Gender-specific data-driven adiposity subtypes using deep-learning-based abdominal CT segmentation.

Journal: Obesity (Silver Spring, Md.)

PMID: 37157112

OBJECTIVE: The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks.

Xiantong Zou

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Xianghai Zhou

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Yufeng Li

Department of Sports Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Qi Huang

State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural Universitygrid.35155.37, Wuhan, China.
Yuan Ni

IBM Research, China, Beijing, China.
Ruiming Zhang
Fang Zhang
Xin Wen
Jiayu Cheng

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Yanping Yuan

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Yue Yu

Department of Mathematics, Lehigh University, Bethlehem, PA, USA.
Chengcheng Guo

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Guotong Xie

Ping An Health Technology, Beijing, China.
Linong Ji

Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.

Adiposity Deep Learning Female Humans Intra-Abdominal Fat Liver Male Obesity Obesity, Abdominal Tomography, X-Ray Computed

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