OBJECTIVE:Monitoring body composition is essential for assessing nutritional status and detecting early muscle loss, yet most methods require clinical settings. This study evaluated a self-assessment model that uses consumer technologies for personalized and data-driven body composition monitoring. METHODS:We developed a consumer-accessible five-compartment model that integrates body volume from smartphone-based three-dimensional optical imaging with total body water from smartwatch-based bioelectrical impedance analysis. Body volume and total body water were calibrated against air displacement plethysmography and a clinical-grade bioimpedance system, then combined within a five-compartment framework to estimate fat-free mass and fat mass. Estimates from the accessible model were compared with corresponding five-compartment estimates derived from reference laboratory methods using linear regression and root mean square error. RESULTS:In 30 adults, smartphone body volume (r 2 = 0.97, RMSE = 2.65 L) and smartwatch total body water (r 2 = 0.98, RMSE = 1.54 L) showed strong agreement with laboratory measures but required offset corrections to remove biases. After calibration, fat-free mass and fat mass estimates closely matched the laboratory-derived five-compartment model (r 2 > 0.96, RMSE = 2.10 kg). CONCLUSIONS:Properly calibrated smartphones and smartwatches can yield multicompartment body composition estimates that closely match laboratory standards and support precise, low-cost monitoring in remote and resource-limited settings.

BACKGROUND & AIMS:Body composition assessment by bioelectrical impedance analysis (BIA) and three-dimensional optical (3DO) imaging is increasingly accessible due to low cost and ease of use, supporting integration into clinical practice and self-monitoring. Both methods estimate body composition based on body shape, yet the impact of acute changes in body shape on the estimation of body composition and, for 3DO, waist circumference (WC), has not been systematically evaluated. METHODS:Young adults completed three consecutive measurements on BIA and 3DO systems, in randomized order, under three abdominal positions: relaxed (normal), distended, and contracted. Mean fat mass percent (FM%) and 3DO-derived WC were compared across positions using repeated-measures analysis of variance with Tukey post hoc tests. RESULTS:Twenty participants (age 21.3 ± 3.0 y; BMI 24.3 ± 4.3 kg/m2) completed all measures. BIA-estimated FM% did not differ across positions (23.3-23.5%; all p > 0.05). In contrast, 3DO-estimated FM% differed significantly across positions: distended 28.1 ± 7.0%, relaxed 26.7 ± 6.8%, contracted 25.8 ± 7.4% (all p < 0.05), with differences across the three positions as large as 8.2%. WC also differed by position: distended 79.6 ± 8.4 cm, relaxed 77.9 ± 8.1 cm, contracted 76.3 ± 7.7 cm (all p < 0.05). CONCLUSION:Although body shape is fundamental to body composition estimation by BIA and 3DO, intentional changes in abdominal shape altered 3DO-derived FM% and WC but did not affect BIA-derived FM% in young adults. Standardized instructions that minimize abdominal distension or contraction are needed when using 3DO in clinics and at home to ensure accurate estimates and reliable longitudinal tracking.

Background: Native Hawaiian (NH) women experience high breast cancer incidence and mortality. Although obesity is a known risk factor, the role of ethnic mixture, defined as self-reported combinations of racial or ethnic backgrounds, remains understudied. This population-based study examined whether breast cancer risk varies by ethnic mixture among NH women and whether these associations differ across body mass index (BMI) categories.Methods: We analyzed data from 7,700 NH women in the Multiethnic Cohort Study. Participants were categorized into ethnic mixture groups as NH only, NH/White, NH/Chinese, NH/White/Chinese, NH/other Asian, and NH/other. Age-adjusted incidence rates were calculated per 1,000 person-years. Cox proportional hazards models estimated hazard ratios (HR) and 95% confidence intervals (CI), adjusting for age, BMI, and other factors.Results: Incidence rates were highest among NH only and NH/White/Chinese (5.7 and 5.9 per 1,000 person-years) and lowest among NH/other (3.6). However, ethnic mixture was not significantly associated with risk in adjusted models. Stratified analyses showed modest, nonsignificant risk elevations among overweight NH/Chinese (HR = 1.33; 95% CI, 0.85-2.10). Among obese women, risk was consistent across all groups (HRs: 0.78-0.96), highlighting the dominant role of BMI. Overall, the association between ethnic mixture and breast cancer risk did not differ by BMI category.Conclusions: Ethnic mixture was not independently associated with breast cancer risk; BMI remained a consistent predictor. These findings underscore the need for research integrating ancestry, body composition, and social context to address cancer disparities.Impact: Obesity, not ethnic mixture, is the dominant factor influencing breast cancer risk in NH women.

Background:AI algorithms for skin cancer detection have shown performance comparable to clinicians in controlled settings, yet their real-world reliability, performance across diverse populations, and readiness for clinical deployment remain uncertain. This umbrella review synthesizes evidence across the screening pathway to characterize AI performance, identify equity gaps, and assess implementation readiness. Methods:We searched PubMed, Web of Science, and CINAHL (November 6, 2024) for systematic reviews and meta-analyses evaluating AI for skin cancer detection, excluding narrative reviews, scoping reviews, and reviews not reporting diagnostic accuracy. Two investigators (LS, AB) independently screened studies and assessed quality using ROBIS; one (LS) extracted data with verification by a second (AB). Findings were synthesized narratively by screening phase. This study is registered with PROSPERO (CRD42024605934). Results:Of 411 records identified, 37 (2008-2024) met inclusion criteria; 10 (27.0%) were judged low risk of bias, 22 (59.5%) high, and five (13.5%) unclear. Self-screening applications demonstrated marked performance variability (sensitivity 0-98%), with reduced sensitivity for melanoma detection reported across reviews. Primary care AI achieved moderate accuracy (sensitivity 60-84%, specificity 88-93%). Specialist dermoscopy-based AI achieved sensitivities comparable to dermatologists (82-91%), and histopathology AI achieved 90% sensitivity. AI augmentation increased clinician sensitivity by 6-8 percentage points, with greater benefit for generalists (+28%) than specialists (+2%). Engagement with skin tone and ethnicity increased but remained largely superficial, and >70% of datasets were from light-skinned populations. Evidence disproportionately targeted melanoma (>40% of reviews) despite it comprising <2% of skin cancers; no reviews employed implementation science frameworks. Conclusions:Current evidence does not support unsupervised clinical deployment of AI-based skin cancer detection. Self-screening tools demonstrate inconsistent performance, equity gaps persist, and common non-melanoma skin cancers remain understudied. These findings support the need for stage-specific validation standards and performance reporting.

Hip structural analysis (HSA) of proximal femur scans by dual energy X-ray absorptiometry estimates hip geometry and structural strength, but little is known about how these parameters change during peak bone mass development. We describe age-related changes, precision and long-term stability, and sex and race/ethnic identity differences in hip geometry measures by HSA, and test whether HSA measures predict childhood fractures in healthy children. We used data from the Bone Mineral Density in Childhood Study, a multi-center, longitudinal cohort study of 2,014 healthy U.S. children, ages 5-23y, with up to 7 annual visits. HSA measures included cortical thickness, cross-sectional area, cross-sectional moment of inertia, section modulus, buckling ratio, and bone width at the narrow neck and femoral shaft, and hip axis length. Additional measures included self-identified race and ethnicity, Z-scores for height, BMIZ, appendicular lean soft tissue mass index and fat mass index, and self-reported physical activity, calcium intake, Tanner stage and fractures. Results indicated age-related trends and sex differences in hip geometry. Reference ranges were generated and HSA Z-scores were adjusted for height-for-age Z-score. Femoral shaft measures showed better precision (CV: 1.1 to 3.7%) than narrow neck measures (CV: 2.2 to 7.4%); buckling ratio (both sites) was the least precise. HSA Z-scores tracked well over one year (0.77 to 0.94). Narrow neck buckling ratio (HR 1.16 [95% CI: 1.02, 1.31]) and hip axis length Z-scores (HR 1.20 [95% CI: 1.00, 1.44]) associated with fracture risk. When adjusted for covariates (BMD Z-score, BMIZ, Tanner stage, sex), narrow neck cross-sectional area, cross-sectional moment of inertia, section modulus and bone width, and femoral shaft cross-sectional area and section modulus Z-scores positively associated with fractures (e.g., section modulus Z-score associated with 44% increased fracture risk). These findings provide the foundation for evaluating hip geometry as an indicator of bone strength in children.

BACKGROUND:Advances in health technology have enabled body composition assessments using smartphone photos, offering an accessible, cost-efficient, and portable alternative that can also be used by non-experts. However, it is essential to provide clarity on their technical development and estimation process for clinicians, researchers, and users. AIM:Here, we aimed to provide a technical description and guidance on the use and interpretation of a selected artificial intelligence (AI)-based app for body composition estimation. METHODS:We selected one app as a representative for in-depth technical analysis, based on a non-systematic review of scientific databases, developer websites, search engines, and digital marketplaces, to generate insights relevant to similar tools. RESULTS:MeThreeSixty® app was selected due to its availability and validation for several body composition measures (body fat, fat mass, fat-free mass, and appendicular lean mass). The app integrates advanced technologies, such as three-dimensional (3D) imaging and AI, which improves its accuracy with potential for refinement. It also features a self-assessment function to enhance user accessibility. Early findings indicate the app provides reliable group-level results for body circumference and composition estimations, with refinements needed for individual assessments. CONCLUSION:MeThreeSixty app used 3D imaging and AI with acceptable group-level accuracy for estimating body circumference and composition, but limited precision at the individual level requires cautious interpretation. Further prospective validation and model refinement are needed, especially in diverse populations, and using longitudinal datasets before supporting personalized nutrition and broader health platform integration.

BackgroundThis study aimed to investigate in knee osteoarthritis patients the feasibility of a digital anthropometric approach for body size and composition assessment in combination with assessments of physical and pain characteristics.MethodsA convenience sample of 56 patients (34 females) was recruited. Clinical and radiographic evaluation, digital pain drawing and anthropometric assessments, and physical performance tests were performed.ResultsPain had an anterior distribution in all patients and several patients showed also a posterior and bilateral distribution. Median values of body fat percentage, fat mass index, and appendicular lean mass index were 28.3%, 7.8 kg/m2, and 8.4 kg/m2 in 19 males and 40.0%, 12.5 kg/m2, 6.8 kg/m2 in 28 females. Most of the patients had fat mass index higher than the cut-points for excess fat, while 2 male patients and none of the female patients had appendicular lean mass index lower than the cut-point for low mass. A relevant impairment of physical performance was observed in all patients.ConclusionInnovative digital tools can be used to quantify the changes in body size and composition and the pain location and extension in patients with late-stage knee osteoarthritis.

The assessment of body composition has long been a fundamental component of research and is gaining increasing adoption in clinical practice. This growing interest has drawn new professionals to the field and increased emphasis on its clinical relevance and applications. However, the diversity of assessment techniques and inconsistent terminology create challenges, highlighting the urgent need for harmonized approaches across research and healthcare settings. Commonly employed methods include bioelectrical impedance approaches, dual-energy X-ray absorptiometry, and computerized tomography, with ultrasound emerging as an increasingly prominent tool. These methods are featured in guidelines for diagnosing conditions such as low muscle mass, malnutrition, sarcopenia, and sarcopenic obesity, among others. This second narrative review in a series, developed by an international panel of experts, focuses on these widely accessible assessment tools that align with clinical recommendations. It presents foundational knowledge, discusses validity and reliability considerations, and offers practical advice on terminology, measurement protocols, data interpretation, and longitudinal monitoring. The report also addresses current limitations and identifies areas needing further research. Our goal is to provide clear, evidence-based guidance that is useful for both experienced practitioners and those newly engaging with body composition assessment. We urge organizations, journals, and stakeholders across the body composition field to adopt the proposed principles and standards to support consistency, transparency, and scientific rigor in both research and clinical care.

Table S1 showing HR and 95% CI for breast cancer and BMI

OBJECTIVES:To provide an overview of the functionality, research findings, and future directions on mobile apps for estimating body composition. METHODS:A nonsystematic literature search (April 2023 - February 2026) was conducted across scientific databases, online search engines, and digital marketplaces to identify apps with scientific backing. Data on apps methodologies were extracted from associated papers and websites. Developers were contacted to address technical gaps, and only apps with responses were included. RESULTS:Out of the 18 studies, 11 apps for body composition prediction were identified, with complete technical data available for 5 apps. Studies reflect available approaches used to capture two-dimensional whole-body images, employing digital anthropometry techniques, such as three-dimensional electronic tape measurements and artificial intelligence, to extract body shape and anthropometric data. The apps demonstrated good accuracy in predicting fat mass percentage; however, most showed reduced accuracy in individuals with higher adiposity. Studies also evaluated fat mass, fat-free mass, and appendicular fat-free mass, and the apps generally presented satisfactory predictive performance. However, important limitations remain, including reduced accuracy at the individual-level, insufficient validation across diverse populations, and limited evidence on longitudinal tracking capabilities. CONCLUSION:Mobile apps offer a promising, cost-efficient, and accessible approach for body composition estimation. Nevertheless, further validation and improvements in accuracy are needed to support its clinical application.

BackgroundTo investigate the feasibility and clinical validity of a digital anthropometric approach for body size and shape assessment in post-bariatric patients scheduled for plastic surgery.MethodsA convenience sample of 42 patients was recruited. Clinical evaluation, administration of outcome questionnaires, and digital anthropometric assessment were performed before and 3 months after abdominoplasty (in 26 patients) and thighplasty (in 16 patients).ResultsSignificant pre-post-intervention decreases in waist and hip circumferences were observed in the abdominoplasty subgroup: the median decrease was 3.3 cm (P < 0.0001) for the waist circumference and 2.6 cm (P = 0.002) for the hip circumference. Significant pre-post-intervention decreases in thigh circumferences and leg volumes were observed in the thighplasty subgroup: the median decreases of the left and right thigh circumferences were 1.7 cm (P = 0.001) and 1.5 cm (p = 0.003) and the median decreases of the left and right leg volumes were both 0.4 l (P values: 0.007 and 0.02). Significant pre-post-intervention improvements were also observed for both BODY-Q abdomen satisfaction scale scores and BODY-Q inner thighs satisfaction scale scores.ConclusionSurgical outcomes in patients undergoing abdominoplasty and thighplasty can be documented through clinimetric and digital anthropometric assessments. The availability of pre- and post-intervention avatars can be useful for both surgeons (for surgical planning and documentation of the surgical outcomes) and patients (for visualization of the surgical outcomes).

Background: Delivering comprehensive cancer prevention, diagnosis, and treatment across Hawai'i and the U.S.-Affiliated Pacific Islands (USAPI) is constrained by geographic isolation, oncology workforce shortages, and persistent cancer inequities. Objectives: The University of Hawai'i Cancer Center, the state's only National Cancer Institute-designated cancer center, partners with community healthcare systems to address cancer health disparities. Here, we describe an implementation-focused strategy initiated in December 2024 that is designed to improve equitable access to evidence-based oncology services across the catchment area. Approach: This program description integrates publicly available demographic and health system data and presents a structured implementation framework centered on (1) workforce development and oncology training pathways; (2) a statewide clinical oncology network supported by telehealth; (3) community-engaged screening and early detection outreach; and (4) strengthening clinical research and trial infrastructure with deliberate inclusion of underserved populations. Evaluation: We outline an evaluation framework incorporating process and outcome metrics spanning workforce capacity, screening participation, timeliness of care, clinical trial enrollment, and equity indicators stratified by county, island, and population group. Conclusions: This approach offers a scalable, implementation-oriented model for developing an academic oncology ecosystem that emphasizes measurement, accountability, and equity, with potential applicability to other geographically dispersed and ethnically diverse regions.

Body weight and health-outcome results of highly effective new GLP-1R agonist medicine trials are usually presented in scientific reports in the form of standard graphs and tables. These representations are not easily translated to what the average participant looked like or their health risks at the outset of the study and how improvements in adiposity and clinical measures changed with treatment. Two recently developed methods for visually presenting complex weight and health-risk information that encapsulate substantial amounts of clinical trial observations were recently developed that can potentially supplement and give new insights into conventional GLP-1R agonist scientific reports. The current study aim was to put these visual presentations into a demonstration format to explore if and to what extent they convey new or useful information beyond traditional graphical and tabular approaches. The first developed approach was the capability of generating, with manifold regression, humanoid avatars with accurate anthropometric dimensions. The second developed approach, body roundness index (BRI), associates a person’s shape phenotype with high-risk adiposity components and multiple health outcomes; BRI can be displayed in a visual format. These two approaches were applied to produce visual representations of body size and shape in Surmount 1 average male and female participants (maximal-tolerated dose group) at baseline and after 72-weeks of tirzepatide treatment. Developed images revealed clear excess adiposity and high health-risk (BRI) at baseline with marked improvements, although not to within the healthy BMI (<25 kg/m2) and BRI ranges at 72 weeks, observations not evident in the published report. Avatar analyses revealed sexual dimorphism in regional shape (volume) changes with weight loss. Visual presentation of new weight loss medicine trial results can supplement standard published reports by condensing substantial amounts of complex technical information in an easily understood format that can potentially yield new study insights.

Background:Breast density, as derived from mammographic images and defined by the Breast Imaging Reporting & Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound is an alternative breast cancer screening modality, particularly useful in low-resource, rural contexts. To date, breast ultrasound has not been used to inform risk models that need breast density. The purpose of this study is to explore the use of artificial intelligence (AI) to predict BI-RADS breast density category from clinical breast ultrasound imaging. Methods:We compared deep learning methods for predicting breast density directly from breast ultrasound imaging, as well as machine learning models from breast ultrasound image gray-level histograms alone. The use of AI-derived breast ultrasound breast density as a breast cancer risk factor was compared to clinical BI-RADS breast density. Retrospective (2009-2022) breast ultrasound data were split by individual into 70/20/10% groups for training, validation, and held-out testing for reporting results. Findings:405,120 clinical breast ultrasound images from 14,066 women (mean age 53 years, range 18-99 years) with clinical breast ultrasound exams were retrospectively selected for inclusion from three institutions: 10,393 training (302,574 images), 2593 validation (69,842), and 1074 testing (28,616). The AI model achieves AUROC 0.854 in breast density classification and statistically significantly outperforms all image statistic-based methods. In an existing clinical 5-year breast cancer risk model, breast ultrasound AI and clinical breast density predict 5-year breast cancer risk with 0.606 and 0.599 AUROC (DeLong's test p-value: 0.67), respectively. Interpretation:BI-RADS breast density can be estimated from breast ultrasound imaging with high accuracy. The AI model provided superior estimates to other machine learning approaches. Furthermore, we demonstrate that age-adjusted, AI-derived breast ultrasound breast density provides similar predictive power to mammographic breast density in our population. Estimated breast density from ultrasound may be useful in performing breast cancer risk assessment in areas where mammography may not be available. Funding:National Cancer Institute.

Breast cancer screening programs using mammography have led to significant mortality reduction in high-income countries. However, many low- and middle-income countries lack resources for mammographic screening. Handheld breast ultrasound (BUS) is a low-cost alternative but requires substantial training. Artificial intelligence (AI) enabled BUS may aid in both the detection and classification of breast cancer, enabling screening use in low-resource contexts. The purpose of this systematic review is to investigate whether AI-enhanced BUS is sufficiently accurate to serve as the primary modality in screening, particularly in resource-limited environments. This review (CRD42023493053) is reported in accordance with the PRISMA guidelines. Evidence synthesis is reported in accordance with the SWiM (Synthesis Without Meta-analysis) guidelines. PubMed and Google Scholar were searched from January 1, 2016 to December 12, 2023. Studies are grouped according to AI task and assessed for quality. Of 763 candidate studies, 314 full texts were reviewed and 34 studies are included. The AI tasks of included studies are as follows: 1 frame selection, 6 lesion detection, 11 segmentation, and 16 classification. 79% of studies were at high or unclear risk of bias. Exemplary classification and segmentation AI systems perform with 0.976 AUROC and 0.838 Dice similarity coefficient. There has been encouraging development of AI for BUS. However, despite studies demonstrating high performance, substantial further research is required to validate reported performance in real-world screening programs. High-quality model validation on geographically external, screening datasets will be key to realizing the potential for AI-enhanced BUS in increasing screening access in resource-limited environments.

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European ancestry and associates with reduced odds of fracture in adults. We aimed to determine if gSOS was associated with bone health in children. Two observational studies of children were evaluated: (1) children enrolled in the Bone Mineral Density in Childhood Study (BMDCS) with genetic data (N = 1727) and (2) children with genetic data for research at the Children's Hospital of Philadelphia (CHOP; N = 10 301). Genetic variants were used to calculate gSOS and genetic ancestry. For the BMDCS, puberty stage, dietary calcium, physical activity, and fracture accumulation (none or ≥1 fracture) were self-reported, height and weight were measured and BMI calculated. Areal BMD (aBMD) of the lumbar spine, hip, radius, and whole body were assessed by DXA and expressed as Z-scores. The CHOP study paired genetic data with documentation of fracture in the electronic health record (EHR). Genetic quantitative ultrasound speed of sound associated with higher aBMD Z-scores across 7 skeletal sites [eg, a 1 SD increase in gSOS associated with 0.17 (95% CI: 0.10-0.24) higher LS aBMD Z-score]. These associations were consistent for males and females, age, puberty stage, and lifestyle factors, and most consistent among children of European genetic ancestry. A 1 SD increase in gSOS associated with 24% reduced likelihood of self-reported fracture in the BMDCS (OR = 0.76, 95% CI: 0.66, 0.88) and a 12% reduced likelihood of a recorded fracture in the CHOP EHR (OR = 0.88; 95% CI: 0.82, 0.95). No sex or genetic ancestry differences were found. A higher gSOS score associated with higher aBMD at multiple skeletal sites and reduced odds of fracture in two independent pediatric samples. This genetic tool may have clinical utility to help enhance bone health in early life and protect against fracture across the lifespan.

Background: Breast cancer remains a significant cause of mortality in women, especially in rural and underserved communities where access to mammography is limited or nonexistent. The incidence of advanced-stage breast cancer is 66% higher in Hawaii than in the mainland US, and 5 – 9 times higher in the U.S. Affiliated Pacific Islands (USAPI). AI-enhanced point-of-care ultrasound (POCUS) breast imaging may be an effective method for detecting breast cancer while still in the early stages. The hypothesis is that AI detection and classification algorithms will increase the accuracy of POCUS such that it would approach that of mammography. Further, it may reduce the training levels needed to do the early detection POCUS scanning. One such candidate device is portable, wireless, and provides an SDK for inserting AI models before presenting the image to the user. We have been exploring a suitable protocol for using this POCUS device in remote conditions where infrastructure may be limited. Further, we asked if the portable battery-operated scanner could keep up with a scanning rate of 2 patients per hour for an indefinite amount of time. In this study, we seek to identify the performance parameters that need to be considered to use this and other POCUS systems in rural, remote, and underserved communities. Methods: The POCUS system consists of a portable handheld scanner (Clarius L7 HD3 model; Clarius Mobile Health Inc, Vancouver, Canada), a tablet computer (Samsung Galaxy Tab S9 Ultra; Samsung Electronics Co., Ltd, South Korea), and a laptop (Dell XPS 15 running Windows 11; Dell Inc., Texas, US). The scanner communicates with the tablet and laptop via an ad hoc WIFI connection. A breast phantom (Gphantom, EDM Medical Solutions, Florida, US) was scanned continuously for 10 minutes, followed by a 20-minute charging period, and repeated. Pretrained AI models were inserted into the image stream using the Cast API. Scanner temperature and battery levels were monitored to determine their time characteristics utilizing the Clarius app. Longer scan and charging periods were used as well to capture the full extent of heating, cooling, and charging cycles. Probe temperature and charging/discharging characteristics were fit to exponential functions.Results: The system was able to operate for 24 minutes continuously before hitting a thermal protection temperature of 48°C. The probe was able to fully recharge from 0 to 100% within 60 minutes. For the 2-patient-per-hour protocol, the probe was able to regain its starting charge and temperature for back to back phantom scans over 4 hours. Time constants were found to be 16.1 minutes for temperature increase and 49.6 minutes for battery discharge. The user found it difficult to hold the probe for temperatures above 42°C. A shorter time between scans may not be feasible but being tested.Conclusion: The findings show that the device can be used continuously for at least two patients per hour with 10 minutes of continuous scanning followed by 20 minutes of charging. However, the operating temperature of the probe is quite high and it may be difficult to handle. Healthcare workers can optimize usage protocol to maximize functional time without frequent recharging in remote areas. Future work will focus on refining AI models for real-time applications and exploring alternative devices with better thermal performance. Citation Format: Nusrat Zaman Zemi, Dustin Valdez, Arianna Bunnell, John Shepherd. Addressing Thermal and Battery Efficiency in AI Enhanced Portable Ultrasound Screening Protocols for Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-06-16.

Body composition assessment is widely used in both research and clinical practice, yet confusion over basic concepts and terminology persists, leading to inaccurate assessments, comparisons, and interpretations. To address this concern, an international working group was formed to clarify basic concepts, standardize terminology, and provide guidance on the use and interpretation of body composition assessment. This initial publication addresses methodological standards, focusing on summarizing body composition levels and models, and introducing standardized terms and definitions. Body composition is organized into 5 distinct levels, ranging from atomic to whole-body, with each higher level encompassing the components of the preceding less complex levels. As a result, terms that describe components at different levels should not be used interchangeably. For example, the use of the molecular-level term “lean body mass” is discouraged because it inaccurately refers to fat-free mass (FFM), lean mass, or lean soft tissue (LST). FFM includes all compartments at the molecular level except fat (nonpolar lipids; mainly triglycerides), and FFM also contains nonfat (or polar) lipids. The term “lean mass” is equivalent to FFM, but not to LST, as FFM includes bone mineral content. Additionally, skeletal muscle is classified at the tissue-organ level and should not be confused with the molecular-level components FFM and LST. Likewise, fat mass and adipose tissue are different components: fat mass, mainly triglycerides, is assessed at the molecular level, whereas adipose tissue is measured at the tissue-organ level. Models are also specific to each level. It is crucial for researchers and clinicians to have a clear understanding of what each body component entails and to use accurate terminology to ensure precise assessment, reporting, and interpretation of body composition data.

BackgroundThe normalization of echocardiographic variables for body surface area (BSA) enables to obtain relative indexes of ventricular size that are useful for diagnosis and monitoring of non-ischaemic cardiomyopathies. The BSA values commonly considered in the clinical practice are obtained using predictive equations. Our aims were to investigate the accuracy of different predictive equations for BSA estimation and to evaluate the impact of different BSA normalizations on ventricular dilatation prevalence in youth soccer players.MethodsTwo samples of 369 and 111 youth soccer players of both genders were recruited. Acquisition of optical images (for the players of the first sample), two-dimensional echocardiographic assessment (for the players of the second sample), and weight and height measurements (for the players of both samples) were performed. BSA estimates were derived from optical images and from ten different predictive equations obtained from the literature.ResultsIn the first sample of 369 players, we found differences among the BSA estimates obtained with ten predictive equations in both male and female players and we also found that all predictive equations in male players and almost all predictive equations in female players overestimated BSA compared to the optical imaging-derived BSA. In the second sample of 111 soccer players, we found that the normalization of each echocardiographic variable for different BSA values resulted in significantly different relative values and that ventricular dilatation prevalence was a function of BSA normalization.ConclusionNewly developed equations seemed the most accurate for BSA estimation in both male and female players: therefore, we suggest to adopt these equations for BSA estimation in youth soccer players. The BSA normalization impacts on the ventricular dilatation prevalence: therefore, we suggest to adopt the proper normalization approach to improve the clinical validity of echocardiography in athletes.