Austin J. Graybeal , Sydney H. Swafford , Abby T. Compton , Megan E. Renna , Tanner Thorsen , Jon Stavres
{"title":"从智能手机数字人体测量学预测骨矿物质含量：评估现有应用并开发新的预测模型","authors":"Austin J. Graybeal , Sydney H. Swafford , Abby T. Compton , Megan E. Renna , Tanner Thorsen , Jon Stavres","doi":"10.1016/j.jocd.2024.101537","DOIUrl":null,"url":null,"abstract":"<div><div><em>Introduction/Background:</em> Bone mineral content (BMC) is most commonly evaluated using dual-energy X-ray absorptiometry (DXA), but there are several challenges that limit use of DXA during routine care. Breakthroughs in digital imaging now allow smartphone applications to automate important anthropometrics that can predict several body composition components. However, it is unknown whether the anthropometrics automated using smartphone applications can predict DXA-derived BMC.</div><div><em>Methodology:</em> A total of 214 participants (129 F, 85 M) had BMC measurements collected from an existing proprietary prediction equation, embedded within a smartphone application (MeThreeSixty), and evaluated against DXA. LASSO regression was then used to develop a new BMC prediction equation using the anthropometric estimates produced by the smartphone application in a portion of the participants (n = 174), which was subsequently evaluated against DXA in the remaining sample (n = 40). BMC z-scores were calculated and used to identify the prevalence of <em>low BMC</em> for the existing and newly developed smartphone prediction equations and evaluated against DXA-derived z-scores.</div><div><em>Results:</em> Neither BMC estimates (R<sup>2</sup>: 0.72; RMSE: 376 g) nor BMC z-scores (R<sup>2</sup>: 0.55; RMSE: 1.09 SD) produced from the existing propriety prediction equation demonstrated equivalence with DXA in the combined sample. Moreover, the existing prediction equation had a 69.6 % accuracy of identifying <em>low BMC</em>. LASSO regression for the newly developed smartphone prediction model produced the following equation:</div><div>BMC (g) = -2020.769 + 60.902(<em>Black=1, 0=all other races</em>) – 180.364(<em>Asian=1, 0=all other races</em>) + 24.433(<em>height</em>) + 1.702(<em>weight</em>) + 2.92(<em>shoulder circumference</em>) + 0.258(<em>arm surface area</em>) – 715.29(<em>waist circumference/(BMI<sup>2/3</sup> x height<sup>1/2</sup>)</em>).</div><div>BMC (R<sup>2</sup>: 0.91; RMSE: 209 g) and BMC z-scores (R<sup>2</sup>: 0.85; RMSE: 0.61) produced from the newly developed equation in the testing sample demonstrated equivalence with DXA and had a 92.5 % accuracy of identifying <em>low BMC</em>.</div><div><em>Conclusions:</em> Smartphone anthropometrics provide accurate and clinically relevant BMC measurements outside of an advanced setting through the use of our newly-developed smartphone prediction model.</div></div>","PeriodicalId":50240,"journal":{"name":"Journal of Clinical Densitometry","volume":"28 1","pages":"Article 101537"},"PeriodicalIF":1.7000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting bone mineral content from smartphone digital anthropometrics: evaluation of an existing application and the development of new prediction models\",\"authors\":\"Austin J. Graybeal , Sydney H. Swafford , Abby T. Compton , Megan E. Renna , Tanner Thorsen , Jon Stavres\",\"doi\":\"10.1016/j.jocd.2024.101537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div><em>Introduction/Background:</em> Bone mineral content (BMC) is most commonly evaluated using dual-energy X-ray absorptiometry (DXA), but there are several challenges that limit use of DXA during routine care. Breakthroughs in digital imaging now allow smartphone applications to automate important anthropometrics that can predict several body composition components. However, it is unknown whether the anthropometrics automated using smartphone applications can predict DXA-derived BMC.</div><div><em>Methodology:</em> A total of 214 participants (129 F, 85 M) had BMC measurements collected from an existing proprietary prediction equation, embedded within a smartphone application (MeThreeSixty), and evaluated against DXA. LASSO regression was then used to develop a new BMC prediction equation using the anthropometric estimates produced by the smartphone application in a portion of the participants (n = 174), which was subsequently evaluated against DXA in the remaining sample (n = 40). BMC z-scores were calculated and used to identify the prevalence of <em>low BMC</em> for the existing and newly developed smartphone prediction equations and evaluated against DXA-derived z-scores.</div><div><em>Results:</em> Neither BMC estimates (R<sup>2</sup>: 0.72; RMSE: 376 g) nor BMC z-scores (R<sup>2</sup>: 0.55; RMSE: 1.09 SD) produced from the existing propriety prediction equation demonstrated equivalence with DXA in the combined sample. Moreover, the existing prediction equation had a 69.6 % accuracy of identifying <em>low BMC</em>. LASSO regression for the newly developed smartphone prediction model produced the following equation:</div><div>BMC (g) = -2020.769 + 60.902(<em>Black=1, 0=all other races</em>) – 180.364(<em>Asian=1, 0=all other races</em>) + 24.433(<em>height</em>) + 1.702(<em>weight</em>) + 2.92(<em>shoulder circumference</em>) + 0.258(<em>arm surface area</em>) – 715.29(<em>waist circumference/(BMI<sup>2/3</sup> x height<sup>1/2</sup>)</em>).</div><div>BMC (R<sup>2</sup>: 0.91; RMSE: 209 g) and BMC z-scores (R<sup>2</sup>: 0.85; RMSE: 0.61) produced from the newly developed equation in the testing sample demonstrated equivalence with DXA and had a 92.5 % accuracy of identifying <em>low BMC</em>.</div><div><em>Conclusions:</em> Smartphone anthropometrics provide accurate and clinically relevant BMC measurements outside of an advanced setting through the use of our newly-developed smartphone prediction model.</div></div>\",\"PeriodicalId\":50240,\"journal\":{\"name\":\"Journal of Clinical Densitometry\",\"volume\":\"28 1\",\"pages\":\"Article 101537\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Densitometry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1094695024000702\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Densitometry","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1094695024000702","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Predicting bone mineral content from smartphone digital anthropometrics: evaluation of an existing application and the development of new prediction models
Introduction/Background: Bone mineral content (BMC) is most commonly evaluated using dual-energy X-ray absorptiometry (DXA), but there are several challenges that limit use of DXA during routine care. Breakthroughs in digital imaging now allow smartphone applications to automate important anthropometrics that can predict several body composition components. However, it is unknown whether the anthropometrics automated using smartphone applications can predict DXA-derived BMC.
Methodology: A total of 214 participants (129 F, 85 M) had BMC measurements collected from an existing proprietary prediction equation, embedded within a smartphone application (MeThreeSixty), and evaluated against DXA. LASSO regression was then used to develop a new BMC prediction equation using the anthropometric estimates produced by the smartphone application in a portion of the participants (n = 174), which was subsequently evaluated against DXA in the remaining sample (n = 40). BMC z-scores were calculated and used to identify the prevalence of low BMC for the existing and newly developed smartphone prediction equations and evaluated against DXA-derived z-scores.
Results: Neither BMC estimates (R2: 0.72; RMSE: 376 g) nor BMC z-scores (R2: 0.55; RMSE: 1.09 SD) produced from the existing propriety prediction equation demonstrated equivalence with DXA in the combined sample. Moreover, the existing prediction equation had a 69.6 % accuracy of identifying low BMC. LASSO regression for the newly developed smartphone prediction model produced the following equation:
BMC (g) = -2020.769 + 60.902(Black=1, 0=all other races) – 180.364(Asian=1, 0=all other races) + 24.433(height) + 1.702(weight) + 2.92(shoulder circumference) + 0.258(arm surface area) – 715.29(waist circumference/(BMI2/3 x height1/2)).
BMC (R2: 0.91; RMSE: 209 g) and BMC z-scores (R2: 0.85; RMSE: 0.61) produced from the newly developed equation in the testing sample demonstrated equivalence with DXA and had a 92.5 % accuracy of identifying low BMC.
Conclusions: Smartphone anthropometrics provide accurate and clinically relevant BMC measurements outside of an advanced setting through the use of our newly-developed smartphone prediction model.
期刊介绍：
The Journal is committed to serving ISCD''s mission - the education of heterogenous physician specialties and technologists who are involved in the clinical assessment of skeletal health. The focus of JCD is bone mass measurement, including epidemiology of bone mass, how drugs and diseases alter bone mass, new techniques and quality assurance in bone mass imaging technologies, and bone mass health/economics.
Combining high quality research and review articles with sound, practice-oriented advice, JCD meets the diverse diagnostic and management needs of radiologists, endocrinologists, nephrologists, rheumatologists, gynecologists, family physicians, internists, and technologists whose patients require diagnostic clinical densitometry for therapeutic management.
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