Reliable Method For Estimation Research Articles

Comparison of turbidometric immunoassay and brix refractometry to radial immunodiffusion for assessment of colostral immunoglobulin concentration in beef cattle.

Colostral immunoglobulin G (IgG) concentration is critical to the attainment of adequate transfer of passive immunity in cattle, however, studies comparing available tools for measurement of colostral IgG concentration in beef cattle are limited. To report the agreement between 3 commercially available tests for evaluating IgG concentration in beef colostrum. Two hundred six beef-breed cows hospitalized for calving management or dystocia. Retrospective study to assess IgG of whole colostrum measured stall-side via turbidimetric immunoassay (TI) and brix refractometry (BRIX), compared to fat separated (FS) analysis via single radial-immunodiffusion (RID; reference standard), TI-FS and BRIX-FS. Test performance was assessed using Passing Bablock regression, Bland-Altman analysis, and area under the curve to determine optimal thresholds. Correlation between RID and TI-FS, BRIX-FS, or BRIX was similar (Spearman's ρ = 0.717, 0.715, 0.716, respectively) but correlation for TI was poor (ρ = 0.586). Regression analysis identified a substantial constant (-214.75 [CI: -272.03 to -178.07]) and proportional (13.24 [CI: 11.81-15.37]) bias between the RID and TI-FS which was similar for TI. TI-FS concentrations of 28.47, 38.75, and 50.62 g/L, BRIX-FS of ≤21.9%, ≤24.0%, and ≤27.4%, and BRIX of ≤21.3%, ≤23.8%, and ≤26.4% indicated IgG concentrations <50, <100, and <150 g/L, respectively; appropriate cutoffs for TI could not be generated. Both TI and TI-FS demonstrated a large constant and proportional bias compared to RID; BRIX and BRIX-FS were well correlated with RID and remain a reliable method for estimation of colostral IgG concentration in beef cattle.

Use of dimensions in posterior dentition for sex estimation in forensic contexts: A systematic review and meta-analysis

ObjectiveThis study assessed the validity of dimensions in posterior dentition for sex estimation in forensic contexts. DesignA systematic review was established following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). After assessing the risk of bias and methodological quality with the QUADAS-2 system, the data were subjected to statistical tests for a meta-analysis of diagnostic accuracy and I2 to verify the heterogeneity. ResultsThe search resulted in 15 studies that underwent qualitative testing, all were selected for quantitative analysis. The papers included: the mesiodistal of the upper first molar, lower first molar, and upper second molar, and the buccolingual of the upper first molar and upper second molar. The results showed that sensitivity and specificity rates were lower with the mesiodistal diameter, with rates of 0.577 for the lower first molar, 0.674 for the upper first molar, and 0.698 for the upper second molar, while the rates were higher with the buccolingual diameter, with 0.724 for the upper first molar, and 0.743 for the upper second molar. The power to estimate sex is greater for males than for females. High heterogeneity was detected among the studies of almost all dimensions, except sensibility for the lower first molar and specificity for the upper second molar. ConclusionsNone of the dimensions reached an accuracy of ≥80%, however, so they are not a reliable method for sex estimation in forensic practice.

Advanced machine learning-based kharif maize evapotranspiration estimation in semi-arid climate.

Accurate Crop Evapotranspiration (ETc) estimation is crucial for understanding hydrological and agrometeorological processes, yet it's challenged by multiple parameters, data variations, and lack of continuity. These limitations restrict numerical methods application. To address this, the study aims to develop and assess ML models for daily maize ETc in semi-arid areas, utilizing varied weather inputs. Five ML models viz., Category Boosting (CB), Linear Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were developed and validated for the ICAR-IARI, New Delhi, Research Station. Penman-Monteith (PM) model estimated ETc values are used as the standard for comparing the performance of the ML model values. Results revealed that the SVM model achieved the highest coefficient of determination (R2) among all models, with a value of 0.987. Furthermore, the SVM model exhibited the lowest model errors (MAE = 0.121 mm day-1, RMSE = 0.172 mm day-1, and MAPE = 4.37%) compared to other models. The ANN model also demonstrated promising results, comparable to the SVM model. Notably, the wind speed parameter was found most influential input parameter. In conclusion, SVM or ANN could be considered reliable alternative methods for the accurate estimation of kharif maize crop ETc in the semi-arid climate.

Enhancing long-term stability of photoacoustic gas sensor using an extremum-seeking control algorithm

Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical sensor used in various applications due to its enhanced performance characteristics. However, the accuracy and reliability of gas concentration measurements from photoacoustic gas sensors may be impacted by several known limitations, including drift of the gas cell resonant frequency over extended periods of time. Researchers have proposed various solutions, including optimization methods and signal processing algorithms, to address this and others issues. In this paper, we propose a novel solution using an extremum-seeking control algorithm to manage the laser modulation frequency of photoacoustic gas sensors. By tracking the changing resonant frequency of the gas cell, long-term stability can be achieved, making it suitable for environmental monitoring, petroleum exploration, and industrial process control. Our approach has the potential to improve the accuracy and reliability of long-term measurements obtained from photoacoustic gas sensors, providing a stable and reliable method for gas concentration estimation.

Optimal vector matching fusion method for bionic compound eye polarization compass and inertial sensor integration

The compound eyes of insects can extract the heading by sensing multi-directional polarization information. Inspired by this, the integration with bionic eye polarization compass and low-precision MEMS inertial sensors yields a new choice for autonomous navigation in a GPS-denied environment. However, the occlusion environments, such as jungles and buildings, seriously affects the attitude estimation accuracy by destroying the polarization information. Considering the above problem, this paper proposes a reliable attitude estimation method based on vector matching measurement models for integration with bionic compound eye polarization compass and low-cost MEMS inertial sensors, including the polarization vector, solar vector, and gravity vector. To realize the matching and fusion among these vector models, the vector optimization selection algorithm is designed according to the number of visible polarization sensors in the occlusion environment. Particularly, the vector optimization selection factor is designed according to the error between the measured and theoretical polarization vectors. Furthermore, when using the solar vector estimation model, the degree of the polarization is applied to improve the calculation accuracy of the solar vector. The gravity vector measurement model is employed to enhance the autonomy of the integration. Finally, the effectiveness of the proposed method is verified by simulated and outdoor experiments under tree obscuration.

Bedside ultrasound of the internal jugular vein to assess fluid status and right ventricular function: The POCUS-JVD study

BackgroundAccurate estimation of fluid status is important in the management of heart failure patients, however, the current methods for bedside assessment can be unreliable or impractical for daily use. MethodsNon-ventilated patients were enrolled immediately prior to scheduled right heart catheterization (RHC). Using M-mode, IJV maximum (Dmax) and minimum (Dmin) anteroposterior diameters were measured during normal breathing, while supine. Respiratory variation in diameter (RVD) was calculated as [(Dmax - Dmin)/Dmax] in percentage. Collapsibility with sniff maneuver (COS) was assessed. Lastly, inferior vena cava (IVC) was assessed. Pulmonary artery pulsatility index (PAPi) was calculated. Data was obtained by five investigators. ResultsTotal 176 patients were enrolled. Mean BMI was 30.5 kg/m2, LVEF 14–69% (range), 38% with LVEF ≤35%. The POCUS of IJV could be performed in all patients in <5 min.Increasing RAP demonstrated progressive increase in IJV and IVC diameters. For high filling pressure (RAP ≥10 mmHg), an IJV Dmax ≥1.2 cm or IJV-RVD < 30% had specificity >70%. Combining the POCUS of IJV to physical examination improved the combined specificity to 97% for RAP ≥10 mmHg. Conversely, a finding of IJV-COS was 88% specific for normal RAP (<10 mmHg). An IJV-RVD <15% is suggested as a cutoff for RAP ≥15 mmHg. The performance of IJV POCUS was comparable to IVC.For RV function assessment, IJV-RVD < 30% had 76% sensitivity and 73% specificity for PAPi <3, while IJV-COS was 80% specific for PAPi ≥3. ConclusionPOCUS of IJV is an easy to perform, specific and reliable method for volume status estimation in daily practice. An IJV-RVD < 30% is suggested for estimation of RAP ≥10 mmHg and PAPi <3.

Vibration Fatigue Testing Procedure of High Strength MARS 600 Steel Fillet Welds Using Stainless Steel Consumable Electrode

Implementation of high strength steels in welded structural designs in the automotive, defence and construction industries is constantly increasing. Prolonged usage of such structures requires deep understanding of welded joint fatigue as well as a reliable and feasible life estimation methods development. Conventional fatigue testing methods often require costly, expensive in maintenance, high loading capacity equipment. They are also commonly restricted to specific specimen geometry and are time consuming due to the limit of a single specimen per test setup. This work presents high cycle fatigue (HCF) testing of high strength MARS 600 welded steel using a quick, simple and efficient resonance fatigue testing (RFT) method. The specimen is a simple cantilever fillet welded to a base plate using austenitic stainless steel 307L consumable electrode. Electrodynamic shaker is used for harmonic base excitation at a constant operating frequency. Several specimens welded to the common base are tested simultaneously, allowing completion of a high number of cycles and statistics in a relatively short time period. A hybrid, practical research approach combining experimental, finite element analysis (FEA), numerical and analytical calculations is presented. Fracture mechanics approach for fatigue life assessment is implemented. Crack growth calculation is based on the Paris - Erdogan law. Reduction in structural integrity due to crack propagation causes a reduction in natural frequency and transmissibility. The change in gain is evaluated via the open crack FEA model and integrated into the crack propagation algorithm. Resonance search, track and dwell module (RSTD) for maintaining constant gain throughout the test is not required. Fatigue life Wohler (SN) curve is constructed. Standard weld fatigue data is often provided for direct loading (tensile stress) and for different stress ratio (R) values. Corrections for mean stress and loading application are required. Current fully reversed (R = -1), indirect loading (bending stress) test results may be readily applied for random vibration fatigue analyses post processing. As expected, actual fatigue life results are higher compared to standard design curves, implying correctness of the manufacturing welding process of examined specimens. The presented procedure is of interest for research as well as for industrial welding processes testing, optimization and qualification.

Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model.

We construct a reliable estimation method for evolutionary parameters within the Wright-Fisher model, which describes changes in allele frequencies due to selection and genetic drift, from time-series data. Such data exist for biological populations, for example via artificial evolution experiments, and for the cultural evolution of behavior, such as linguistic corpora that document historical usage of different words with similar meanings. Our method of analysis builds on a Beta-with-Spikes approximation to the distribution of allele frequencies predicted by the Wright-Fisher model. We introduce a self-contained scheme for estimating parameters in the approximation, and demonstrate its robustness with synthetic data, especially in the strong-selection and near-extinction regimes where previous approaches fail. We further apply the method to allele frequency data for baker's yeast (Saccharomyces cerevisiae), finding a significant signal of selection in cases where independent evidence supports such a conclusion. We further demonstrate the possibility of detecting time-points at which evolutionary parameters change in the context of a historical spelling reform in the Spanish language.

Dental age assessment: scoring systems and models from the past until the present-how is it presented in the court?

The aim of this study is to evaluate the accuracy of the methods from the past and present, such as Demirjian, Cameriere, and AlQahtani, as well as to determine the most reliable method for human age estimation under the request from the courts according to the effect size from each method in age estimation. Four hundred eighty-three orthopantomographic images were selected from 318 patients from Centro Hospitalar Universitário Lisboa Norte aged between 6 and 15years old. Several measurements of widths and lengths and classification of tooth development stages were performed, according to each age estimation method. The listing of patients and orthopantomographic images were consulted through SECTRA®. All data was entered and analysed using SPSS version 28. Observations' validity was verified through inter- and intraobserver validation. The correlation coefficients between age and age estimates by the three methods on both sides were close to 90%. Regarding the estimation error correlation coefficient, Demirjian and AlQahtani values were low, whereas Cameriere's was significantly negative; i.e., underestimation increases as age increases. Between left and right, any significant difference in age estimation in AlQahtani and Cameriere methods was not observed, but there were great variability and large effect for the Demirjian method. Comparing females and males, the statistical analysis showed no significant differences and quite small effects in the precision of the estimates for any of the methods. Finally, although significant differences were detected when comparing estimated values and age, small effects were revealed except for the Demirjian method which attained a medium effect and, therefore, less consistency of estimation. Since it was not possible to determine the most reliable method for age estimation, a combined application of different age estimation methodologies is recommended to be use in the courts with relevant statistical data such as effect size.

Reliability Analysis of Structures by Active Learning Enhanced Sparse Bayesian Regression

Adaptive sampling near a limit state is important for metamodeling-based reliability analysis of structures involving an implicit limit state function. Active learning based on the posterior mean and standard deviation provided by a chosen metamodel is widely used for such adaptive sampling. Most studies on active learning-based reliability estimation methods use the Kriging approach, which provides prediction along with its variance. As with the Kriging approach, sparse Bayesian learning-based regression also provides posterior mean and standard deviation. Due to the sparsity involved in learning, it is expected to be computationally faster than the Kriging approach. Motivated by this, active learning-enhanced adaptive sampling-based sparse Bayesian regression is explored in the present study for reliability analysis. In doing so, polynomial basis functions, which do not involve free parameters, are chosen for the sparse Bayesian regression to avoid computationally expensive parameter tuning. The convergence of the proposed approach is attained based on the stabilization of 10 consecutive failure estimates. The effectiveness of the proposed adaptive sparse Bayesian regression approach is illustrated numerically with five examples.

A comparative study of in vitro dose-response estimation under extreme observations.

Quantifying drug potency, which requires an accurate estimation of dose-response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median-effect equation to describe the dose-response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose-response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression-based methods have also been introduced in estimation of the median-effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose-response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification.

A Framework for Reliability Analysis of Combinational Circuits Using Approximate Bayesian Inference

A commonly used approach to compute the error rate at the primary outputs (POs) of a circuit is to compare the fault-free and faulty copies of the circuit using XOR gates. This model results in poor accuracies with nonsampling-based methods for reliability estimation. An alternative is to use a single copy of the circuit with a four-valued representation for each net corresponding to the correct and incorrect signals. One problem in this formulation is the accurate propagation of associated probabilities. We use the framework of Bayesian inference (BI) to address this issue. We derive the conditional probability distribution (CPD) corresponding to the four-valued signals and find the output error rate using various approximate BI techniques. With our formulation, we demonstrate that the output error rate scales with the gate error probabilities. It is guaranteed to be zero when the gate error probability is zero, provided approximate BI algorithms based on sum-product belief propagation (BP) are used. Although inaccuracies increase at very low gate error probabilities, it is able to capture the relative reliability of outputs with respect to each other. We also propose a new method for finding the overall circuit error rate as the partition function for a fixed state of POs. This method provides a significant improvement in accuracy when compared with the existing method using OR gates.

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health-economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. A total of 7421 computed tomography (CT) datasets between January 2007-July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52-11.03) for the convolutional neural network (CNN), 9.96 h (8.68-11.32) for the radiomics model, 13.38 h (11.21-15.74) for rater 1 and 11.21 h (9.61-12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.

Age and growth assessment of the near-threatened fish Rita rita (Siluriformes: Bagridae) in the Ganges basin

Introduction: Rita rita is a freshwater catfish under threat of extinction, mainly from loss of breeding and nursing grounds. A reliable method for age and growth estimation is needed by fishery managers. Objective: To identify the best body structure for age and growth estimation. Methods: We assessed estimates precision based on Average Percent Error (APE), Coefficient Variation (CV), and Percent Agreement (PA) between readers separately analyzed each calcified structure. We used 390 fish samples from three rivers, Ganga, Yamuna and Ramganga, from September 2018 to August 2019. Results: The three indicators favored the use of vertebrae for age estimation; the growth band seems to be annual and formed from May to September. The growth equations were Lt = 90.19(1-e-0.145(t+0.51)) for Ganga; Lt = 91.19 (1-e-0.14(t+0.59)) for Yamuna and Lt = 89.63 (1-e-0.15(t+0.68)) for Ramganga. Conclusion: This species reaches moderate growth in these rivers, where vertebrae are the recommended age estimation structure, followed, in case of need, by sectioned otoliths, whole otoliths and opercular bones. Pectoral spines should be avoided, especially in older fish.

Applied Complex Diagnostics and Monitoring of Special Power Transformers

As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.

Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method

Generative models of the human connectome enable in silico generation of brain networks based on probabilistic wiring rules. These wiring rules are governed by a small number of parameters that are typically fitted to individual connectomes and quantify the extent to which geometry and topology shape the generative process. A significant shortcoming of generative modeling in large cohort studies is that parameter estimation is computationally burdensome, and the accuracy and reliability of current estimation methods remain untested. Here, we propose a fast, reliable, and accurate parameter estimation method for connectome generative models that is scalable to large sample sizes. Our method achieves improved estimation accuracy and reliability and reduces computational cost by orders of magnitude, compared to established methods. We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters. While we focus on the classic two-parameter generative model based on connection length and the topological matching index, our method can be generalized to other growth-based generative models. Our work provides a statistical and practical guide to parameter estimation for connectome generative models.

Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling

Reliability of blades has a profound impact on both serviceability and safety of offshore wind turbines. Reliability estimation of wind turbine blade is a complex and time-consuming problem with multi-physics coupling and multi-failure modes correlation. Developing physics-of-failure modeling and reliability analysis methods with high efficiency and accuracy is a long-term challenge. In this work, a high availability and cost-effectiveness reliability estimation framework for offshore wind turbine blade by combining transfer learning (TL) and direct probability integral method (DPIM) is proposed. Extensive performance simulation of offshore wind turbine blade is a complex and necessary task, this paper develops a new adaptive sampling strategy to improve the validity of sample selection in the design space; On this basis, a physics-of-failure surrogate modeling approach is proposed by introducing TL method to fuse two kinds of multi-physics coupling analysis data, then the performance of all critical loads can be predicted efficiently in advance to provide a reliable design; Further, this paper provides an efficient reliability estimation method for offshore wind turbine blades by combining DPIM and surrogate model. Finally, the validity of the proposed approach is illustrated by numerical example and offshore wind turbine blade reliability estimation. The proposed framework provides a cost-effective alternative to higher loads simulation efforts and safety factors selection.

A reliability estimation method based on signal feature extraction and artificial neural network supported Wiener process with random effects

Accurately predicting degradation and reliability is the key to effective condition-based machinery maintenance. This paper presents a reliability estimation method based on an artificial neural network (ANN)-supported Wiener process model with random effects. A number of time-domain features, frequency-domain features, and intrinsic energy features are considered to reflect the health condition of products. These features can be selected by the PROMETHEE II method considering multi-sample and multi-measure conditions. An ANN-supported health index function is defined to describe the relationship between the health condition and the selected signal features. Based on the health index function, an ANN-supported Wiener process is used to model machine degradation. The corresponding health index function training and process parameter inference approaches are presented. An actual testing dataset of a machine bearing is used to demonstrate the proposed method. The proposed method provides accurate service life and reliability predictions.

Comparative Study of Flood and Long-term Mean Monthly Flow Estimation Approaches: Case Studies of Six Basins in Nepal

&#x0D; &#x0D; &#x0D; Most of Nepal's river basins have poor hydro- meteorological databases, with several river basins being ungauged. Thus, hydrological parameters need to be estimated using different types of computation methods. The primary goal of this study is to identify the most accurate method for calculating peak flood and long-term mean monthly flow among the most commonly used methods in Nepal. We compared the peak flood calculated using various flood computation formulas, such as Hydest, Modified Hydest, MHSP (Medium Hydropower Study Project) 1997, Modified Dickens, PCJ (Prem Chandra Jha) 1996, Rational, and Specific Discharge, to the flood calculated using gauged discharge data frequency analysis. We find that it is wise to use the Modified Hydest method in the khokana basin for all Return Periods (RPs) and in the Belkot basin (for RP ≤ 100 years), the Specific Discharge method in the Jamu basin, the MHSP 1997 method in the Belkot basin (for RP ≤ 100 years) and the Bagasoti Gaun basin (for RP ≤ 20 years). The PCJ 1996 method having the lowest cumulative value of Root Mean Square Error (RMSE) for the six studied catchments is suitable for Rabhuwa Bazar (for RP &gt; 50 years) and Bagasoti Gaun (for RP &gt;20 years). Similarly, the Modified Dickens method is suitable in the Bagasoti Gaun basin for RP ≤ 50 years. This paper also shows the performance of the Hydest and MHSP 1997 mean flow estimation methods and suggests different coefficients or constants to be used with the MHSP 1997, Modified MIP (Medium Irrigation Project), and Hydest methods to obtain more reliable long-term mean monthly flows. Overall, our study will help the designer choose a reliable method for design flow estimation. This study also shows that the flow obtained from even the most suitable methods needs to be adjusted. As a result, intensive research is required to adjust previous methods and develop the new one.&#x0D; &#x0D; &#x0D;

Diagnostic Accuracy of Kramer Visual Assessment of Jaundice in Term Neonates Taking Total Serum Bilirubin Levels as the Gold Standard

Objective: To determine the Diagnostic accuracy of Kramer's visual assessment of jaundice in term neonates by taking total serum bilirubin levels as a gold standard.&#x0D; Study Design: Cross-sectional study.&#x0D; Place and Duration of Study: Neonatology Department, Pak Emirates Military Hospital, Rawalpindi Pakistan, Mar to Sep 2019.&#x0D; Methodology: The study was based on examining and investigating 300 neonates. Kramer scale was used to examine jaundice in all neonates, and results were compared to total serum bilirubin levels.&#x0D; Results: A total of 300 patients were included in this study. The mean bilirubin level was 229.1±74.5mol/L. The sensitivity and specificity of visual assessment of jaundice using the Kramer visual scale were 83.84% and 73.53%, respectively, for neonates who were not jaundiced below the abdomen and thighs included in Kramer zones 1 to 3.&#x0D; Conclusion: Kramer visual assessment can be used as a noninvasive screening method in neonates jaundiced up to the abdomen and thighs, which is included in Kramer zones 1 to 3 but for markedly jaundiced neonates Kramer scale is not a reliable noninvasive method of bilirubin estimation.