Shape Model Research Articles

Resilience or Collapse? Reconstructing the Water Quality Time Series of a Tropical River Impacted by a Mine Tailings Dam Failure

The 2015 Fundão tailings dam collapse in Mariana, Brazil, was a major environmental catastrophe. Assessing its long-term effects on water quality is critical for environmental restoration and policy development. In this study, we reconstructed a 15-year time series of five water quality parameters to assess whether the collapse caused permanent changes. Using public data from the Minas Gerais Water Institute (IGAM), we fitted generalized additive models for location, scale, and shape to model long-term trends in turbidity, total solids, conductivity, pH, and dissolved oxygen. Predictor variables included daily precipitation and smooth functions for time and longitudinal distance along the river. As expected, turbidity and total solids increased sharply after the collapse; however, the mean values returned to pre-collapse levels within four years. Conductivity, which was already elevated pre-collapse, remained high following the passage of the tailings plume. Although we observed a tendency toward pre-collapse values, the long-term conductivity mean did not fully stabilize to previous levels. No clear patterns were observed for pH or dissolved oxygen. This study highlights the acute impact of the dam collapse on five water quality parameters in the Doce River and illustrates the river’s subsequent stabilization process, although other important and chronic impacts are still persistent. Long-term studies such as this provide valuable insights into the dynamics of fluvial systems.

3D statistical shape models for automatic segmentation of the fetal cerebellum in ultrasound images

3D statistical shape models for automatic segmentation of the fetal cerebellum in ultrasound images

Statistical Analysis of Complex Shape Graphs.

This paper provides developments in statistical shape analysis of shape graphs, and demonstrates them using such complex objects as Retinal Blood Vessel (RBV) networks and neurons. The shape graphs are represented by sets of nodes and edges (articulated curves) connecting some nodes. The goals are to utilize nodes (locations, connectivity) and edges (edge weights and shapes) to: (1) characterize shapes, (2) quantify shape differences, and (3) model statistical variability. We develop a mathematical representation, elastic Riemannian metrics, and associated tools for shape graphs. Specifically, we derive tools for shape graph registration, geodesics, statistical summaries, shape modeling, and shape synthesis. Geodesics are convenient for visualizing optimal deformations, and PCA helps in dimension reduction and statistical modeling. One key challenge lies in comparing shape graphs with vastly different complexities (in number of nodes and edges). This paper introduces a novel multi-scale representation to handle this challenge. Using the notions of (1) "effective resistance" to cluster nodes and (2) elastic shape averaging of edge curves, it reduces graph complexity while retaining overall structures. This allows shape comparisons by bringing graphs to similar complexities. We demonstrate these ideas on 2D RBV networks from the STARE and DRIVE databases and 3D neurons from the NeuroMorpho database.

Wide Variation in Shape of Hypoplastic Left Ventricles Undergoing Recruitment and Biventricular Repair: A Statistical Shape Modeling Study.

Patients with hypoplastic left ventricles (LV) who undergo volume-loading procedures (recruitment, biventricular (BIV) repair) are at risk for adverse outcomes including heart failure and death. We investigated pre-BIV LV shape as a predictor of outcome after BIV repair in patients with hypoplastic LVs. Baseline and post-recruitment cardiac MRI and CT data were analyzed in patients with hypoplastic LV (< 50 ml/m2). Statistical shape modeling (SSM) was utilized to generate a model of the shape and variability of LVs. Traditional measures of LV sphericity and eccentricity were also measured. Major adverse cardiac events (MACE) included heart failure, transplant, and death. Of 95 patients with baseline mean LV volume 29 ± 13ml/m2, 45 (47%) had a right dominant atrioventricular canal defect, 31 (34%) had a variant of hypoplastic left heart syndrome, and 18 (19%) had endocardial fibroelastosis (EFE). A wide variation in LV shape was found by SSM, and shape modes were associated with RV and LV size, and diagnosis. BIV repair was achieved in 74 (78%) patients; and 13 (18%) of BIV patients had MACE. Predictors of MACE following BIV repair included EFE, higher RV mass index, and higher RV end-diastolic volume index. No baseline or post-recruitment LV shape parameter was associated with outcome after BIV repair. The shape model of hypoplastic LVs demonstrated a wide array of LV shapes. LVs gained sphericity and size and lost eccentricity with recruitment. Though the ventricles changed shape with recruitment, no specific LV shape characteristic at the baseline or post-recruitment stage was predictive of decision to proceed with BIV repair nor outcome. Higher RV mass and volume may represent new biomarkers that predict outcome following BIV repair in patients with hypoplastic LV. Further investigation could determine the reproducibility of these findings.

Hydrological drought assessment of the Yellow River Basin based on non-stationary model

Study Regionthe Yellow River Basin Study FocusNon-stationary hydrological conditions are becoming increasingly common due to climate change and human activities, and pose a novel challenge to the management of water resources and related risks, especially for large basins. However, prevalent research on drought assessment often ignores the non-stationary characteristics of hydrological processes. In this study, we investigated the stationarity of the runoff of the Yellow River Basin (YRB), the second-longest basin in China. We used an approach for assessing non-stationary droughts based on the generalized additive model for location, scale, and shape to establish a standardized runoff index containing covariates (SRI_cov) to identify hydrological droughts in the basin from 1986 to 2015. New Hydrological Insights for the RegionThe results show that the runoff was non-stationary in the YRB. Based on SRI_cov, hydrological drought predominantly occurred in the entire YRB in spring. From 1986, the number of months in which droughts occurred in the YRB exhibited a general trend of increase and peaked around 2002. After that, the total number of droughts significantly decreased but extreme droughts had become more prominent since 2005. The drought was more severe in the middle reaches of the Yellow River, and was characterized by a high frequency, intensity, and severity. Our analysis enhances the understanding of hydrological modeling and drought assessment under non-stationary conditions.

International norms for adult handgrip strength: A systematic review of data on 2.4 million adults aged 20 to 100+ years from 69 countries and regions.

Muscular strength is a powerful marker of current health status and robust predictor of age-related disease and disability. Handgrip strength (HGS) using isometric dynamometry is a convenient, feasible, and widely used method of assessing muscular strength among people of all ages. While adult HGS norms have been published for many countries, no study has yet synthesized available data to produce international norms. The objective of this study was to generate international sex- and age-specific norms for absolute and body size-normalized HGS across the adult lifespan. Systematic searches were conducted in 6 databases/web search engines (MEDLINE, SPORTDiscus, Embase, Web of Science, CINAHL, and Google Scholar) up to December 1, 2023. We included full-text peer-reviewed observational studies that reported normative HGS data for adults aged ≥20 years by sex and age. Pseudo data were generated using Monte Carlo simulation following harmonization for methodological variation. Population-weighted Generalized Additive Models for Location, Scale, and Shape were used to develop sex- and age-specific norms for absolute HGS (kg) and HGS normalized by height (Ht, m) squared (i.e., HGS/Ht2 in kg/m2). Norms were tabulated as percentile values (5th to 95th) and visualized as smoothed percentile curves. We included data from 100 unique observational studies representing 2,405,863 adults (51.9% female) aged 20 to 100+ years from 69 countries and regions tested from the year 2000 onward. On average, absolute and normalized HGS values negligibly improved throughout early adulthood, peaked from age 30-39 years (at 47.8 kg (males) and 29.7 kg (females) for absolute HGS or 16.3 kg/m2 (males) and 11.3 kg/m2 (females) for HGS/Ht2), and declined afterwards. The age-related decline in HGS accelerated from middle to late adulthood and was slightly larger for males than for females during middle adulthood. This study provides the world's largest and most geographically comprehensive international norms for adult HGS by sex and age. These norms have utility for global peer-comparisons, health screening, and surveillance.

Geometric preprocessing for measurements of comet 67P acquired by Rosetta's Visible and InfraRed Thermal Imaging Spectrometer, Mapping channel (VIRTIS-M).

Comet 67P/Churyumov-Gerasimenko (hereafter 67P) was the primary target of ESA's Rosetta mission. Hyperspectral images acquired by the Mapping channel of the Visible and InfraRed Thermal Imaging Spectrometer aboard Rosetta can be used to derive physical and compositional surface properties by detailed spectrophotometric analyses. This calls for a precise spatial co-registration between measurements and geometry information. In this work, we improve the wavelength-dependent co-registration and also the spatial consistency of the radiometric calibration. This is accomplished by applying a feature-based image matching method comparing measured 67P nucleus images from the entire mission to corresponding photometric simulations. The derived geometric distortions suggest previously unaccounted optical aberrations of the instrument, in conjunction with non-systematic spacecraft pointing and perspective errors, and discrepancies between the true nucleus shape at data acquisition time and the used digital shape model.

Scapular morphology is associated with certain patterns of glenohumeral osteoarthritis, but not with full-thickness rotator cuff tears

BackgroundThe role of anatomic factors in rotator cuff (RC) pathology or in the development of shoulder osteoarthritis (OA) is not yet well understood or recognized. While two-dimensional imaging and measurements are easy to capture and interpret, three-dimensional (3D) methods provide a more thorough description and analysis of the scapula’s complex shape. The purpose of this study is to investigate whether scapular anatomy is associated with RC tears or glenohumeral OA using 3D statistical shape modelling (SSM). MethodsA total of 126 computed tomography (CT)-scans of complete scapulae were captured from three cohorts: RC pathology, OA, and control (C). The RC cohort included patients with posterosuperior RC tears, the OA cohort included patients with glenohumeral OA, and the C cohort included patients without shoulder pathology, imaged for other purposes. The RC and OA cohorts each included 55 patients matched by age and gender, while the C cohort included 16 patients, without a specific matching. A full segmentation of each scapula was manually created and 3D surfaces were reconstructed for input to a SSM analysis. SSM-based correspondence particles were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). Various landmarks were manually identified on each scapula. From these landmarks, 3D measurements of glenoid version and inclination, critical shoulder angle (CSA), acromial angle, among others, were obtained. Measurements were compared between the three cohorts using ANOVA or Student’s T-test, as appropriate. ResultsThere were no statistically significant differences in mean age (RC:57±9, OA:58±8 and C:61±7 years) or gender proportion (males:RC:51%, OA:51% and C:75%) between the three cohorts. 13 PCA modes captured significant shape variation (83.3% of overall variation) of the scapula. The LDA was significantly different between OA and both RC and C (RC:1.0±1.4, OA: -1.0±1.9 and C:1.4±1.2). Compared to C, the OA cohort had smaller CSA (24.9±6.1° vs. 32.4±4.3°; p<0.001), larger acromion angle (65.4±9.3° vs. 57.4±9.5°; p=0.019), among other parameters. Compared to RC, the OA cohort also had less distance between coracoid and acromion (47.2±5.8° vs. 44.2±6.4°; p=0.026), as well as similar morphological differences as to the C cohort. There were no significant differences between RC and C cohort. ConclusionsPCA and LDA applied to manually segmented scapulae revealed statistically significant differences in scapular morphology between OA and control scapulae (a more superior and more horizontal acromion with a lower CSA), but only minimal differences between RC and control scapulae (except a more anterior anterolateral corner of the acromion).

Truly Multivariate Structured Additive Distributional Regression

Generalized additive models for location, scale and shape (GAMLSS) are a popular extension to mean regression models where each parameter of an arbitrary parametric distribution is modelled through covariates. While such models have been developed for univariate and bivariate responses, the truly multivariate case remains extremely challenging for both computational and theoretical reasons. Alternative approaches to GAMLSS may allow for higher-dimensional response vectors to be modelled jointly but often assume a fixed dependence structure not depending on covariates or are limited with respect to modelling flexibility or computational aspects. We contribute to this gap in the literature and propose a truly multivariate distributional model, which allows one to benefit from the flexibility of GAMLSS even when the response has dimension larger than two or three. Building on copula regression, we model the dependence structure of the response through a Gaussian copula, while the marginal distributions can vary across components. Our model is highly parameterized but estimation becomes feasible with Bayesian inference employing shrinkage priors. We demonstrate the competitiveness of our approach in a simulation study and illustrate how it complements existing approaches along the examples of childhood malnutrition and a yet unexplored data set on traffic detection in Berlin.

Optimizing the Accuracy and Precision of the Minimal Detectable Change Statistic: Secondary Analysis of Test-Retest Data from the NIH Toolbox Study.

The minimal detectable change (MDC) statistic is often used by clinicians to monitor change in patients. However, the way in which the MDC is traditionally calculated might be suboptimal in terms of accuracy and precision, potentially resulting in erroneous clinical decisions. This study compared the performance of the MDC statistic as traditionally calculated to that of 2 regression-based alternatives. This analysis used test-retest data from adults who participated in usual walking speed (n = 169) or grip strength (n = 178) assessments as part of the NIH Toolbox Study. Three approaches for MDC calculation were compared: the traditional approach (MDCTrad), simple linear regression (MDCSLR), and generalized additive models for location, scale, and shape (MDCGAMLSS). These approaches were compared in terms of accuracy and precision across all levels of measurement and separately for initial test values above and below the median. Each of the 3 approaches accurately modeled detectable change thresholds when performance was averaged across all test values. However, MDCTrad demonstrated inaccuracies when performance was considered separately for initial test values below or above the median. For walking speed, average precision improved by 12% with MDCSLR and 16% with MDCGAMLSS, compared to MDCTrad. For grip strength, average precision improved by 3% with MDCSLR and 21% with MDCGAMLSS, compared to MDCTrad. MDCSLR and MDCGAMLSS appeared to more accurately and precisely model detectable change thresholds, compared to MDCTRAD. In general, MDCGAMLSS demonstrated the best overall performance in this within-sample analysis. Improved precision and accuracy in detectable change thresholds for walking speed or grip strength might facilitate clinicians' ability to promptly detect a decline in function and intervene and to confidently detect improvements in function over time.

Generative 3D Cardiac Shape Modelling for in-silico Trials.

We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by a family of trainable embedding vectors with encode the geometric features of each shape. The network is trained on a dataset of aortic root meshes reconstructed from CT images by making the neural field vanish on sampled surface points and enforcing its spatial gradient to have unit norm. Empirical results show that our model can represent aortic shapes with high fidelity. Moreover, by sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies, which can be used for in-silico trials.

Statistical Shape Modeling to Determine Poromechanics of the Human Knee Joint.

Subject-specific knee joint models are widely used to predict joint contact mechanics for individuals but may not capture the variance in knee joint geometry across a population. Statistical shape modeling uses the dataset of a cohort to encapsulate population-wide variability. The present study aimed to develop a shape modeling procedure for poromechanical finite element models of knee joint to account for population diversity in the creep response of knees. Shape models of right knee joints were created from MRI of 31 healthy male subjects using principal component analysis. Creep analysis was performed for 13 shape models in total, i.e., the average model, plus six models for both the first and second principal modes. For a given loading, the contact and fluid pressures varied substantially within these mathematically produced models but compared reasonably well to that of three subject-specific models that were constructed from individual knees, representing approximately the smallest, median and largest knees of the 31 right knees. While the joint size variation, generally represented by the first principal component, predominantly influenced the magnitudes of contact and fluid pressures, the joint shape variation characterized by the second principal component further affected the pressure distribution, and load sharing between the lateral and medial compartments. The present study evaluated a workflow for the statistical shape modeling of poromechanical behavior of knee joints with sample results based on a small population. However, the workflow can be readily used for a large population to address the challenge of interpatient variability in joint contact mechanics, particularly in contact and fluid pressures in articular cartilage, and variable creep behaviors of the joint associated with individual anatomical variations.

Review of “Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications” by Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr &amp; Gillian Z. Heller

Review of “Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications” by Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr &amp; Gillian Z. Heller

Multi-Section Magnetic Soft Robot with Multirobot Navigation System for Vasculature Intervention.

Magnetic soft robots have recently become a promising technology that has been applied to minimally invasive cardiovascular surgery. This paper presents the analytical modeling of a novel multi-section magnetic soft robot (MS-MSR) with multi-curvature bending, which is maneuvered by an associated collaborative multirobot navigation system (CMNS) with magnetic actuation and ultrasound guidance targeted for intravascular intervention. The kinematic and dynamic analysis of the MS-MSR's telescopic motion is performed using the optimized Cosserat rod model by considering the effect of an external heterogeneous magnetic field, which is generated by a mobile magnetic actuation manipulator to adapt to complex steering scenarios. Meanwhile, an extracorporeal mobile ultrasound navigation manipulator is exploited to track the magnetic soft robot's distal tip motion to realize a closed-loop control. We also conduct a quadratic programming-based optimization scheme to synchronize the multi-objective task-space motion of CMNS with null-space projection. It allows the formulation of a comprehensive controller with motion priority for multirobot collaboration. Experimental results demonstrate that the proposed magnetic soft robot can be successfully navigated within the multi-bifurcation intravascular environment with a shape modeling error and a tip error of under the actuation of a CMNS through invitro ultrasound-guided vasculature interventional tests.

Look Ma, no markers: holistic performance capture without the hassle

We tackle the problem of highly-accurate, holistic performance capture for the face, body and hands simultaneously. Motion-capture technologies used in film and game production typically focus only on face, body or hand capture independently, involve complex and expensive hardware and a high degree of manual intervention from skilled operators. While machine-learning-based approaches exist to overcome these problems, they usually only support a single camera, often operate on a single part of the body, do not produce precise world-space results, and rarely generalize outside specific contexts. In this work, we introduce the first technique for markerfree, high-quality reconstruction of the complete human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware. Our approach produces stable world-space results from arbitrary camera rigs as well as supporting varied capture environments and clothing. We achieve this through a hybrid approach that leverages machine learning models trained exclusively on synthetic data and powerful parametric models of human shape and motion. We evaluate our method on a number of body, face and hand reconstruction benchmarks and demonstrate state-of-the-art results that generalize on diverse datasets.

Impact of knee geometry on joint contact mechanics after meniscectomy.

Finite element modeling has served as a cornerstone in understanding knee joint mechanics post-meniscectomy, yet the influence of varying knee geometries remains unknown. The present study aimed to fill that gap by employing statistical shape modeling to generate knee models from MRI data of 31 human knees, capturing the population's knee size and shape variations. Finite element simulations were conducted to replicate intact, partial, and total medial meniscectomy conditions during standing. The results revealed a substantial shift in load distribution from the medial to lateral compartment following medial meniscectomy with its magnitude depending on knee geometry. Cartilages experienced variable degrees of pressure changes at different sites, which could also be different for fluid and contact pressures. While changes in joint size led to somewhat predictable alterations in contact pressure, variations in joint shape resulted in unexpected changes in contact and fluid pressures, emphasizing the need for computational simulations. The average knee geometry exhibited the lowest contact and fluid pressures under the given loading and boundary conditions, in contrast to knees with shapes deviating from the average. This study highlights the significance of individual knee shape in the biomechanical outcome of meniscectomy, potentially explaining the variability in clinical outcomes observed post-surgery.

Direct Manipulation of Procedural Implicit Surfaces

Procedural implicit surfaces are a popular representation for shape modeling. They provide a simple framework for complex geometric operations such as Booleans, blending and deformations. However, their editability remains a challenging task: as the definition of the shape is purely implicit, direct manipulation of the shape cannot be performed. Thus, parameters of the model are often exposed through abstract sliders, which have to be nontrivially created by the user and understood by others for each individual model to modify. Further, each of these sliders needs to be set one by one to achieve the desired appearance. To circumvent this laborious process while preserving editability, we propose to directly manipulate the implicit surface in the viewport. We let the user naturally interact with the output shape, leveraging points on a co-parameterization we design specifically for implicit surfaces, to guide the parameter updates and reach the desired appearance faster. We leverage our automatic differentiation of the procedural implicit surface to propagate interactions made by the user in the viewport to the shape parameters themselves. We further design a solver that uses such information to guide an intuitive and smooth user workflow. We demonstrate different editing processes across multiple implicit shapes and parameters that would be tedious by tuning sliders.

Société de Biomécanique young investigator award 2023: Estimation of intersegmental load at L5-S1 during lifting/lowering tasks using force plate free markerless motion capture

Accurate estimation of joint load during a lifting/lowering task could provide a better understanding of the pathogenesis and development of musculoskeletal disorders. In particular, the values of the net force and moment at the L5-S1 joint could be an important criterion to identify the unsafe lifting/lowering tasks. In this study, the joint load at L5-S1 was estimated from the motion kinematics acquired using a multi-view markerless motion capture system without force plate. The 3D human pose estimation was first obtained on each frame using deep learning. The kinematic analysis was then performed to calculate the velocity and acceleration information of each segment. Then, the net force and moment at the L5-S1 joint were calculated using inverse dynamics with a top-down approach. This estimate was compared to a reference with a bottom-up approach. It was computed using a marker-based motion capture system combined with force plates and using personalized body segment inertial parameters derived from a 3D model of the human body shape constructed for each subject using biplanar radiographs. The average differences of the estimates for force and moment among all subjects were 14.0 ± 6.9 N and 9.0 ± 2.3 Nm, respectively. Meanwhile, the mean peak value differences of the estimates were 10.8 ± 8.9 N and 11.9 ± 9.5 Nm, respectively. This study then proposed the most rigorous comparison of mechanical loading on the lumbar spine using computer vision. Further work is needed to perform such an estimation under realistic industrial conditions.

Improved calculation of single-scattering properties of frozen droplets and frozen-droplet aggregates observed in deep convective clouds

Abstract. During multiple field campaigns, small quasi-spherical ice crystals, commonly referred to as frozen droplets (FDs), and their aggregates (frozen-droplet aggregates, FDAs) have been identified as the predominant habits in the upper regions of deep convective clouds (DCCs) and their associated anvils. These findings highlight the significance of FDs and FDAs for understanding the microphysics and radiative properties of DCCs. Despite the prevalence of FDs and FDAs at the tops of DCCs where they directly contribute to cloud radiative effect, the detailed single-scattering properties (e.g., scattering-phase function P11 and asymmetry parameter g) of FDs and FDAs remain highly uncertain. This uncertainty is mainly due to insufficient in situ measurements and the resolution of cloud probes, which hinder the development of idealized shape models for FDs and FDAs. In this study, two shape models, the Gaussian random sphere (GS) and droxtal (DX), are proposed as possible representations for the shapes of FDs and FDAs measured in situ. A total of 120 individual models of GSs and 129 models of DXs were generated by varying their shapes. Furthermore, by attaching these individual models in both a homogeneous and heterogeneous manner, three different types and a total of 404 models of FDAs were created: (1) aggregates of GSs; (2) aggregates of DXs; and (3) combinations of GSs and DXs, which are called habit mixtures (HMs). The P11 and g values of the developed models were calculated using a geometric optics method at a wavelength of 0.80 µm and then compared with those obtained using a polar nephelometer (PN) during the CIRCLE-2 field campaign to assess the models. Both individual-component ice crystals (i.e., either GS or DX) and homogeneous-component aggregates (i.e., either aggregates of GSs or aggregates of DXs) showed substantial differences compared with the PN measurements, whereas the P11 of the HMs was found to most accurately match the P11 measured in situ, reducing the differences to +0.87 %, +0.88 %, and −5.37 % in the forward-, lateral-, and backward-scattering regions, respectively. The g value of the HMs was found to be 0.80, which falls within the range of the PN measurement (0.78 ± 0.04). The root-mean-square error for the HM was minimized to a value of 0.0427. It was shown that the novel HMs developed in this study demonstrated better performance than in previous research where HMs were developed indirectly by weighting the calculated P11 of shape models to interpret in situ measurement. The results of this study suggest potential implications for enhancing the calculation of single-scattering properties of ice crystals in DCCs.

Small But Healthy: An Adaptive Response in Baduy Children

Life history explains natural selection, resulting in phenotypic plasticity that can be studied through the growth pattern (growth rate and body size). The Baduy People, one of the indigenous peoples in Indonesia, exhibit prolonged growth, a slow growth rate, a low peak growth spurt, and small adult size as an adaptation to challenging biocultural conditions. The life history trade-offs of the Baduy People are demonstrated by ontogenetic allometry, which synchronizes between skeletal growth and future reproduction. The study aims to characterize the growth of Baduy children aged 0-5 years with more precise ‘tracking’ of developmental plasticity. We collected anthropometric data (body height-BH, body weight-BW, head circumference-HC, body mass index-BMI) using a cross-sectional method from 118 girls and 97 boys aged 0-5 years across 24 Baduy Luar hamlets. Data were analyzed with Generalized Additive Models for Location, Scale, and Shape (GAMLSS) in R version 4.2.1. Nutritional statuses were categorized based on the BMI z-score. The growth pattern showed a decline in growth rate after birth until 2 or 3 years, but the body size increased yearly. The body size (BH and BW) of Baduy children is lower than the Indonesian National Synthetic Growth Charts (INSGC), but most of them have a good nutritional status (&gt;80%). This finding is consistent with the "small but healthy" hypothesis: a small body is an advantageous evolutionary strategy for energy efficiency and maximizing growth potential in challenging biocultural conditions.