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

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

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. Park, MacLean, et ...

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Veröffentlicht in:Cell reports. Medicine 2022-12, Vol.3 (12), p.100862-100862, Article 100862
Hauptverfasser: Qadri, Sami, Yki-Järvinen, Hannele
Format: Artikel
Sprache:eng
Schlagworte:
Deep Learning
Fatty Liver - diagnostic imaging
Fatty Liver - genetics
Humans
Machine Learning
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Tomography, X-Ray Computed
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Zusammenfassung: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. 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.
ISSN:2666-3791
2666-3791
DOI:10.1016/j.xcrm.2022.100862