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: 10677782
• 负责人: Jessie Torgersen
• 金额: $ 16.79万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10677782
• 关键词: Address; Aging; Alcohol abuse; Alcohol consumption; Algorithms; Artificial Intelligence; Bioinformatics; Biometry; CD4 Lymphocyte Count; Cardiovascular system; Caring; Cells; Cirrhosis; Classification; Clinical; Cohort Studies; Computed Tomography Scanners; Computer Assisted; Computerized Medical Record; Data; Development; Diabetes Mellitus; Diagnosis; Dyslipidemias; Electronic Health Record; Epidemiology; Evaluation; Fatty Liver; Future; General Population; HIV; HIV Infections; Hepatic; Hepatocyte; Hepatology; Hypertension; Image; Informatics; Intervention; Knowledge; Liver; Liver diseases; Magnetic Resonance Imaging; Manuals; Measurement; Mediating; Medicine; Mentors; Mentorship; Metabolic dysfunction; Methodology; Methods; Morbidity - disease rate; Obesity; Outcome; Outcome Study; Pathway interactions; Patients; Persons; Population; Primary carcinoma of the liver cells; Prospective, cohort study; Research; Research Personnel; Resources; Risk; Risk Factors; Risk Reduction; Sample Size; Sampling; Scanning; Science; Selection Bias; Selection for Treatments; Severities; Surrogate Markers; Time; Training; Triglycerides; United States; Veterans; Veterans Health Administration; Viral; Viral hepatitis; Viremia; Work; X-Ray Computed Tomography; abdominal CT; antiretroviral therapy; artificial intelligence method; clinical care; co-infection; cohort; deep learning; deep learning algorithm; elastography; follow-up; interest; large datasets; liver biopsy; liver inflammation; liver transplantation; mortality; multidisciplinary; novel; obesogenic; prevent; preventive intervention; radiological imaging; radiologist; repository; screening; tool

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