2022
DOI: 10.1007/s00330-022-08934-w
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Standardized measurement of abdominal muscle by computed tomography: association with cardiometabolic risk in the Framingham Heart Study

Abstract: Objectives To provide a standard for total abdominal muscle mass (TAM) quantification on computed tomography (CT) and investigate its association with cardiovascular risk in a primary prevention setting. Methods We included 3016 Framingham Heart Study participants free of cardiovascular disease (CVD) who underwent abdominal CT between 2002 and 2005. On a single CT slice at the level of L3/L4, we segmented (1) TAM-Area, (2) TAM-Index (= TAM-Area/height) and… Show more

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Cited by 4 publications
(2 citation statements)
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References 32 publications
(54 reference statements)
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“…The application of noninvasive scoring systems to predict cardiovascular disease risk is increasingly becoming a new strategy for cardiovascular disease prevention [ 29 , 30 ]. Our study, conducted in hypertensive patients without a history of cardiovascular disease, demonstrated for the first time that liver fibrosis scores were significantly associated with the risk of cardiovascular disease in a hypertensive population.…”
Section: Discussionmentioning
confidence: 99%
“…The application of noninvasive scoring systems to predict cardiovascular disease risk is increasingly becoming a new strategy for cardiovascular disease prevention [ 29 , 30 ]. Our study, conducted in hypertensive patients without a history of cardiovascular disease, demonstrated for the first time that liver fibrosis scores were significantly associated with the risk of cardiovascular disease in a hypertensive population.…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, convolutional neural network (CNN) based models, like V-Net 7 and U-Net 8 , have shown excellent performance in medical image segmentation tasks [9][10][11] , where some of them were designed for body composition segmentation 12,13 . However, two challenging problems significantly affect the performance of those models: (1) The unsatisfactory segmentation results concerning hard-to-distinguish or indistinguishable anatomical structures, and (2) the mandatory requirement for a large-scale training dataset. To improve the performance of deep neural networks for body composition tissue segmentation and to reduce the requirement of annotated data for training a well-generalizable model, we introduce the concept of the body area and a novel neural network model called Geographic Attention Net (GA-Net).…”
Section: Introductionmentioning
confidence: 99%