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Changes in abdominal fat depots after bariatric surgery are associated with improved metabolic profile

Authors
Eun Ji Lee, Nayoung Song, Eui Seok Chung, Eun Heo, Haekyung Lee, Hyungnae Kim, Jin Seok Jeon, Hyunjin Noh, Sang Hyun Kim, Soon Hyo Kwon
Journal
NMCD (Nutrition, Metabolism & Cardiovascular Diseases)
Related Product

Research

Date Published
2022.12
doi
https://doi.org/10.1016/j.numecd.2022.12.017
Summary

In this study, the position of the inferior alveolar nerve (IAN) was automatically imaged and tracked using artificial intelligence (AI) for safer surgery. The IAN segmentation was performed using AVIEW Modeler software (version 1.0.3, Coreline Software, Seoul, Korea), and the deep active learning framework proved to be a fast, accurate, and robust clinical tool for demarcating IAN location. Similarly, in another study, CT scans were analyzed using AVIEW Research (Coreline) software to measure abdominal fat depots in patients with obesity before and after bariatric surgery. The software was able to measure the volumes and fat attenuation index (FAI) of different fat depots accurately, and the study found that specific abdominal fat depot changes measured by CT were positively associated with metabolic health status conversion.

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