiologistrepositoryscreeningtool
项目摘要
Hepatic steatosis, defined by >5% hepatocyte triglyceride content, may be potentiated in people with HIV
(PWH) through viral-mediated mechanisms or metabolic dysfunction associated with antiretroviral therapy
(ART). However, the epidemiology of hepatic steatosis remains unclear among PWH, primarily because
studies have been limited to small patient samples that ascertained steatosis via specialized radiographic
methods or liver biopsy. Since liver disease is a leading cause of morbidity and mortality among PWH, it is
critically important to identify the determinants and consequences of hepatic steatosis in this group. Such
studies will inform interventions and management strategies to mitigate HIV-specific steatosis mechanisms and
its consequences, particularly hepatic decompensation and hepatocellular carcinoma (HCC).
Recent advances in artificial intelligence have facilitated the development of automated computer-aided liver
assessment to determine the presence and severity of hepatic steatosis within noncontrast abdominal
computed tomography (CT) scans. The 8utomatic ,!:iver 8ttenuation Region-Of-Interest-based Measurement
(ALARM) is a deep learning tool previously developed for the identification of moderate-to-severe hepatic
steatosis. Preliminary studies conducted by the applicant demonstrate the high accuracy of ALARM compared
to manual radiologist review across multiple centers and CT scanners, including within the Veterans Health
Administration. To address the knowledge gaps of existing studies, this proposal will first establish a cohort of
over 40,000 PWH and people without HIV (PWOH) in the Veterans Aging Cohort Study (VACS) who
underwent noncontrast abdominal CT imaging for any indication in the context of clinical care between 2002-
2020. The VACS, an ongoing national prospective cohort study of PWH and PWOH across the United States,
includes access to electronic health record data, including image files of CT scans. The ALARM tool will be
applied to this repository of radiographic images to objectively classify the presence or absence of moderateto-
severe hepatic steatosis. The research plan aims to: 1) identify the HIV-specific determinants associated
with hepatic steatosis among PWH, 2) define how traditional determinants of steatosis differ by HIV status, and
3) determine the risk of liver complications associated with steatosis in PWH and how this risk differs by HIV
status. The findings from these studies will inform interventions to prevent and mitigate the development of
hepatic steatosis among persons with HIV, which will help lower the risk of liver complications and prolong
survival in this population. This project will bring together a mentoring team of nationally recognized
researchers and provide time for coursework and training in advanced epidemiology, biostatistics, informatics,
artificial intelligence, hepatology, and HIV medicine that are needed to establish the applicant as an
independent investigator in the field of HIV-related liver diseases.
肝脂肪变性,定义为肝细胞甘油三酯含量的5%,可能在HIV感染者中增强
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
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Jessie Torgersen其他文献
Jessie Torgersen的其他文献
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{{ truncateString('Jessie Torgersen', 18)}}的其他基金
Use of Deep Learning Algorithms to Enable Evaluation of the Determinants and Outcomes of Hepatic Steatosis, by HIV Status
使用深度学习算法根据 HIV 状态评估肝脂肪变性的决定因素和结果
- 批准号:
10548504 - 财政年份:2022
- 资助金额:
$ 16.79万 - 项目类别:
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