ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2019
A Novel Hybrid Model for Visceral Adipose Tissue Prediction using Shape Descriptors.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology.NPJ digital medicine · 2025Article
- Predicting Nonalcoholic Fatty Liver Disease in Obese Populations with 3D Body Scans.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Machine learning-based obesity classification considering 3D body scanner measurements.Scientific reports · 2023Article
- 3D Body shape for regional and appendicular body composition estimation.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk.NPJ digital medicine · 2022Article
- Liver Fat Assessment with Body Shape.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- S2FLNet: Hepatic steatosis detection network with body shape.Computers in biology and medicine · 2022Article
- Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.Journal of biomedical informatics · 2021Article
- Body shape matters: Evidence from machine learning on body shape-income relationship.PloS one · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Obesity is gaining increasing attention in modern society since it is associated with various health issues. The visceral adipose tissue (VAT) deposits around the abdominal organs and is considered an extremely important indicator of health risk. VAT can be assessed through magnetic resonance imaging (MRI) or computed tomography (CT) accurately, but the cost is prohibitive. Shape-based body composition prediction has become a promising topic thanks to the prevalence of commodity optical body scan systems, from which numerous anthropometries can be extracted automatically. In this paper, we propose an innovative shape-based hybrid VAT prediction model. The most appealing benefit of our method is to robustly handle the lack of knowledge about gender and demographics. First, we train a baseline VAT prediction model for each gender separately. Second, we train a classifier to predict the gender likelihood and a classifier to predict the shape likelihood of being overestimated in VAT baseline prediction. Third, we integrate the gender likelihood and shape likelihood into the baseline models to derive one hybrid VAT prediction model. We compare our prediction model with other state-of-the-art VAT prediction methods. The result shows that our method outperforms the comparison methods by 21.8% on average.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.