Open AccessQuantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank
Author(s) -
Matthew T. MacLean,
Qasim Jehangir,
Marijana Vujkovic,
Yi-An Ko,
Harold Litt,
Arijitt Borthakur,
Hersh Sagreiya,
Mark Rosen,
David A. Mankoff,
Mitchell D. Schnall,
Haochang Shou,
Julio A. Chirinos,
Scott M. Damrauer,
Drew A. Torigian,
Rotonya M. Carr,
Daniel J. Rader,
Walter R. Witschey
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa342
Subject(s) - biobank , medicine , intraclass correlation , abdomen , segmentation , deep learning , subcutaneous fat , diabetes mellitus , artificial intelligence , radiology , computer science , bioinformatics , adipose tissue , endocrinology , biology , clinical psychology , psychometrics
The objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank.