Dataset Information

Novel body fat estimation using machine learning and 3-dimensional optical imaging.

ABSTRACT: Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n?=?158), producing a R2 value of 0.78 and a constant error of (X?±?SD) 0.8?±?4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland-Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.

SUBMITTER: Harty PS

PROVIDER: S-EPMC7220828 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Novel body fat estimation using machine learning and 3-dimensional optical imaging.

Harty Patrick S PS Sieglinger Breck B Heymsfield Steven B SB Shepherd John A JA Bruner David D Stratton Matthew T MT Tinsley Grant M GM

European journal of clinical nutrition 20200316 5

Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream<sup>®</sup> SS20) and a 4C model. Machine learning was used to identify s ...[more]

PMID: 32203233

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Project description:Objective: Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate early detection of this disease is still unavailable. This study is designed to combine the use of 3D imaging and machine learning techniques in facial feature analysis and identification of acromegalic patients, in an effort to ascertain how both techniques performed in terms of applicability and value in the early detection of the disease. Methods: One hundred and twenty-four participants including 62 patients with acromegaly and 62 matched controls were enrolled. Using three-dimensional imaging techniques, 58 facial parameters were measured on each face. A two-way analysis of variance (ANOVA) and a post-hoc t-tests were conducted to examine the variations of these parameters with disease status and gender. Using linear discriminant analysis (LDA), we further distinguished patients from controls, characterized what combinations of the parameters could best predict disease state and their relative contributions. Results: Patients are significantly different from normal subjects in many variables, and facial changes of male patients are more significant than female ones. Both male and female patients present following major changes: the increase of facial length and breadth, the widening and elevation of the nose, the thickening of vermilion and the enlargement of the mandible. Facial variables which strongly related to the pathological states can be used to predict the morbid state with high accuracy (prediction accuracies 92.86% in females, p < 0.0001 and 75% in males, p < 0.001). We have further testified that only a few variables play a vital role in disease prediction and the vital combination of variables vary with gender. Conclusions: Three-dimensional imaging enables comprehensive and accurate quantification of facial characteristics, which makes it a promising technique to investigate facial features of acromegalic patients. In combination with machine learning technique, patients can be accurately identified and predicted by their facial variables. This approach might be beneficial for the early detection of acromegalic patients and timely consultation to improve their outcomes.

Dataset Information

Novel body fat estimation using machine learning and 3-dimensional optical imaging.

Publications

Novel body fat estimation using machine learning and 3-dimensional optical imaging.

Similar Datasets

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