The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Sami Qadri; Hannele Yki-Järvinen

doi:10.1016/j.xcrm.2022.100862

The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Cell Rep Med. 2022 Dec 20;3(12):100862. doi: 10.1016/j.xcrm.2022.100862.

Authors

Sami Qadri¹, Hannele Yki-Järvinen²

Affiliations

¹ University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland.
² University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland. Electronic address: hannele.yki-jarvinen@helsinki.fi.

Abstract

Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank.¹ By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fat-associated genetic variants.

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MeSH terms

Deep Learning*
Fatty Liver* / diagnostic imaging
Fatty Liver* / genetics
Humans
Machine Learning
Tomography, X-Ray Computed