Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

[Evaluation of the accuracy of modern intraocular lens calculation formulas when optical biometry is not possible]. / Otsenka tochnosti sovremennykh formul rascheta intraokulyarnykh linz pri nevozmozhnosti vypolneniya opticheskoi biometrii.

PURPOSE: This study evaluates the accuracy of modern intraocular lens (IOL) calculation formulas using axial length (AL) data obtained by ultrasound biometry (UBM) compared to the third-generation SRK/T calculator. MATERIAL AND METHODS: The study included 230 patients (267 eyes) with severe lens opacities that prevented optical biometry, who underwent phacoemulsification (PE) with IOL implantation. IOL power calculation according to the SRK/T formula was based on AL and anterior chamber depth obtained by UBM (Tomey Biometer Al-100) and keratometry on the Topcon KR 8800 autorefractometer. To adapt AL for new generation calculators - Barrett Universal II (BUII), Hill RBF ver. 3.0 (RBF), Kane and Ladas Super Formula (LSF) - the retinal thickness (0.20 mm) was added to the axial length determined by UBM, and then the optical power of the artificial lens was calculated. The mean error and its modulus value were used as criteria for the accuracy of IOL calculation. RESULTS: A significant difference (p=0.008) in the mean IOL calculation error was found between the formulas. Pairwise analysis revealed differences between SRK/T (-0.32±0.58 D) and other formulas - BUII (-0.16±0.52 D; p=0.014), RBF (-0.17±0.51 D; p=0.024), Kane (-0.17±0.52 D; p=0.029), but not with the LSF calculator (-0.19±0.53 D; p=0.071). No significant differences between the formulas were found in terms of mean error modulus (p=0.238). New generation calculators showed a more frequent success in hitting target refraction (within ±1.00 D in more than 95% of cases) than the SRK/T formula (86%). CONCLUSION: The proposed method of adding 0.20 mm to the AL determined by UBM allows using this parameter in modern IOL calculation formulas and improving the refractive results of PE, especially in eyes with non-standard anterior segment structure.

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation.

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines' capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.

Efficient data integration under prior probability shift.

Conventional supervised learning usually operates under the premise that data are collected from the same underlying population. However, challenges may arise when integrating new data from different populations, resulting in a phenomenon known as dataset shift. This paper focuses on prior probability shift, where the distribution of the outcome varies across datasets but the conditional distribution of features given the outcome remains the same. To tackle the challenges posed by such shift, we propose an estimation algorithm that can efficiently combine information from multiple sources. Unlike existing methods that are restricted to discrete outcomes, the proposed approach accommodates both discrete and continuous outcomes. It also handles high-dimensional covariate vectors through variable selection using an adaptive least absolute shrinkage and selection operator penalty, producing efficient estimates that possess the oracle property. Moreover, a novel semiparametric likelihood ratio test is proposed to check the validity of prior probability shift assumptions by embedding the null conditional density function into Neyman's smooth alternatives (Neyman, 1937) and testing study-specific parameters. We demonstrate the effectiveness of our proposed method through extensive simulations and a real data example. The proposed methods serve as a useful addition to the repertoire of tools for dealing dataset shifts.

A method for determining groups in cumulative incidence curves in competing risk data.

The cumulative incidence function is the standard method for estimating the marginal probability of a given event in the presence of competing risks. One basic but important goal in the analysis of competing risk data is the comparison of these curves, for which limited literature exists. We proposed a new procedure that lets us not only test the equality of these curves but also group them if they are not equal. The proposed method allows determining the composition of the groups as well as an automatic selection of their number. Simulation studies show the good numerical behavior of the proposed methods for finite sample size. The applicability of the proposed method is illustrated using real data.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components.

Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practical applications.

Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

Testing conditional quantile independence with functional covariate.

We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.

Changes in ocular biometrics following cycloplegic refraction in strabismic and amblyopic children.

This study was aimed to analyze ocular biometric changes following cycloplegia in pediatric patients with strabismus and amblyopia. Cycloplegia is routinely used to measure refractive error accurately by paralyzing accommodation. However, effects on axial length (AL), anterior chamber depth (ACD), keratometry (Km), and white-to-white distance (WTW) are not well studied in this population. This retrospective study examined 797 patients (1566 eyes) undergoing cycloplegic refraction at a Samsung Kangbuk hospital pediatric ophthalmology clinic from 2010 to 2023. Ocular biometry was measured before and after instilling 1% cyclopentolate and 0.5% phenylephrine/0.5% tropicamide. Patients were categorized by strabismus diagnosis, age, refractive error and amblyopia status. Differences in AL, ACD, Km, WTW, and refractive error pre- and post-cycloplegia were analyzed using paired t tests. ACD (3.44 ±â€…0.33 vs 3.58 ±â€…0.29 mm, P < .05) and WTW (12.09 ±â€…0.42 vs 12.30 ±â€…0.60 mm, P < .05) increased significantly after cycloplegia in all groups except other strabismus subgroup (Cs) in both parameters and youngest subgroup (G1) in ACD. Refractive error demonstrated a hyperopic shift from -0.48 ±â€…3.00 D to -0.06 ±â€…3.32 D (P < .05) in overall and a myopic shift from -6.97 ±â€…4.27 to -8.10 ±â€…2.26 in high myopia (HM). Also, AL and Km did not change significantly. In conclusion, cycloplegia impacts ocular biometrics in children with strabismus and amblyopia, significantly increasing ACD and WTW. Refractive error shifts hyperopically in esotropia subgroup (ET) and myopically in high myopia subgroup (HM), eldest subgroup (G3) relating more to anterior segment changes than AL/Km. Understanding cycloplegic effects on biometry is important for optimizing refractive correction in these patients.

[Changes in intraocular pressure and biometric parameters of the anterior segment of the eye after intravitreal injections]. / Izmeneniya urovnya vnutriglaznogo davleniya i biometricheskikh pokazatelei perednego segmenta glaza posle intravitreal'nykh in"ektsii.

PURPOSE: This study compares the changes in the parameters of the anterior chamber of the eye using anterior segment optical coherence tomography (AS-OCT) in patients with a natural and artificial lens after treatment of neovascular age-related macular degeneration (nAMD) by multiple intravitreal injections (IVI) of anti-VEGF drugs. MATERIAL AND METHODS: The patients were divided into 2 groups: group 1 (control) included 30 patients (30 eyes) with a natural lens, group 2 - 30 patients (30 eyes) with an intraocular lens (IOL). AS-OCT was performed using the Revo NX tomograph (Optopol, Poland) to analyze anterior chamber depth (ACD) and the parameters of anterior chamber angle (ACA). Intraocular pressure (IOP) was measured with a contact tonometer ICare Pro. RESULTS: In patients with an IOL, the IOP level 1 minute after intravitreal injection (IVI) of an anti-VEGF drug was statistically lower than in the control group, on average by 17.8% during the first IVI and by 28.7% after 1 year of observation (p<0.001). ACD before treatment was statistically significantly higher in patients with IOL compared to patients of group 1 by an average of 39.3% (p<0.001). ACA from the nasal and temporal sides in the meridian 0°-180° before the start of treatment was statistically significantly wider in phakic patients than in the control group, by an average of 15.9±9.3° (p<0.001) and 16.9±8.2° (p<0.001), respectively. According to AS-OCT, there was no shift of the iris-lens diaphragm in patients with an IOL after multiple IVI of an anti-VEGF drug, in contrast to the control group. CONCLUSIONS: AS-OCT was used to determine for the first time the changes in the parameters of the anterior chamber of the eye in patients with a natural and artificial lens after multiple injections of an anti-VEGF drug in the treatment of nAMD.

Vector analysis of corneal astigmatism in cataractous eyes based on IOLMaster 700 biometry.

PURPOSE: The purpose of this study was to investigate the effect of the corneal back surface by comparing the keratometric astigmatism (K, derived from the corneal front surface) of a modern optical biometer against astigmatism of Total Keratometry (TK, derived from both corneal surfaces) in a large population with cataractous eyes. The results were then used to define linear prediction models to map K to TK. METHODS: From a large dataset containing bilateral biometric measurements (IOLMaster 700) in 9736 patients prior to cataract surgery, the total corneal astigmatism was decomposed into vectors for K, corneal back surface (BS), and TK. A multivariate prediction model (MV), simplified model with separation of vector components (SM) and a constant model (CM) were defined to map K to TK vector components. RESULTS: The K centroid (X/Y) showed some astigmatism with-the-rule (0.1981/-0.0211 dioptre (dpt)) whereas the TK centroid was located around zero (-0.0071/-0.0381 dpt against-the-rule) and the BS centroid showed systematic astigmatism against-the-rule (-0.2367/-0.0145 dpt). The respective TK-K centroid was located at -0.2052/-0.0302 dpt. The MV model showed the same performance (i.e. mean absolute residuum) as the SM did (0.1098 and 0.1099 dpt respectively) while the CM performed only slightly worse (0.1121 dpt mean absolute residuum). CONCLUSION: In cases where tomographic data are unavailable statistical models could be used to consider the overall contribution of the back surface to the total corneal astigmatism. Since the performance of the CM is sufficiently close to that of MV and SM we recommend using the CM which can be directly considered e.g. as surgically induced astigmatism.

Repeatability assessment of anterior segment measurements in myopic patients using an anterior segment OCT with placido corneal topography and agreement with a swept-source OCT.

BACKGROUND: The precision of anterior segment biometric measurements in eyes has become increasingly important in refractive surgery. The purpose of this study is to assess the repeatability of the automatic measurements provided by a new spectral-domain optical coherence tomograph (SD-OCT)/Placido topographer (MS-39, CSO) and its agreement with a swept-source OCT (SS-OCT) biometer (CASIA SS-1000, Tomey) in patients with myopia. METHODS: The right eye of 235 subjects was scanned 3 times with both devices. The evaluated parameters included central corneal radius of the steep meridian, central corneal radius of the flat meridian, mean central corneal radius, thinnest corneal thickness, central corneal thickness, anterior chamber depth, corneal volume and diameter. The intraobserver repeatability of the MS-39 measurements was calculated using intraclass correlation coefficient (ICC), within subject standard deviation, coefficient of repeatability, coefficient of variation and repeated-measures analysis of variance of the 3 repeated measurements. The agreement between the two devices was evaluated by 95% limits of agreement (LoA). RESULTS: The majority of the parameters acquired from MS-39 showed high repeatability. The repeatability of corneal diameter was slightly lower than the other measurements, although the ICC remained high. Agreement with the CASIA SS-1000 was good, indicated by the Bland-Altman plots with narrow 95% LoA values for all parameters assessed. CONCLUSIONS: The high repeatability of automatic measurements by the new device supports its clinical application in eyes with myopia, and the good agreement between the two devices indicates they could be used interchangeably for the parameters evaluated.

Differences Between Simulated Keratometry and Total Corneal Power in Eyes With Keratoconus and a Formula to Improve IOL Power Calculation Results.

PURPOSE: To compare simulated keratometry (SimK) and total corneal power (TCP) in keratoconic eyes, to determine whether the differences are systematic and predictable and to evaluate an adjusted TCP-based formula for intraocular lens (IOL) power calculation. METHODS: In a consecutive series of keratoconic eyes, measurements of SimK, TCP, posterior keratometry, and anterior and posterior corneal asphericities (Q-values) were retrospectively collected. The difference between SimK and TCP was linearly correlated to the biometric parameters. In a separate sample of keratoconic eyes that had undergone cataract surgery, IOL power was calculated with the Barrett Universal II, Hoffer QST, Holladay 1, Kane, and SRK/T formulas using the SimK and an adjusted TCP power. The respective prediction errors were calculated. RESULTS: A total of 382 keratoconic eyes (271 patients) were enrolled. An increasing overestimation of SimK by TCP was detected from stage I to III, with a significant correlation between the SimK and TCP difference and SimK in the whole sample (P < .0001, r2 = 0.1322). Approximately 7% of cases presented an underestimation of SimK by TCP. IOL power calculation with the adjusted TCP improved outcomes, achieving a maximum of 80% of eyes with a prediction error within ±0.50 diopters with the Hoffer QST, Holladay 1, and Kane formulas. CONCLUSIONS: Overall, SimK overestimated TCP. Such a difference could not be predicted by any variable. The proposed TCP-adjustment formula (TCPadj = TCP + 0.56 diopters) in keratoconic eyes for IOL power calculation might be valuable for improving refractive outcomes. [J Refract Surg. 2024;40(4):e253-e259.].

Addressing age measurement errors in fish growth estimation from length-stratified samples.

Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.

High-dimensional covariate-augmented overdispersed poisson factor model.

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.