Accuracy Of Weight Estimation Research Articles

Enhancing instance segmentation: Leveraging multiscale feature fusion and attention mechanisms for automated fish weight estimation

The accurate estimation of fish weight relies on the crucial parameter of individual fish contour features. While instance segmentation proves effective in extracting fish contours, challenges arise from diverse fish postures and reduced image sharpness underwater. Current instance segmentation methods often struggle to effectively balance global and local detailed features, which can result in inaccurate positioning of contour keypoints and consequently limit the accuracy of fish weight estimation. To overcome this, our study introduces a novel instance segmentation network tailored for precise fish contour extraction. The proposed approach incorporates multi-scale feature fusion and an attention mechanism based on the Segmenting Objects by Locations (SOLO) network, referred to as SOLO-MFFA. This paper designs a multi-scale context aggregation module to integrate features with a wider range of receptive fields, augmenting the model's capability to comprehend both local features and global information. At the same time, the introduction of a mixed domain attention mechanism emphasizes more critical channel features, and simultaneously improves the localization accuracy of contour points. Compared with SOLO and its improved model CAM-SOLO on the fish instance segmentation dataset, SOLO-MFFA’s demonstrated an effective improvement, with a 4.3% and 1.6% increase in mAP (mean Average Precision), respectively. The Decoupled-SOLO-MFFA achieved higher mAP. The visualization results also demonstrate that the contour features extracted in this paper are smoother and more accurately positioned. Additionally, in comparison to other well-known instance segmentation networks, our method has demonstrated significant improvements in both qualitative and quantitative evaluations. Furthermore, the integration of contour features derived from Decoupled-SOLO-MFFA, along with binocular vision, was utilized for the precise estimation of fish perimeter and weight. The findings reveal a strong correlation between the perimeter calculated by Decoupled-SOLO-MFFA and the actual weight, with a notably reduced error in weight estimation. Compared to previous methods, RMSE, MAE, and MAPE of the linear model constructed in this paper decreased by 3.92, 3.19, and 1.4%.

Cat owners do not provide more reliable weight estimates for cats compared with veterinary professionals in an emergency setting.

The aim of the present study was to assess the accuracy of weight estimation in cats provided by pet owners, veterinary technicians, house officers (interns and residents) and attending clinicians in an emergency room (ER). An additional objective was to determine whether carrying the cat contributed to a more accurate weight estimate. A total of 72 cats presented to an ER and were enrolled in the study. Pet owners, veterinary technicians, house officers and attending clinicians were asked to record the cats' estimated weights on individual data collection cards. The actual weights of the cats were then obtained and compared with participants' estimations. There were no significant differences between weight estimates provided by pet owners, veterinary technicians, house officers and attending clinicians. Similarly, neither the length of experience of the veterinary staff nor carrying the cat had an effect on the provision of a more accurate weight estimate. Pet owners are no better at weight estimation of cats in the ER setting compared with veterinary professionals. Carrying the cat did not improve the accuracy of weight estimation among veterinary professionals. The mean cat weight of 4.9 kg could be used as an average cat weight in an emergency situation for an adult cat.

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire-Landrace crossbred sows. We selected 200 Yorkshire-Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire-Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning.

This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight. A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model. The accuracy of body weight estimation was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman rank correlation coefficient ( ρ ). In the linear regression models, the estimated body weight at the T5 level correlated best with the actual body weight (MAE, 5.39 kg; RMSE, 7.01 kg; ρ = 0.912). The deep learning-based models showed the best accuracy at the L5 level (MAE, 6.72 kg; RMSE, 8.82 kg; ρ = 0.865). Although both methods were feasible for estimating body weight at different single-slice CT levels, the linear regression model using total cross-sectional body area at the T5 level as an input variable was the most favorable method for single-slice CT analysis for estimating body weight.

IMPROVING THE ACCURACY OF CALCULATION WEIGHTS BY VISUAL INPUT: A PILOT STUDY IN THE EMERGENCY DEPARTMENT OF A LOW MIDDLE-INCOME COUNTRY

Accurate weight estimation is critical in emergency medical scenarios requiring immediate interventions. This pilot study explores the feasibility of improving weight calculation accuracy through visual input, focusing on height-based estimations. The research aims to contribute valuable insights to weight estimation methodologies, particularly in resource-constrained settings. The objective of the pilot study is to explore the feasibility of improving weight calculation accuracy through visual input, focusing on height-based estimations. Data was collected encompassing diverse height ranges from 1.45 to 1.94 meters. Comprehensive datasets included actual body weights, estimated weights, standard deviation, and standard error. eBW(kg) = (N− 1)100 is the estimated body weight method, where "N" is the height measured in meters. Body weight classifications were employed to analyze the accuracy of estimations further. Correction factors for everyone were computed. The correction factor separates the obtained data into underweight, close to actual weight, and overweight. Following optimization, the average correction factor for every category is updated. These updated correction factors improve weight estimation precision. Linear regression analyses were conducted to compare actual and estimated weights, visually representing the discrepancies. The calculated correction factors are essential to improving weight calculations in medicine. The thorough research and improvement procedure resulted in revised correction factors significantly improving the precision of weight estimates in the medical field. We can say that the following equation provides a more accurate weight estimate. Wt corrected = (N-1) x 100 x Correction Factor. This revised weight is an enhanced estimate that considers the updated correction factors. The calculated correction variables are essential to improving weight estimations in medicine. The thorough research and improvement procedure resulted in revised correction factors that significantly improved the precision of weight estimations in the medical field. The derived correction factors demonstrate their effectiveness in enhancing weight estimations, notably in specific patient groups. The investigation classifies patients and delivers precise modifications to improve weight estimations, ensuring safer prescribing procedures. The proposed correction variables will be critical in evaluating emergency medicine doses for individuals.

Prawn morphometrics and weight estimation from images using deep learning for landmark localization

Accurate morphometric analyses and weight estimation are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production animals. However, the collection of phenotypic data through traditional manual approaches at industrial scales and in real-time is time-consuming, labour-intensive, and prone to errors. Digital imaging of individuals and subsequent training of prediction models using Deep Learning (DL) has the potential to rapidly and accurately acquire phenotypic data from aquaculture species. In this study, we applied a novel DL approach to automate morphometric analysis and weight estimation using the black tiger prawn (Penaeus monodon) as a model crustacean. The DL approach comprises two main components: a feature extraction module that efficiently combines low-level and high-level features using the Kronecker product operation; followed by a landmark localization module that then uses these features to predict the coordinates of key morphological points (landmarks) on the prawn body. Once these landmarks were extracted, weight was estimated using a weight regression module based on the extracted landmarks using a fully connected network. For morphometric analyses, we utilized the detected landmarks to derive five important prawn traits. Principal Component Analysis (PCA) was also used to identify landmark-derived distances, which were found to be highly correlated with shape features such as body length, and width. We evaluated our approach on a large dataset of 8164 images of the Black tiger prawn (Penaeus monodon) collected from Australian farms. Our experimental results demonstrate that the novel DL approach outperforms existing DL methods in terms of accuracy, robustness, and efficiency.

On-Plant Size and Weight Estimation of Tomato Fruits Using Deep Neural Networks and RGB-D Imaging

Highlights Deep learning-based instance segmentation models were applied and evaluated for tomato fruit detection. Mask R-CNN with vision transformer backbone showed the highest accuracy for tomato instance detection. Size and weight estimation indexes were calculated using tomato region depth data from instance segmentation models. Area-based index has higher accuracy for weight estimation than indexes based on weight and height information. Abstract. The size and weight of fruits are crucial factors in yield prediction and determining harvesting time. Machine vision, including fruit detection, is a key technology in the automated monitoring and harvesting of fruits. In particular, deep learning-based fruit-detection methods have been actively applied. Estimation of fruit size after fruit detection requires depth information, which can be acquired using depth imaging. RGB-D cameras include color and depth information required for fruit detection and size estimation. In this study, the RGB-D imaging technique was used to estimate the size and weight of tomatoes. Furthermore, deep learning-based instance segmentation models, including Mask R-CNN, YOLACT, and RTMDet for tomato fruit detection, were trained and evaluated. The proposed method estimated the fruit width with a root mean square error (RMSE) of 4 mm, a mean absolute percentage error (MAPE) of 4.28%, and a fruit height with an RMSE of 5.12 mm and a MAPE of 6.42%. Furthermore, the weight-prediction model based on the area index estimated the tomato fruit weight with an RMSE of 19.69 g and MAPE of 9.44%. Thus, the method can be used for accurate size and weight estimation and can be applied in growth monitoring and automated tomatoes harvesting. Keywords: Deep learning, Fruit sizing, Instance segmentation, RGB-D, Tomato.

Accuracy of sonographic fetal weight estimation and prediction of birth-weight discordance in twin pregnancy: large single-center study.

To determine the accuracy of sonographic fetal weight estimation in predicting birth weight (BW) and BW discordance in twin gestations, and to evaluate maternal and fetal characteristics that may affect the accuracy of this assessment. This was a retrospective cohort study of all twins delivered at a single tertiary medical center between 2010 and 2021. Twin gestations for which sonographic estimation of fetal weight was performed within the week preceding delivery were included. Statistical analysis was performed to evaluate the strength of the correlation between sonographic estimated fetal weight (EFW) and BW, and to determine the impact of maternal and fetal factors on the accuracy of sonographic estimation. The study included 2154 twin pregnancies. There was a strong correlation between sonographic EFW and corresponding BW for all twins (r = 0.922; P < 0.001). Strong correlations were observed for both the presenting and non-presenting cotwin (r = 0.921 and r = 0.922, respectively; both P < 0.001), as well as the larger and smaller cotwin (r = 0.928 and r = 0.934, respectively; both P < 0.001). The overall mean ± SD absolute error of sonographic EFW was 7.41 ± 6.81%. This error was greater for the non-presenting cotwin compared with the presenting cotwin (7.99 ± 6.12% vs 7.17 ± 5.64%; P < 0.001), and for the smaller cotwin compared with the larger cotwin (8.56 ± 7.50% vs 6.58 ± 5.47%; P < 0.001). Advanced gestational age at scanning was correlated inversely with the mean absolute error of sonographic EFW. Multivariate logistic regression indicated that an earlier gestational age at scanning, being the non-presenting cotwin and being the smaller cotwin were independent risk factors for sonographic EFW inaccuracy. Pregnancies in which the presenting twin was estimated to be the smaller cotwin had twice the rate of false-positive BW discordance compared with pregnancies in which the presenting twin was estimated to be the larger cotwin (36.0% vs 13.0% for BW discordance > 15%, 35.0% vs 17.0% for BW discordance > 20% and 37.7% vs 12.1% for BW discordance > 25%; all P < 0.001). The error in sonographic EFW discordance was not related to chorionicity, the position of the presenting fetus or gestational age at the time of fetal weight estimation. Sonographic estimation of fetal weight within 7 days before delivery accurately predicts BW in twin pregnancy. Sonographic EFW accuracy is reduced for the non-presenting twin, the smaller cotwin and when delivery occurs at an earlier gestational age. Sonographic estimation of fetal weight discordance is less accurate when the presenting twin is the smaller cotwin. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Comparing estimation methods for psychometric networks with ordinal data.

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: extended Bayesian information criterion graphical lasso with regularized edge estimates ("EBIC"), Bayesian information criterion model selection with partial correlation edge estimates ("BIC"), and multiple regression with p-value-based edge selection and partial correlation edge estimates ("MR"). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias. Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Feature extraction using multi-view video analytics for dairy cattle body weight estimation

Accurate body weight (BW) estimation of livestock provides valuable information about animal health and welfare and can be used to assess changes in nutrient status of individuals or groups of animals. To obtain BW information regularly, automated low-cost methods that do not intrude upon normal farm operation are essential. Measurements of BW from 196 lactating, Holstein dairy cattle were collected across 5 days (655 +/- 77.1 kg; range 458 - 876 kg). This population of animals was used to develop a BW estimation system for dairy cattle using a synchronized two-camera system. The system applies deep-learning models and domain knowledge to both camera views to estimate BW of dairy cattle despite challenges such as occluding fences and crowded backgrounds. Videos from side and front-view were used to extract features about the anatomical locations and shape of the individual cows, and a regression model was applied to the features for BW prediction. We demonstrate that each view provides valuable information for BW estimation, where combining two views outperforms either view separately. This is despite the side view having distractors such as occluding fences and crowded backgrounds. The experimental results, applied to videos captured within the constraints of an operating dairy farm, demonstrate a root-mean-squared prediction error of 34 kg and a mean absolute percentage error of 4.1%, when a Random Forest is trained on four days of data and tested on the fifth unseen day. This model enabled 57% of cattle BW to be estimated within 25 kg of their measured BW. In addition, we demonstrated that linear regression generalized well to unseen data.

Improving Lettuce Fresh Weight Estimation Accuracy through RGB-D Fusion

Computer vision provides a real-time, non-destructive, and indirect way of horticultural crop yield estimation. Deep learning helps improve horticultural crop yield estimation accuracy. However, the accuracy of current estimation models based on RGB (red, green, blue) images does not meet the standard of a soft sensor. Through enriching more data and improving the RGB estimation model structure of convolutional neural networks (CNNs), this paper increased the coefficient of determination (R2) by 0.0284 and decreased the normalized root mean squared error (NRMSE) by 0.0575. After introducing a novel loss function mean squared percentage error (MSPE) that emphasizes the mean absolute percentage error (MAPE), the MAPE decreased by 7.58%. This paper develops a lettuce fresh weight estimation method through the multi-modal fusion of RGB and depth (RGB-D) images. With the multimodal fusion based on calibrated RGB and depth images, R2 increased by 0.0221, NRMSE decreased by 0.0427, and MAPE decreased by 3.99%. With the novel loss function, MAPE further decreased by 1.27%. A MAPE of 8.47% helps to develop a soft sensor for lettuce fresh weight estimation.

EP14.18: Accuracy of sonographic fetal weight estimation and birthweight discordance in twin pregnancies

EP14.18: Accuracy of sonographic fetal weight estimation and birthweight discordance in twin pregnancies

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network.

The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig's back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

The Impact of the Active Stage of Labor on Estimate Fetal Weight: A Prospective Cohort Study

Objective: In the literature, there have been few data on ultrasound measurements during the active phase of labor. In this study, it was aimed to compare the accuracy of fetal weight estimation by ultrasound in terms of active phase of labor. Methods: This was a prospective cohort study at the obstetrics clinic of Esenler Gynecology and Children's Hospital in Istanbul. A total of 85 patients in active labor were included in the study. All pregnant women were evaluated for estimated fetal weight (EFW) by ultrasound evaluation, and cervical dilation and effacement measurements by pelvic examination, at most 24 hours before labor. Hadlock-4 formula was used for EFW. EFW and actual birth weight (ABW) and absolute errors of pregnant women in active labor were compared. Results: The mean EFW and ABW of the patients were 3161±482 and 3150±476 g, respectively. In terms of EFW and ABW, a finding in favor of the ultrasound was found. The error and error rates between EFW and ABW were found to be 234±191 g and 7.6±6.2, respectively. No significant difference was found in terms of these parameters (p&amp;gt;0.05). A significant and strong (0.80) correlation was found with estimated fetal weights and actual birth weights. Conclusion: Ultrasound can be used as a safe method to measure estimated fetal weight in the active phase of labor.

Data-Driven Bridge Weigh-in-Motion

Heavy vehicle detection in the road system is now an urgent issue from the perspectives of law enforcement and road health monitoring. Weigh-in-motion (WIM) is a technology which estimates vehicle weights without stopping the vehicles. Paved WIM (PWIM) is expensive and has limited installation locations. Bridge WIM (BWIM), which utilizes bridge components as weight scales, is quite inexpensive and easier to install. BWIM requires the dynamic characteristics of the bridge and traffic conditions for accurate weight estimation. In general, such characteristics are measured by several experimental runs using a vehicle with known axle weights. The weighing accuracy may be greatly degraded owing to the influence of the other traveling vehicles. In this paper, we propose a data-driven BWIM using a neural network. The model parameters are optimized automatically by video analysis and vehicle identification between WIMs. The model can estimate vehicle weights accurately considering various traffic conditions that may degrade the weighing accuracy.

Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.

Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.

Assessment of the Accuracy of Visual Weight Estimates of Cats

Visual estimation of cat weight is commonly performed during trap-neuter-return (TNR) events where cats are brought in humane traps of various models with differing weights. These cats are typically asocial and cannot be safely handled before the induction of anesthesia. Weight estimate accuracy is important since it determines the anesthetic induction dose. This study measured the accuracy of the visual weight estimates made by various veterinary professionals involved in the program, including a veterinarian, veterinary students of all years, and several veterinary technicians, and explored whether there were variables that systematically predicted percent deviation of estimate from actual weight. Subjects estimated the weight and noted the posture (standing, sitting, crouching, laying). Cat demographics, including actual weight (kg), body condition score (BCS), coat color, solid or patterned coat, sex and pregnancy status were recorded. Age was not considered due to the correlation with weight. Accuracy was measured as the percent of estimates within 20% (PW20) and 10% (PW10). Linear regression clustered on cat was used to evaluate whether cat posture or demographic variables were associated with the percent deviation between estimated and actual weight. Potential association between the deviation and student experience as measured by student year, previous technician experience, and experience with TNRs was evaluated using linear regression. A total of 315 cats and 647 estimates were evaluated for this study from 5 TNR clinics held at Midwestern University between May 2022 and October 2022. A veterinarian provided 142 visual weight estimates, veterinary students 310, and a veterinary technician 195. The median weight was 2.7 kg (IQR 2.0-3.5). Veterinarians had a PW20/PW10 of 54%/32%, veterinary students 43%/23%, and veterinary technicians 35%/16%. Heavier cats tended to be underestimated, while lighter cats tended to be overestimated. The percent deviation decreased by 15% (95% CI -18 to -12%) per kg from a high of 45% at 1 kg, crossing 0% at approximately 4 kg, then decreasing to -43% at 7 kg. The mean difference, which did not vary by the actual weight, was 0.5 kg (SD 0.8) for cats under 3.5 kg. At weights of 3.5 or greater, the difference decreased by -0.9 kg for each additional kg of actual weight. There were several predictors of percent deviation from actual weight. The weight of female cats tended to be overestimated by 9% (95% CI 1-16%, P = 0.020), females that were pregnant underestimated by 22% (-39 to -5%), and cats with a BCS less than 5 overestimated by 10% (95% CI 2-18%, P = 0.012). There was no significant systematic variation by cat posture, color or coat pattern. Student experience did not significantly impact the deviation in their estimates. As measured by the PW20 and PW10, the veterinarian was the most accurate at visual weight estimates, although only slightly more than half of the cats were estimated within 20%. There were predictors of percent deviation, but those predictors are often only determined after anesthesia is induced. Difference between estimated and actual weights for cats under 3.5 kg was similar, with the larger difference in percent deviation for lighter cats being due to the smaller denominator of the actual weight. Cats over 3.5 kg tended to be underestimated in weight, with both difference and deviation increasing.

Prediction of birthweight and risk of macrosomia in pregnancies complicated by diabetes

Antenatal detection of accelerated fetal growth and macrosomia in pregnancies complicated by diabetes mellitus is important for patient counseling and management. Sonographic fetal weight estimation is the most commonly used tool to predict birthweight and macrosomia. However, the predictive accuracy of sonographic fetal weight estimation for these outcomes is limited. In addition, an up-to-date sonographic fetal weight estimation is often unavailable before birth. This may result in a failure to identify macrosomia, especially in pregnancies complicated by diabetes mellitus where care providers might underestimate fetal growth rate. Therefore, there is a need for better tools to detect and alert care providers to the potential risk of accelerated fetal growth and macrosomia. This study aimed to develop and validate prediction models for birthweight and macrosomia in pregnancies complicated by diabetes mellitus. This was a completed retrospective cohort study of all patients with a singleton live birth at ≥36 weeks of gestation complicated by preexisting or gestational diabetes mellitus observed at a single tertiary center between January 2011 and May 2022. Candidate predictors included maternal age, parity, type of diabetes mellitus, information from the most recent sonographic fetal weight estimation (including estimated fetal weight, abdominal circumference z score, head circumference-to-abdomen circumference z score ratio, and amniotic fluid), fetal sex, and the interval between ultrasound examination and birth. The study outcomes were macrosomia (defined as birthweights >4000 and >4500 g), large for gestational age (defined as a birthweight >90th percentile for gestational age), and birthweight (in grams). Multivariable logistic regression models were used to estimate the probability of dichotomous outcomes, and multivariable linear regression models were used to estimate birthweight. Model discrimination and predictive accuracy were calculated. Internal validation was performed using the bootstrap resampling technique. A total of 2465 patients met the study criteria. Most patients had gestational diabetes mellitus (90%), 6% of patients had type 2 diabetes mellitus, and 4% of patients had type 1 diabetes mellitus. The overall proportions of infants with birthweights >4000 g, >4500 g, and >90th percentile for gestational age were 8%, 1%, and 12%, respectively. The most contributory predictor variables were estimated fetal weight, abdominal circumference z score, ultrasound examination to birth interval, and type of diabetes mellitus. The models for the 3 dichotomous outcomes had high discriminative accuracy (area under the curve receiver operating characteristic curve, 0.929-0.979), which was higher than that achieved with estimated fetal weight alone (area under the curve receiver operating characteristic curve, 0.880-0.931). The predictive accuracy of the models had high sensitivity (87%-100%), specificity (84%-92%), and negative predictive values (84%-92%). The predictive accuracy of the model for birthweight had low systematic and random errors (0.6% and 7.5%, respectively), which were considerably smaller than the corresponding errors achieved with estimated fetal weight alone (-5.9% and 10.8%, respectively). The proportions of estimates within 5%, 10%, and 15% of the actual birthweight were high (52.3%, 82.9%, and 94.9%, respectively). The prediction models developed in the current study were associated with greater predictive accuracy for macrosomia, large for gestational age, and birthweight than the current standard of care that includes estimated fetal weight alone. These models may assist care providers in counseling patients regarding the optimal timing and mode of delivery.

Evaluating the accuracy of sonographic fetal weight estimations using the Hadlock IV formula in a Chinese population.

Despite being the most generalized formula in China, the Hadlock IV formula has never been examined to determine if it is suitable for Chinese newborns, nor have the factors that might affect its performance been investigated. However, previous studies have reported varying results about other formulas in other nationalities. This study sought to evaluate the performance of the Hadlock IV formula in estimating fetal weight (FW) in pregnant Chinese women and use ultrasound to identify the factors affecting the accuracy of estimations of newborn weight; through these means, we aimed to create a reference for predicting neonatal weight for obstetricians. A retrospective observational study comprising data from 976 cases of live-birth singleton pregnancies at the Shanghai General Hospital was conducted. The participants' clinical data were examined and subjected to a logistic regression analysis to identify the multitude of possible factors affecting the estimation of FW. The proportions and correlations between the accurate and inaccurate estimation groups were compared to determine the different prognosis of these 2 groups. The correlations between the accuracy of the sonographic-based fetal weight estimation (SFWE) and newborns with different weight ranges were also analyzed. The overall accuracy rate of the SFWE predicted by the Hadlock IV formula was 79.61%, while that of the inaccurate estimation group was only 20.39%. The incidence of spontaneous vaginal delivery (VD) was lower in the inaccurate estimation group than in the accurate estimation group (40.7% vs. 48.13%; P=0.041). In the inaccurate estimation group, 11.56% (23/199) of the participants underwent a secondary cesarean section (sCS), compared to only 6.44% (50/777) in the accurate estimation group. The low birth weight (LBW) rates and macrosomia rates were lower in the accurate estimation group than in the inaccurate estimation group, with odds ratios (ORs) of 0.483 and 0.459, respectively (P<0.05). The results indicated that the SFWE was more accurate for newborns weighing 2,500-4,000 g than those weight out of this range. In relation to macrosomia, the SFWE was likely to be underestimated, but it was usually overestimated in the LBW group. The overall performance of the Hadlock IV formula in predicting the birth weight of Chinese newborns remains suboptimal. Extra caution should be exercised in cases of suspected large-for-gestational age (LGA) infants, small-for-gestational age (SGA) infants, infants with macrosomia, or LBW fetuses in the Chinese population.

Determination of fetal weight by ultrasonographic evaluation of fetal mid-thigh soft-tissue thickness in late third trimester

Background: Estimation of fetal weight is of utmost importance in the planning and management of uneventful labor. This study was done to compare the estimated fetal weight determined by Hadlock’s method and Scioscia’s formula (using femur length and mid-thigh soft-tissue thickness [MTSTT]) to the actual birth weight (ABW). Aims and Objectives: The objectives of this study were as follows: (1) To estimate fetal weight by Hadlock’s method and ultrasonographic evaluation of fetal MTSTT in late third trimester. (2) To correlate fetal weight derived by Hadlock’s method and ultrasonographic evaluation of fetal MTSTT with ABW. Materials and Methods: The study was conducted during the period November 2022–January 2023, at Sri Devaraj Urs Medical College, Tamaka, Kolar. Seventy-six pregnant ladies at term (between 37 and 40 weeks) were included in the study. Results: This study assessed validity of Hadlock formulae and Scioscia’s formulae for detecting fetal weight. For detection of fetal weight by Hadlock’s method, area under curve (AUC) was 0.961 and according to Scioscia’s formulae, AUC was 0.965. This study showed good agreement between fetal weight derived by Hadlock formulae and actual fetal weight (Kappa value – 0.725). There was also good agreement between fetal weight derived by Scioscia’s formulae and actual fetal weight (Kappa value – 0.745). Conclusion: The fetal weight derived by Scioscia’s formula was comparable to the actual fetal weight allowing us to rely on Scioscia’s formulae to detect fetal weight. Calculating fetal weight by measurement of fetal MTSTT serves as an additional tool for estimation of fetal weight along with the existing parameters, thereby increasing the likelihood for accurate fetal weight estimation.