These studies look at fat, lean, and bone tissue to quantify how aspects of body shape exhibit risk of diseases such as cancers and obesity. We also study how the development and a person’s environment may affect their body. Click the links to learn more.
Ongoing Projects
Shape Up! Studies
Shape Up! Studies explore and develop ways to measure health and body composition from 2D and 3D images, which can then provide useful and detailed information about various health and wellness risks. The research relies on information gathered from subjects. If you’d like to participate, visit the site to learn more!
Deep Learning and Total Body DXA Scans (The TBDXA.I. Project)
Overview Deep Learning trains neural networks on large datasets, including images, to generate algorithms that predict endpoints. Total Body DXA scans contain digital information about body composition (fat, bone, lean… Read More »Deep Learning and Total Body DXA Scans (The TBDXA.I. Project)
Space-Feasible Body Composition and Body Shape Analysis for Long Duration Missions (Astro3DO)
Overview Long-duration space flights and extraterrestrial colonizing magnify physiology challenges for the voyagers involved, including debilitating loss of muscle and bone mass. The musculoskeletal changes are primarily from acclimation to… Read More »Space-Feasible Body Composition and Body Shape Analysis for Long Duration Missions (Astro3DO)
Completed Projects
Samsung Bioimpedance System Evaluation Study
Overview Bioimpedance analysis systems are rapidly evolving and are now increasingly being used in research, clinical, and home settings (1). The novel Samsung BIA devices operate through a wrist-worn watch… Read More »Samsung Bioimpedance System Evaluation Study
Da Kine Body Composition Study
Overview Hydration status and total body water (TBW) in humans may vary in special populations such as athletes and age groups. This variability may lead to inaccurate body composition measurements… Read More »Da Kine Body Composition Study
Related publications
Latest publications related to body composition:
2318789 body composition items 1 nlm 10 date desc 1 1 1 title https://shepherdresearchlab.org/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22instance%22%3A%22zotpress-64701cd26cdf66659ed0b818f1e286fc%22%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22VIDLHCJ8%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Tian%20et%20al.%22%2C%22parsedDate%22%3A%222023-09-01%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3ETian%20IY%2C%20Wong%20MC%2C%20Nguyen%20WM%2C%20Kennedy%20S%2C%20McCarthy%20C%2C%20Kelly%20NN%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS0261561423002315%27%3EAutomated%20body%20composition%20estimation%20from%20device-agnostic%203D%20optical%20scans%20in%20pediatric%20populations%3C%5C%2Fa%3E.%20Clinical%20Nutrition%20%5BInternet%5D.%202023%20Sep%201%3B42%289%29%3A1619%5Cu201330.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DVIDLHCJ8%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Automated%20body%20composition%20estimation%20from%20device-agnostic%203D%20optical%20scans%20in%20pediatric%20populations%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Isaac%20Y.%22%2C%22lastName%22%3A%22Tian%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%20C.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22William%20M.%22%2C%22lastName%22%3A%22Nguyen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Samantha%22%2C%22lastName%22%3A%22Kennedy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cassidy%22%2C%22lastName%22%3A%22McCarthy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Nisa%20N.%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yong%20E.%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andrea%20K.%22%2C%22lastName%22%3A%22Garber%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Steven%20B.%22%2C%22lastName%22%3A%22Heymsfield%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Brian%22%2C%22lastName%22%3A%22Curless%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22Summary%5CnBackground%5CnExcess%20adiposity%20in%20children%20is%20strongly%20correlated%20with%20obesity-related%20metabolic%20disease%20in%20adulthood%2C%20including%20diabetes%2C%20cardiovascular%20disease%2C%20and%2013%20types%20of%20cancer.%20Despite%20the%20many%20long-term%20health%20risks%20of%20childhood%20obesity%2C%20body%20mass%20index%20%28BMI%29%20Z-score%20is%20typically%20the%20only%20adiposity%20marker%20used%20in%20pediatric%20studies%20and%20clinical%20applications.%20The%20effects%20of%20regional%20adiposity%20are%20not%20captured%20in%20a%20single%20scalar%20measurement%2C%20and%20their%20effects%20on%20short-%20and%20long-term%20metabolic%20health%20are%20largely%20unknown.%20However%2C%20clinicians%20and%20researchers%20rarely%20deploy%20gold-standard%20methods%20for%20measuring%20compartmental%20fat%20such%20as%20magnetic%20resonance%20imaging%20%28MRI%29%20and%20dual%20X-ray%20absorptiometry%20%28DXA%29%20on%20children%20and%20adolescents%20due%20to%20cost%20or%20radiation%20concerns.%20Three-dimensional%20optical%20%283DO%29%20scans%20are%20relatively%20inexpensive%20to%20obtain%20and%20use%20non-invasive%20and%20radiation-free%20imaging%20techniques%20to%20capture%20the%20external%20surface%20geometry%20of%20a%20patient%27s%20body.%20This%203D%20shape%20contains%20cues%20about%20the%20body%20composition%20that%20can%20be%20learned%20from%20a%20structured%20correlation%20between%203D%20body%20shape%20parameters%20and%20reference%20DXA%20scans%20obtained%20on%20a%20sample%20population.%5CnStudy%20aim%5CnThis%20study%20seeks%20to%20introduce%20a%20radiation-free%2C%20automated%203D%20optical%20imaging%20solution%20for%20monitoring%20body%20shape%20and%20composition%20in%20children%20aged%205%5Cu201317.%5CnMethods%5CnWe%20introduce%20an%20automated%2C%20linear%20learning%20method%20to%20predict%20total%20and%20regional%20body%20composition%20of%20children%20aged%205%5Cu201317%20from%203DO%20scans.%20We%20collected%20145%20male%20and%20206%20female%203DO%20scans%20on%20children%20between%20the%20ages%20of%205%20and%2017%20with%20three%20scanners%20from%20independent%20manufacturers.%20We%20used%20an%20automated%20shape%20templating%20method%20first%20introduced%20on%20an%20adult%20population%20to%20fit%20a%20topologically%20consistent%2060%2C000%20vertex%20%2860%5Cu00a0k%29%20mesh%20to%203DO%20scans%20of%20arbitrary%20scanning%20source%20and%20mesh%20topology.%20We%20constructed%20a%20parameterized%20body%20shape%20space%20using%20principal%20component%20analysis%20%28PCA%29%20and%20estimated%20a%20regression%20matrix%20between%20the%20shape%20parameters%20and%20their%20associated%20DXA%20measurements.%20We%20automatically%20fit%20scans%20of%2030%20male%20and%2038%20female%20participants%20from%20a%20held-out%20test%20set%20and%20predicted%2012%20body%20composition%20measurements.%5CnResults%5CnThe%20coefficient%20of%20determination%20%28R2%29%20between%203DO%20predicted%20body%20composition%20and%20DXA%20measurements%20was%20at%20least%200.85%20for%20all%20measurements%20with%20the%20exception%20of%20visceral%20fat%20on%203D%20scan%20predictions.%20Precision%20error%20was%201%5Cu20134%20times%20larger%20than%20that%20of%20DXA.%20No%20predicted%20variable%20was%20significantly%20different%20from%20DXA%20measurement%20except%20for%20male%20trunk%20lean%20mass.%5CnConclusion%5CnOptical%20imaging%20can%20quickly%2C%20safely%2C%20and%20inexpensively%20estimate%20regional%20body%20composition%20in%20children%20aged%205%5Cu201317.%20Frequent%20repeat%20measurements%20can%20be%20taken%20to%20chart%20changes%20in%20body%20adiposity%20over%20time%20without%20risk%20of%20radiation%20overexposure.%22%2C%22date%22%3A%222023-09-01%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1016%5C%2Fj.clnu.2023.07.012%22%2C%22ISSN%22%3A%220261-5614%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS0261561423002315%22%2C%22collections%22%3A%5B%228UXNM7I7%22%2C%22A2J6KQIV%22%5D%2C%22dateModified%22%3A%222023-07-21T23%3A54%3A48Z%22%7D%7D%2C%7B%22key%22%3A%22FGUSXXC7%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wong%20et%20al.%22%2C%22parsedDate%22%3A%222023-07-19%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3EWong%20MC%2C%20Bennett%20JP%2C%20Quon%20B%2C%20Leong%20LT%2C%20Tian%20IY%2C%20Liu%20YE%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS000291652366054X%27%3EAccuracy%20and%20Precision%20of%203D%20Optical%20Imaging%20for%20Body%20Composition%20by%20Age%2C%20BMI%2C%20and%20Ethnicity%3C%5C%2Fa%3E.%20The%20American%20Journal%20of%20Clinical%20Nutrition%20%5BInternet%5D.%202023%20Jul%2019%3B%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DFGUSXXC7%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Accuracy%20and%20Precision%20of%203D%20Optical%20Imaging%20for%20Body%20Composition%20by%20Age%2C%20BMI%2C%20and%20Ethnicity%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%20C.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jonathan%20P.%22%2C%22lastName%22%3A%22Bennett%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Brandon%22%2C%22lastName%22%3A%22Quon%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lambert%20T.%22%2C%22lastName%22%3A%22Leong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Isaac%20Y.%22%2C%22lastName%22%3A%22Tian%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yong%20E.%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Nisa%20N.%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cassidy%22%2C%22lastName%22%3A%22McCarthy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dominic%22%2C%22lastName%22%3A%22Chow%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Sergi%22%2C%22lastName%22%3A%22Pujades%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andrea%20K.%22%2C%22lastName%22%3A%22Garber%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gertraud%22%2C%22lastName%22%3A%22Maskarinec%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Steven%20B.%22%2C%22lastName%22%3A%22Heymsfield%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22Background%5CnThe%20obesity%20epidemic%20brought%20a%20need%20for%20accessible%20methods%20to%20monitor%20body%20composition%2C%20as%20excess%20adiposity%20has%20been%20associated%20with%20cardiovascular%20disease%2C%20metabolic%20disorders%2C%20and%20some%20cancers.%20Recent%20three-dimensional%20optical%20%283DO%29%20imaging%20advancements%20have%20provided%20these%20opportunities%20for%20assessing%20body%20composition.%20However%2C%20the%20accuracy%20and%20precision%20of%20an%20overall%203DO%20body%20composition%20model%20in%20specific%20subgroups%20are%20unknown.%5CnObjective%5CnTo%20evaluate%203DO%5Cu2019s%20accuracy%20and%20precision%20by%20subgroups%20of%20age%2C%20body%20mass%20index%20%28BMI%29%2C%20and%20ethnicity.%5CnMethods%5CnA%20cross-sectional%20analysis%20was%20performed%20using%20data%20from%20the%20Shape%20Up%21%20Adults%20study.%20Each%20participant%20received%20duplicate%203DO%20and%20DXA%20scans.%203DO%20meshes%20were%20digitally%20registered%20and%20reposed%20using%20Meshcapade.%20Principal%20component%20analysis%20was%20performed%20on%203DO%20meshes.%20The%20resulting%20principal%20components%20estimated%20dual-energy%20X-ray%20absorptiometry%20%28DXA%29%20whole-body%20and%20regional%20body%20composition%20using%20stepwise%20forward%20linear%20regression%20with%20five-fold%20cross-validation.%20Duplicate%203DO%20and%20DXA%20scans%20were%20used%20for%20test-retest%20precision.%20Student%5Cu2019s%20t-tests%20were%20performed%20between%203DO%20and%20DXA%20by%20subgroup%20to%20determine%20significant%20differences.%5CnResults%5CnSix%20hundred%20thirty-four%20participants%20%28females%20%3D%20346%29%20had%20completed%20the%20study%20at%20the%20time%20of%20the%20analysis.%203DO%20total%20fat%20mass%20%28FM%29%20in%20the%20entire%20sample%20achieved%20R2%20of%200.94%20with%20RMSE%20of%202.91%20kg%20compared%20to%20DXA%20in%20females%20and%20similarly%20in%20males.%203DO%20total%20FM%20achieved%20a%20%25CV%20%28RMSE%29%20of%201.76%25%20%280.44%20kg%29%2C%20while%20DXA%20was%200.98%25%20%280.24%20kg%29%20in%20females%20and%20similarly%20in%20males.%20There%20were%20no%20mean%20differences%20for%20total%20fat%2C%20fat-free%2C%20percent%20fat%2C%20or%20visceral%20adipose%20tissue%20by%20age%20group%20%28p-value%20%3E%200.068%29.%20However%2C%20there%20were%20mean%20differences%20for%20underweight%2C%20Asian%2C%20and%20black%20females%20as%20well%20as%20NHOPI%20%28p-value%3C0.038%29.%5CnConclusion%5CnA%20single%203DO%20body%20composition%20model%20produced%20accurate%20and%20precise%20body%20composition%20estimates%20that%20can%20be%20used%20on%20diverse%20populations.%20However%2C%20adjustments%20to%20specific%20subgroups%20may%20be%20warranted%20to%20improve%20the%20accuracy%20in%20those%20that%20had%20significant%20differences.%5CnClinical%20Trials%20Number%5CnShape%20Up%21%20Adults%20%28NCT03637855%2C%20https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03637855%29%22%2C%22date%22%3A%222023-07-19%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1016%5C%2Fj.ajcnut.2023.07.010%22%2C%22ISSN%22%3A%220002-9165%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS000291652366054X%22%2C%22collections%22%3A%5B%228UXNM7I7%22%2C%22A2J6KQIV%22%5D%2C%22dateModified%22%3A%222023-07-24T20%3A55%3A54Z%22%7D%7D%2C%7B%22key%22%3A%22Y8ZYI33V%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22McCarthy%20et%20al.%22%2C%22parsedDate%22%3A%222023-02-08%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3EMcCarthy%20C%2C%20Tinsley%20GM%2C%20Yang%20S%2C%20Irving%20BA%2C%20Wong%20MC%2C%20Bennett%20JP%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS0002916523040984%27%3ESmartphone%20Prediction%20of%20Skeletal%20Muscle%20Mass%3A%20Model%20Development%20and%20Validation%20in%20Adults%3C%5C%2Fa%3E.%20The%20American%20Journal%20of%20Clinical%20Nutrition%20%5BInternet%5D.%202023%20Feb%208%3B%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DY8ZYI33V%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Smartphone%20Prediction%20of%20Skeletal%20Muscle%20Mass%3A%20Model%20Development%20and%20Validation%20in%20Adults%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cassidy%22%2C%22lastName%22%3A%22McCarthy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Grant%20M.%22%2C%22lastName%22%3A%22Tinsley%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Shengping%22%2C%22lastName%22%3A%22Yang%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Brian%20A.%22%2C%22lastName%22%3A%22Irving%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%20C.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jonathan%20P.%22%2C%22lastName%22%3A%22Bennett%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Steven%20B.%22%2C%22lastName%22%3A%22Heymsfield%22%7D%5D%2C%22abstractNote%22%3A%22Background%5CnSkeletal%20muscle%20%28SM%29%20is%20a%20large%20and%20clinically%20relevant%20body%20component%20that%20has%20been%20difficult%20and%20impractical%20to%20quantify%20outside%20of%20specialized%20facilities.%20Advances%20in%20smartphone%20technology%20now%20provide%20the%20opportunity%20to%20quantify%20multiple%20body%20surface%20dimensions%20including%20circumferences%2C%20lengths%2C%20surface%20areas%2C%20and%20volumes%5CnObjective%5CnThe%20current%20study%20aim%20was%20to%20test%20the%20hypothesis%20that%20anthropometric%20body%20measurements%20acquired%20with%20a%20smartphone%20application%20can%20be%20used%20to%20accurately%20estimate%20an%20adult%5Cu2019s%20level%20of%20muscularity%5CnMethods%5CnAppendicular%20lean%20mass%20%28ALM%29%20measured%20by%20dual-energy%20X-ray%20absorptiometry%20%28DXA%29%20served%20as%20the%20reference%20for%20muscularity%20in%20a%20sample%20of%20322%20adults.%20Participants%20also%20had%20digital%20anthropometric%20dimensions%20%28circumferences%2C%20lengths%3B%20regional%20and%20total%20body%20surface%20areas%20and%20volumes%29%20quantified%20with%20a%2020-camera%203D%20imaging%20system.%20Least%20Absolute%20Shrinkage%20and%20Selection%20Operator%20%28LASSO%29%20regression%20procedures%20were%20used%20to%20develop%20the%20ALM%20prediction%20equations%20in%20a%20portion%20of%20the%20sample%20and%20these%20models%20were%20tested%20in%20the%20remainder%20of%20the%20sample.%20The%20accuracy%20of%20the%20prediction%20models%20was%20then%20cross%20validated%20in%20a%20second%20independent%20sample%20of%2053%20adults%20who%20had%20ALM%20estimates%20by%20DXA%20and%20the%20same%20digital%20anthropometric%20estimates%20acquired%20with%20a%20smartphone%20application%5CnResults%5CnLASSO%20models%20included%20multiple%20significant%20demographic%20and%203D%20digital%20anthropometric%20predictor%20variables.%20Evaluation%20of%20the%20models%20in%20the%20testing%20sample%20indicated%20respective%20root-mean-square%20errors%20%28RMSEs%29%20in%20females%20and%20males%20of%201.56%20kg%20and%201.53%20kg%20and%20R2s%20of%200.74%20and%200.90.%20Cross%20validation%20of%20the%20LASSO%20models%20in%20the%20smartphone%20application%20group%20yielded%20respective%20RMSEs%20in%20females%20and%20males%20of%201.78%20kg%20and%201.50%20kg%20and%20R2s%20of%200.79%20and%200.95%3B%20no%20significant%20differences%20or%20bias%20between%20measured%20and%20predicted%20ALM%20values%20were%20observed%5CnConclusions%5CnSmartphone%20image%20capture%20capabilities%20combined%20with%20device%20software%20applications%20can%20now%20provide%20accurate%20renditions%20of%20the%20adult%20muscularity%20phenotype%20outside%20of%20specialized%20laboratory%20facilities.%5CnStudy%20Registration%5CnClinicalTrials.gov%3A%20NCT03637855%20%28https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03637855%29%2C%20NCT05217524%20%28https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT05217524%29%2C%20and%20NCT03771417%20%28https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03771417%29.%22%2C%22date%22%3A%222023-02-08%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1016%5C%2Fj.ajcnut.2023.02.003%22%2C%22ISSN%22%3A%220002-9165%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS0002916523040984%22%2C%22collections%22%3A%5B%228UXNM7I7%22%2C%22A2J6KQIV%22%5D%2C%22dateModified%22%3A%222023-02-14T01%3A47%3A56Z%22%7D%7D%2C%7B%22key%22%3A%22IF2AMWTZ%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wong%20et%20al.%22%2C%22parsedDate%22%3A%222023%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3EWong%20MC%2C%20Bennett%20JP%2C%20Leong%20LT%2C%20Tian%20IY%2C%20Liu%20YE%2C%20Kelly%20NN%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS0002916523041527%27%3EMonitoring%20Body%20Composition%20Change%20for%20Intervention%20Studies%20with%20Advancing%203D%20Optical%20Imaging%20Technology%20in%20Comparison%20to%20Dual-Energy%20X-Ray%20Absorptiometry%3C%5C%2Fa%3E.%20The%20American%20Journal%20of%20Clinical%20Nutrition%20%5BInternet%5D.%202023%3B%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DIF2AMWTZ%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Monitoring%20Body%20Composition%20Change%20for%20Intervention%20Studies%20with%20Advancing%203D%20Optical%20Imaging%20Technology%20in%20Comparison%20to%20Dual-Energy%20X-Ray%20Absorptiometry%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%20C.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jonathan%20P.%22%2C%22lastName%22%3A%22Bennett%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lambert%20T.%22%2C%22lastName%22%3A%22Leong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Isaac%20Y.%22%2C%22lastName%22%3A%22Tian%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yong%20E.%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Nisa%20N.%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cassidy%22%2C%22lastName%22%3A%22McCarthy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Julia%20M.W.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cara%20B.%22%2C%22lastName%22%3A%22Ebbeling%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22David%20S.%22%2C%22lastName%22%3A%22Ludwig%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Brian%20A.%22%2C%22lastName%22%3A%22Irving%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Matthew%20C.%22%2C%22lastName%22%3A%22Scott%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22James%22%2C%22lastName%22%3A%22Stampley%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Brett%22%2C%22lastName%22%3A%22Davis%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Neil%22%2C%22lastName%22%3A%22Johannsen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rachel%22%2C%22lastName%22%3A%22Matthews%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cullen%22%2C%22lastName%22%3A%22Vincellette%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andrea%20K.%22%2C%22lastName%22%3A%22Garber%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gertraud%22%2C%22lastName%22%3A%22Maskarinec%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ethan%22%2C%22lastName%22%3A%22Weiss%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jennifer%22%2C%22lastName%22%3A%22Rood%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Alyssa%20N.%22%2C%22lastName%22%3A%22Varanoske%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Stefan%20M.%22%2C%22lastName%22%3A%22Pasiakos%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Steven%20B.%22%2C%22lastName%22%3A%22Heymsfield%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22Abstract%3A%20Background%5CnRecent%203-dimensional%20optical%20%283DO%29%20imaging%20advancements%20have%20provided%20more%20accessible%2C%20affordable%2C%20and%20self-operating%20opportunities%20for%20assessing%20body%20composition.%203DO%20is%20accurate%20and%20precise%20in%20clinical%20measures%20made%20by%20DXA.%20However%2C%20the%20sensitivity%20for%20monitoring%20body%20composition%20change%20over%20time%20with%203DO%20body%20shape%20imaging%20is%20unknown.%5CnObjectives%5CnThis%20study%20aimed%20to%20evaluate%20the%20ability%20of%203DO%20in%20monitoring%20body%20composition%20changes%20across%20multiple%20intervention%20studies.%5CnMethods%5CnA%20retrospective%20analysis%20was%20performed%20using%20intervention%20studies%20on%20healthy%20adults%20that%20were%20complimentary%20to%20the%20cross-sectional%20study%2C%20Shape%20Up%21%20Adults.%20Each%20participant%20received%20a%20DXA%20%28Hologic%20Discovery%5C%2FA%20system%29%20and%203DO%20%28Fit3D%20ProScanner%29%20scan%20at%20the%20baseline%20and%20follow-up.%203DO%20meshes%20were%20digitally%20registered%20and%20reposed%20using%20Meshcapade%20to%20standardize%20the%20vertices%20and%20pose.%20Using%20an%20established%20statistical%20shape%20model%2C%20each%203DO%20mesh%20was%20transformed%20into%20principal%20components%2C%20which%20were%20used%20to%20predict%20whole-body%20and%20regional%20body%20composition%20values%20using%20published%20equations.%20Body%20composition%20changes%20%28follow-up%20minus%20the%20baseline%29%20were%20compared%20with%20those%20of%20DXA%20using%20a%20linear%20regression%20analysis.%5CnResults%5CnThe%20analysis%20included%20133%20participants%20%2845%20females%29%20in%206%20studies.%20The%20mean%20%28SD%29%20length%20of%20follow-up%20was%2013%20%285%29%20wk%20%28range%3A%203%5Cu201323%20wk%29.%20Agreement%20between%203DO%20and%20DXA%20%28R2%29%20for%20changes%20in%20total%20FM%2C%20total%20FFM%2C%20and%20appendicular%20lean%20mass%20were%200.86%2C%200.73%2C%20and%200.70%2C%20with%20root%20mean%20squared%20errors%20%28RMSEs%29%20of%201.98%20kg%2C%201.58%20kg%2C%20and%200.37%20kg%2C%20in%20females%20and%200.75%2C%200.75%2C%20and%200.52%20with%20RMSEs%20of%202.31%20kg%2C%201.77%20kg%2C%20and%200.52%20kg%2C%20in%20males%2C%20respectively.%20Further%20adjustment%20with%20demographic%20descriptors%20improved%20the%203DO%20change%20agreement%20to%20changes%20observed%20with%20DXA.%5CnConclusions%5CnCompared%20with%20DXA%2C%203DO%20was%20highly%20sensitive%20in%20detecting%20body%20shape%20changes%20over%20time.%20The%203DO%20method%20was%20sensitive%20enough%20to%20detect%20even%20small%20changes%20in%20body%20composition%20during%20intervention%20studies.%20The%20safety%20and%20accessibility%20of%203DO%20allows%20users%20to%20self-monitor%20on%20a%20frequent%20basis%20throughout%20interventions.%20This%20trial%20was%20registered%20at%20clinicaltrials.gov%20as%20NCT03637855%20%28Shape%20Up%21%20Adults%3B%20https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03637855%29%3B%20NCT03394664%20%28Macronutrients%20and%20Body%20Fat%20Accumulation%3A%20A%20Mechanistic%20Feeding%20Study%3B%20https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03394664%29%3B%20NCT03771417%20%28Resistance%20Exercise%20and%20Low-Intensity%20Physical%20Activity%20Breaks%20in%20Sedentary%20Time%20to%20Improve%20Muscle%20and%20Cardiometabolic%20Health%3B%20https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03771417%29%3B%20NCT03393195%20%28Time%20Restricted%20Eating%20on%20Weight%20Loss%3B%20https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT03393195%29%2C%20and%20NCT04120363%20%28Trial%20of%20Testosterone%20Undecanoate%20for%20Optimizing%20Performance%20During%20Military%20Operations%3B%20https%3A%5C%2F%5C%2Fclinicaltrials.gov%5C%2Fct2%5C%2Fshow%5C%2FNCT04120363%29.%22%2C%22date%22%3A%222023%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1016%5C%2Fj.ajcnut.2023.02.006%22%2C%22ISSN%22%3A%220002-9165%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.sciencedirect.com%5C%2Fscience%5C%2Farticle%5C%2Fpii%5C%2FS0002916523041527%22%2C%22collections%22%3A%5B%22A2J6KQIV%22%5D%2C%22dateModified%22%3A%222023-03-13T17%3A47%3A41Z%22%7D%7D%2C%7B%22key%22%3A%22VSDENHJ3%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22McCarthy%2C%20C.%20et%20al.%22%2C%22parsedDate%22%3A%222022-09-05%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3EMcCarthy%2C%20C.%2C%20Schoeller%2C%20D.%2C%20Brown%2C%20J.%20C.%2C%20Gonzalez%2C%20M.%20C.%2C%20Varanoske%2C%20A.%20N.%2C%20Cataldi%2C%20D.%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Fonlinelibrary.wiley.com%5C%2Fdoi%5C%2F10.1002%5C%2Fjcsm.13083%27%3ED%283%29%20-creatine%20dilution%20for%20skeletal%20muscle%20mass%20measurement%3A%20historical%20development%20and%20current%20status%3C%5C%2Fa%3E.%20J%20Cachexia%20Sarcopenia%20Muscle%20%5BInternet%5D.%202022%20Sep%205%3B%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DVSDENHJ3%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22D%283%29%20-creatine%20dilution%20for%20skeletal%20muscle%20mass%20measurement%3A%20historical%20development%20and%20current%20status%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22McCarthy%2C%20C.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Schoeller%2C%20D.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Brown%2C%20J.%20C.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Gonzalez%2C%20M.%20C.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Varanoske%2C%20A.%20N.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Cataldi%2C%20D.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Shepherd%2C%20J.%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22name%22%3A%22Heymsfield%2C%20S.%20B.%22%7D%5D%2C%22abstractNote%22%3A%22The%20French%20chemist%20Michel%20Eug%5Cu00e8ne%20Chevreul%20discovered%20creatine%20in%20meat%20two%20centuries%20ago.%20Extensive%20biochemical%20and%20physiological%20studies%20of%20this%20organic%20molecule%20followed%20with%20confirmation%20that%20creatine%20is%20found%20within%20the%20cytoplasm%20and%20mitochondria%20of%20human%20skeletal%20muscles.%20Two%20groups%20of%20investigators%20exploited%20these%20relationships%20five%20decades%20ago%20by%20first%20estimating%20the%20creatine%20pool%20size%20in%20vivo%20with%20%2814%29%20C%20and%20%2815%29%20N%20labelled%20isotopes.%20Skeletal%20muscle%20mass%20%28kg%29%20was%20then%20calculated%20by%20dividing%20the%20creatine%20pool%20size%20%28g%29%20by%20muscle%20creatine%20concentration%20%28g%5C%2Fkg%29%20measured%20on%20a%20single%20muscle%20biopsy%20or%20estimated%20from%20the%20literature.%20This%20approach%20for%20quantifying%20skeletal%20muscle%20mass%20is%20generating%20renewed%20interest%20with%20the%20recent%20introduction%20of%20a%20practical%20stable%20isotope%20%28creatine-%28methyl-d%283%29%20%29%29%20dilution%20method%20for%20estimating%20the%20creatine%20pool%20size%20across%20the%20full%20human%20lifespan.%20The%20need%20for%20a%20muscle%20biopsy%20has%20been%20eliminated%20by%20assuming%20a%20constant%20value%20for%20whole-body%20skeletal%20muscle%20creatine%20concentration%20of%204.3%5Cu00a0g%5C%2Fkg%20wet%20weight.%20The%20current%20single%20compartment%20model%20of%20estimating%20creatine%20pool%20size%20and%20skeletal%20muscle%20mass%20rests%20on%20four%20main%20assumptions%3A%20tracer%20absorption%20is%20complete%3B%20tracer%20is%20all%20retained%3B%20tracer%20is%20distributed%20solely%20in%20skeletal%20muscle%3B%20and%20skeletal%20muscle%20creatine%20concentration%20is%20known%20and%20constant.%20Three%20of%20these%20assumptions%20are%20false%20to%20varying%20degrees.%20Not%20all%20tracer%20is%20retained%20with%20urinary%20isotope%20losses%20ranging%20from%200%25%20to%209%25%3B%20an%20empirical%20equation%20requiring%20further%20validation%20is%20used%20to%20correct%20for%20spillage.%20Not%20all%20tracer%20is%20distributed%20in%20skeletal%20muscle%20with%20non-muscle%20creatine%20sources%20ranging%20from%202%25%20to%2010%25%20with%20a%20definitive%20value%20lacking.%20Lastly%2C%20skeletal%20muscle%20creatine%20concentration%20is%20not%20constant%20and%20varies%20between%20muscles%20%28e.g.%203.89-4.62%5Cu00a0g%5C%2Fkg%29%2C%20with%20diets%20%28e.g.%20vegetarian%20and%20omnivore%29%2C%20across%20age%20groups%20%28e.g.%20middle-age%2C%20~4.5%5Cu00a0g%5C%2Fkg%3B%20old-age%2C%204.0%5Cu00a0g%5C%2Fkg%29%2C%20activity%20levels%20%28e.g.%20athletes%2C%20~5%5Cu00a0g%5C%2Fkg%29%20and%20in%20disease%20states%20%28e.g.%20muscular%20dystrophies%2C%20%3C3%5Cu00a0g%5C%2Fkg%29.%20Some%20of%20the%20variability%20in%20skeletal%20muscle%20creatine%20concentrations%20can%20be%20attributed%20to%20heterogeneity%20in%20the%20proportions%20of%20wet%20skeletal%20muscle%20as%20myofibres%2C%20connective%20tissues%2C%20and%20fat.%20These%20observations%20raise%20serious%20concerns%20regarding%20the%20accuracy%20of%20the%20deuterated-creatine%20dilution%20method%20for%20estimating%20total%20body%20skeletal%20muscle%20mass%20as%20now%20defined%20by%20cadaver%20analyses%20of%20whole%20wet%20tissues%20and%20in%20vivo%20approaches%20such%20as%20magnetic%20resonance%20imaging.%20A%20new%20framework%20is%20needed%20in%20thinking%20about%20how%20this%20potentially%20valuable%20method%20for%20measuring%20the%20creatine%20pool%20size%20in%20vivo%20can%20be%20used%20in%20the%20future%20to%20study%20skeletal%20muscle%20biology%20in%20health%20and%20disease.%22%2C%22date%22%3A%22Sep%205%202022%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1002%5C%2Fjcsm.13083%22%2C%22ISSN%22%3A%222190-5991%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fonlinelibrary.wiley.com%5C%2Fdoi%5C%2F10.1002%5C%2Fjcsm.13083%22%2C%22collections%22%3A%5B%22SJAER8EK%22%2C%228UXNM7I7%22%5D%2C%22dateModified%22%3A%222022-09-12T18%3A18%3A11Z%22%7D%7D%2C%7B%22key%22%3A%22BNQ77S2S%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Tian%20et%20al.%22%2C%22parsedDate%22%3A%222022-07-15%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3ETian%20I%2C%20Wong%20M%2C%20Kennedy%20S%2C%20Kelly%20N%2C%20Liu%20E%2C%20Garber%20A%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Faapm.onlinelibrary.wiley.com%5C%2Fdoi%5C%2F10.1002%5C%2Fmp.15843%27%3EA%20Device-Agnostic%20Shape%20Model%20for%20Automated%20Body%20Composition%20Estimates%20from%203D%20Optical%20Scans%3C%5C%2Fa%3E.%20Medical%20Physics%20%5BInternet%5D.%202022%20Jul%2015%3B%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DBNQ77S2S%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22A%20Device-Agnostic%20Shape%20Model%20for%20Automated%20Body%20Composition%20Estimates%20from%203D%20Optical%20Scans%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22IY%22%2C%22lastName%22%3A%22Tian%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22MC%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22S%22%2C%22lastName%22%3A%22Kennedy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22NN%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22EY%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22AK%22%2C%22lastName%22%3A%22Garber%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22SB%22%2C%22lastName%22%3A%22Heymsfield%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22B%22%2C%22lastName%22%3A%22Curless%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22JA%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22Background%3A%20Many%20predictors%20of%20morbidity%20caused%20by%20metabolic%20disease%20are%20associated%20with%20body%20shape.%203D%20optical%20%283DO%29%20scanning%20captures%20body%20shape%20and%20has%20been%20shown%20to%20accurately%20and%20precisely%20predict%20body%20composition%20variables%20associated%20with%20mortality%20risk.%203DO%20is%20safer%2C%20less%20expensive%2C%20and%20more%20accessible%20than%20criterion%20body%20composition%20assessment%20methods%20such%20as%20dual-energy%20X-ray%20absorptiometry%20%28DXA%29.%20However%2C%203DO%20scanning%20has%20not%20been%20standardized%20across%20manufacturers%20for%20pose%2C%20mesh%20resolution%2C%20and%20post%20processing%20methods.%5Cn%5CnPurpose%3A%20We%20introduce%20a%20scanner-agnostic%20algorithm%20that%20automatically%20fits%20a%20topologically%20consistent%20human%20mesh%20to%203DO%20scanned%20point%20clouds%20and%20predicts%20clinically%20important%20body%20metrics%20using%20a%20standardized%20body%20shape%20model.%20Our%20models%20transform%20raw%20scans%20captured%20by%20any%203DO%20scanner%20into%20fixed%20topology%20meshes%20with%20anatomical%20consistency%2C%20standardizing%20the%20outputs%20of%203DO%20scans%20across%20manufacturers%20and%20allowing%20for%20the%20use%20of%20common%20prediction%20models%20across%20scanning%20devices.%5Cn%5CnMethods%3A%20A%20fixed-topology%20body%20mesh%20template%20was%20automatically%20registered%20to%20848%20training%20scans%20from%20three%20different%203DO%20systems.%20Participants%20were%20between%2018%20and%2089%20years%20old%20with%20BMI%20ranging%20from%2014%20to%2052%20kg%5C%2Fm2%20.%20Scans%20were%20registered%20by%20first%20performing%20a%20coarse%20nearest%20neighbor%20alignment%20between%20the%20template%20and%20the%20input%20scan%20with%20an%20anatomically%20constrained%20PCA%20domain%20deformation%20using%20a%20device%20and%20gender%20specific%20bootstrap%20basis%20trained%20on%2070%20seed%20scans%20each.%20The%20template%20mesh%20was%20then%20optimized%20to%20fit%20the%20target%20with%20a%20smooth%20per-vertex%20surface-to-surface%20deformation.%20A%20combined%20unified%20PCA%20model%20was%20created%20from%20the%20superset%20of%20all%20automatically%20fit%20training%20scans%20including%20all%20three%20devices.%20Body%20composition%20predictions%20to%20DXA%20measurements%20were%20learned%20from%20the%20training%20mesh%20PCA%20coefficients%20using%20linear%20regression.%20Using%20this%20final%20unified%20model%2C%20we%20tested%20the%20accuracy%20of%20our%20body%20composition%20models%20on%20a%20withheld%20sample%20of%20562%20scans%20by%20fitting%20a%20PCA%20parameterized%20template%20mesh%20to%20each%20raw%20scan%20and%20predicting%20the%20expected%20body%20composition%20metrics%20from%20the%20principal%20components%20using%20the%20learned%20regression%20model.%5Cn%5CnResults%3A%20We%20achieved%20coefficients%20of%20determination%20%28R2%20%29%20above%200.8%20on%20all%20nine%20fat%20and%20lean%20predictions%20except%20female%20visceral%20fat%20%280.77%29.%20R2%20was%20as%20high%20as%200.94%20%28total%20fat%20and%20lean%2C%20trunk%20fat%29%20and%20all%20root-mean-squared%20errors%20%28RMSE%29%20were%20below%203.0%20kg.%20All%20predicted%20body%20composition%20variables%20were%20not%20significantly%20different%20from%20reference%20DXA%20measurements%20except%20for%20visceral%20fat%20and%20female%20trunk%20fat.%20Repeatability%20precision%20as%20measured%20by%20the%20coefficient%20of%20variation%20%28%25CV%29%20was%20around%202-3x%20worse%20than%20DXA%20precision%2C%20with%20visceral%20fat%20%25CV%20below%202x%20DXA%20%25CV%20and%20female%20total%20fat%20mass%20at%205x.%5Cn%5CnConclusions%3A%20Our%20method%20provides%20an%20accurate%2C%20automated%2C%20and%20scanner%20agnostic%20framework%20for%20standardizing%203DO%20scans%20and%20a%20low%20cost%2C%20radiation-free%20alternative%20to%20criterion%20radiology%20imaging%20for%20body%20composition%20analysis.%20We%20published%20a%20web-app%20version%20of%20this%20work%20at%20https%3A%5C%2F%5C%2Fshapeup.shepherdresearchlab.org%5C%2F3do-bodycomp-analyzer%5C%2F%20that%20accepts%20mesh%20file%20uploads%20and%20returns%20templated%20meshes%20with%20body%20composition%20predictions%20for%20demo%20purposes.%20This%20article%20is%20protected%20by%20copyright.%20All%20rights%20reserved.%22%2C%22date%22%3A%222022%20Jul%2015%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1002%5C%2Fmp.15843%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Faapm.onlinelibrary.wiley.com%5C%2Fdoi%5C%2F10.1002%5C%2Fmp.15843%22%2C%22collections%22%3A%5B%22SJAER8EK%22%2C%228UXNM7I7%22%5D%2C%22dateModified%22%3A%222022-07-19T22%3A44%3A24Z%22%7D%7D%2C%7B%22key%22%3A%222NI33PDH%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Cataldi%20et%20al.%22%2C%22parsedDate%22%3A%222022-06-13%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%20%20%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3ECataldi%20D%2C%20Bennett%20JP%2C%20Leong%20LT%2C%20Liu%20YE%2C%20Kelly%20NN%2C%20Shepherd%20JA.%20Muscular%20Function%20and%20Strength%20Predictions%20Using%20DXA.%20Oral%20Presentation%20presented%20at%3A%20International%20Workshop%20on%20Quantitative%20Musculoskeletal%20Imaging%3B%202022%20Jun%2013%3B%20Noordwijk%2C%20Netherlands.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3D2NI33PDH%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%20%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22presentation%22%2C%22title%22%3A%22Muscular%20Function%20and%20Strength%20Predictions%20Using%20DXA%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22D.%22%2C%22lastName%22%3A%22Cataldi%22%7D%2C%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22Jonathon%20P.%22%2C%22lastName%22%3A%22Bennett%22%7D%2C%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22Lambert%20T.%22%2C%22lastName%22%3A%22Leong%22%7D%2C%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22Yong%20En%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22Nisa%20N.%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22Lean%20body%20mass%20%28LBM%29%20and%20function%20are%20well-known%20predictors%20of%20overall%20health%20and%20quality%20of%20life%20and%20have%20played%20an%20integral%20part%20in%20athletic%20and%20medical%20research%20%281%29%20.%20Isokinetic%20dynamometry%20is%20widely%20regarded%20as%20the%20gold%20standard%20for%20assessing%20muscle%20strength.%20Methods%20of%20determining%20LBM%20or%20skeletal%20muscle%20mass%20%28SMM%29%20include%20DXA%2C%20BIA%2C%203D%20optical%20and%20recently%2C%20a%20novel%20method%20of%20isotopic%20labeled%20D%203%20-Cr%20%282%29%20.%20Few%20studies%20reported%20the%20association%20of%20D%203%20-Cr%20derived%20SMM%20and%20its%20relationship%20with%20muscle%20function%20and%20strength%20and%20no%20study%20has%20investigated%20the%20associations%20within%20an%20athletic%20population.%20This%20study%20investigates%20the%20associations%2C%20predictive%20accuracy%20and%20power%20of%20using%20LBM%20obtained%20from%20various%20means%20in%20order%20to%20provide%20a%20valid%20measure%20of%20muscular%20strength%20in%20the%20general%20population%20and%20athletes.%22%2C%22date%22%3A%22June%2013-17%2C%202022%22%2C%22url%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22SJAER8EK%22%2C%22IDI3J9GR%22%5D%2C%22dateModified%22%3A%222022-07-09T02%3A35%3A09Z%22%7D%7D%2C%7B%22key%22%3A%228IIXG7YS%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Cataldi%20et%20al.%22%2C%22parsedDate%22%3A%222022-06-06%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3ECataldi%20D%2C%20Bennett%20JP%2C%20Quon%20BK%2C%20Liu%20YE%2C%20Heymsfield%20SB%2C%20Kelly%20T%2C%20et%20al.%20%3Ca%20target%3D%27_blank%27%20href%3D%27https%3A%5C%2F%5C%2Facademic.oup.com%5C%2Fjn%5C%2Fadvance-article-abstract%5C%2Fdoi%5C%2F10.1093%5C%2Fjn%5C%2Fnxac116%5C%2F6602363%27%3EAgreement%20and%20Precision%20of%20Deuterium%20Dilution%20for%20Total%20Body%20Water%20and%20Multicompartment%20Body%20Composition%20Assesment%20in%20Collegiate%20Athletes%3C%5C%2Fa%3E.%20The%20Journal%20of%20Nutrition%20%5BInternet%5D.%202022%20Jun%206%3B%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3D8IIXG7YS%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Agreement%20and%20Precision%20of%20Deuterium%20Dilution%20for%20Total%20Body%20Water%20and%20Multicompartment%20Body%20Composition%20Assesment%20in%20Collegiate%20Athletes%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Devon%22%2C%22lastName%22%3A%22Cataldi%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jonathan%20P.%22%2C%22lastName%22%3A%22Bennett%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Brandon%20K.%22%2C%22lastName%22%3A%22Quon%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Young%20En%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Steven%20B.%22%2C%22lastName%22%3A%22Heymsfield%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%2206%20June%202022%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1093%5C%2Fjn%5C%2Fnxac116%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Facademic.oup.com%5C%2Fjn%5C%2Fadvance-article-abstract%5C%2Fdoi%5C%2F10.1093%5C%2Fjn%5C%2Fnxac116%5C%2F6602363%22%2C%22collections%22%3A%5B%22SJAER8EK%22%2C%228UXNM7I7%22%5D%2C%22dateModified%22%3A%222022-07-08T21%3A46%3A50Z%22%7D%7D%2C%7B%22key%22%3A%22ZDXU35RV%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Leong%22%2C%22parsedDate%22%3A%222022-04-07%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%20%20%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3ELeong%20L.%20Modular%20artificial%20intelligence%20models%20for%20body%20composition%20research.%20Poster%20presented%20at%3A%202022%20Biomedical%20Sciences%20%26amp%3B%20Health%20Disparities%20Symposium%3B%202022%20Apr%207%3B%20Honolulu%2C%20HI.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DZDXU35RV%27%3ECite%3C%5C%2Fa%3E%20%20%3Ca%20title%3D%27Download%27%20class%3D%27zp-DownloadURL%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.dl.php%3Fapi_user_id%3D2318789%26amp%3Bdlkey%3DZN9PTPWF%26amp%3Bcontent_type%3Dapplication%5C%2Fpdf%27%3EDownload%3C%5C%2Fa%3E%3C%5C%2Fdiv%3E%5Cn%20%20%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22presentation%22%2C%22title%22%3A%22Modular%20artificial%20intelligence%20models%20for%20body%20composition%20research%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22Lambert%22%2C%22lastName%22%3A%22Leong%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Michael%20C.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Yong%20E.%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Nisa%20N.%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Yannik%22%2C%22lastName%22%3A%22Glaser%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Peter%22%2C%22lastName%22%3A%22Sadowski%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Steven%20B%22%2C%22lastName%22%3A%22Heymsfield%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%224%5C%2F7%5C%2F2022%22%2C%22url%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22SJAER8EK%22%2C%22IDI3J9GR%22%5D%2C%22dateModified%22%3A%222022-06-08T20%3A07%3A06Z%22%7D%7D%2C%7B%22key%22%3A%22NQJGQ46C%22%2C%22library%22%3A%7B%22id%22%3A2318789%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wong%22%2C%22parsedDate%22%3A%222022-04-07%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%20style%3D%5C%22clear%3A%20left%3B%20%5C%22%3E%5Cn%20%20%20%20%3Cdiv%20class%3D%5C%22csl-left-margin%5C%22%20style%3D%5C%22float%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%5C%22%3E1.%3C%5C%2Fdiv%3E%3Cdiv%20class%3D%5C%22csl-right-inline%5C%22%20style%3D%5C%22margin%3A%200%20.4em%200%201.5em%3B%5C%22%3EWong%20MC.%20Evaluation%20of%20Body%20Shape%20as%20a%20Space-Feasible%20Body%20Composition%20Assessment.%20Poster%20presented%20at%3A%202022%20Biomedical%20Sciences%20%26amp%3B%20Health%20Disparities%20Symposium%3B%202022%20Apr%207%3B%20Honolulu%2C%20HI.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2318789%26amp%3Bitem_key%3DNQJGQ46C%27%3ECite%3C%5C%2Fa%3E%20%20%3Ca%20title%3D%27Download%27%20class%3D%27zp-DownloadURL%27%20href%3D%27https%3A%5C%2F%5C%2Fshepherdresearchlab.org%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.dl.php%3Fapi_user_id%3D2318789%26amp%3Bdlkey%3DDZ956D89%26amp%3Bcontent_type%3Dapplication%5C%2Fpdf%27%3EDownload%3C%5C%2Fa%3E%3C%5C%2Fdiv%3E%5Cn%20%20%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22presentation%22%2C%22title%22%3A%22Evaluation%20of%20Body%20Shape%20as%20a%20Space-Feasible%20Body%20Composition%20Assessment%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22presenter%22%2C%22firstName%22%3A%22Michael%20C.%22%2C%22lastName%22%3A%22Wong%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Jonathan%22%2C%22lastName%22%3A%22Bennett%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Yong%20E.%22%2C%22lastName%22%3A%22Liu%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22Nisa%20N.%22%2C%22lastName%22%3A%22Kelly%22%7D%2C%7B%22creatorType%22%3A%22contributor%22%2C%22firstName%22%3A%22John%20A.%22%2C%22lastName%22%3A%22Shepherd%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%224%5C%2F7%5C%2F2022%22%2C%22url%22%3A%22%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22SJAER8EK%22%2C%22IDI3J9GR%22%5D%2C%22dateModified%22%3A%222022-06-08T20%3A07%3A06Z%22%7D%7D%5D%7D
1.
Tian IY, Wong MC, Nguyen WM, Kennedy S, McCarthy C, Kelly NN, et al. Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations. Clinical Nutrition [Internet]. 2023 Sep 1;42(9):1619–30. Cite
1.
Wong MC, Bennett JP, Quon B, Leong LT, Tian IY, Liu YE, et al. Accuracy and Precision of 3D Optical Imaging for Body Composition by Age, BMI, and Ethnicity. The American Journal of Clinical Nutrition [Internet]. 2023 Jul 19; Cite
1.
McCarthy C, Tinsley GM, Yang S, Irving BA, Wong MC, Bennett JP, et al. Smartphone Prediction of Skeletal Muscle Mass: Model Development and Validation in Adults. The American Journal of Clinical Nutrition [Internet]. 2023 Feb 8; Cite
1.
Wong MC, Bennett JP, Leong LT, Tian IY, Liu YE, Kelly NN, et al. Monitoring Body Composition Change for Intervention Studies with Advancing 3D Optical Imaging Technology in Comparison to Dual-Energy X-Ray Absorptiometry. The American Journal of Clinical Nutrition [Internet]. 2023; Cite
1.
McCarthy, C., Schoeller, D., Brown, J. C., Gonzalez, M. C., Varanoske, A. N., Cataldi, D., et al. D(3) -creatine dilution for skeletal muscle mass measurement: historical development and current status. J Cachexia Sarcopenia Muscle [Internet]. 2022 Sep 5; Cite
1.
Tian I, Wong M, Kennedy S, Kelly N, Liu E, Garber A, et al. A Device-Agnostic Shape Model for Automated Body Composition Estimates from 3D Optical Scans. Medical Physics [Internet]. 2022 Jul 15; Cite
1.
Cataldi D, Bennett JP, Leong LT, Liu YE, Kelly NN, Shepherd JA. Muscular Function and Strength Predictions Using DXA. Oral Presentation presented at: International Workshop on Quantitative Musculoskeletal Imaging; 2022 Jun 13; Noordwijk, Netherlands. Cite
1.
Cataldi D, Bennett JP, Quon BK, Liu YE, Heymsfield SB, Kelly T, et al. Agreement and Precision of Deuterium Dilution for Total Body Water and Multicompartment Body Composition Assesment in Collegiate Athletes. The Journal of Nutrition [Internet]. 2022 Jun 6; Cite
View more publications on our Publication page.