Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.

Jessie Torgersen, Scott Akers,Yuankai Huo,James G Terry,J Jeffrey Carr, Alexander T Ruutiainen,Melissa Skanderson, Woody Levin,Joseph K Lim,Tamar H Taddei,Kaku So-Armah,Debika Bhattacharya,Christopher T Rentsch, Li Shen,Rotonya Carr,Russell T Shinohara, Michele McClain,Matthew Freiberg,Amy C Justice,Vincent Lo Re

Pharmacoepidemiology and drug safety（2023）

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摘要

ALARM demonstrated excellent accuracy for moderate-to-severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real-world studies to evaluate medications associated with steatosis and assess differences by HIV status. This article is protected by copyright. All rights reserved.

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hepatic steatosis,computed tomography scans,deep learning algorithm,computed tomography,tomography scans

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