Dual-energy X-ray absorptiometry is a reliable non-invasive technique for determining whole body composition of chickens

Linear and nonlinear models for assessing carcass composition using dual X-ray absorptiometry in egg- and meat-type chickens

The objective of this study was to develop appropriate correction equations for dual energy X-ray absorptiometry (DXA) for total carcass composition of live meat- and egg-type chickens. Linear (bivariate linear and multivariate linear) and nonlinear (polynomial, multivariate polynomial, broken-line and Gompertz) equations were used to estimate carcass composition of DXA-scanned birds based on chemical proximate analysis. A total of 288 laying females (10-30 wk of age) and 305 broiler breeder females (4-32 wk of age) were used. The same birds scanned by DXA were dissected and utilized for whole-body proximate chemical analysis for body lean, fat, and mineral content (ash). As indicators of carcass fat and lean, abdominal fat pad and breast muscle weights were also recorded. Models were evaluated using root mean square error (RMSE), Bayesian Information Criterion (BIC), coefficient of determination (R2), Durbin Watson test for autocorrelation (DW), and residuals observation (RES). Model estimations were done separately by strain or combined. Estimations of composition responses fit at least 1 of each linear and nonlinear models for the egg- and meat-type chickens on all parameters estimated (P < 0.05). In the egg-type chickens, multivariate linear regression was the best fit for body lean with the lowest RMSE and BIC, and highest R2 whereas body fat, body ash, and breast muscle were best predicted by the multivariate polynomial model. In the meat-type chickens, body lean was best predicted by the multivariate linear model with the lowest RMSE and BIC, and the highest R2 whereas the multivariate polynomial was the most parsimonious model for body fat, body ash, and abdominal fat. Positive autocorrelations were observed in several models tested for body fat, body ash, breast muscle, and abdominal fat pad when both strains were analyzed combined (P < 0.05). In summary, a strain-based correction is recommended to all the parameters, with exception of the BW estimation. Correction equations developed in this study demonstrated that the DXA technique is a reliable alternative to proximate chemical analysis in egg- and meat-type chickens.

Potential non-invasive detection of lesions in broiler femur heads: application of the DXA imaging system

Leg health is a significant economic and welfare concern for the poultry industry. Current methods of detection rely on visual assessment of the legs and gait scores and bone scoring during necropsy for full characterization. Additionally, the current scoring of femurs only examines the external surface of the femoral head. Through the use of the dual-energy X-ray absorptiometry (DXA) imaging system, we show the presence of a necrotic region in the femurs that would otherwise be considered healthy based on the current evaluation procedures. Importantly, these lesions were present in almost 60% (22 of 37) of femurs that scored normal for femoral head necrosis (FHN). Additionally, these femurs showed greater bone mineral content (BMC) relative to weight compared to their counterparts with no lucent lesions (6.95% ± 0.20% vs. 6.26% ± 0.25; p = 0.038). Identification of these lesions presents both a challenge and an opportunity. These subclinical lesions are likely to be missed in routine scoring procedures for FHN and can inadvertently impact the characterization of the disease and genetic selection programs. Furthermore, this imaging system can be used for in vivo, ex vivo, and embryonic (egg) studies and, therefore, constitutes a potential non-invasive method for early detection of bone lesions in chickens and other avian species.

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning

Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

Dual-energy X-ray absorptiometry: an effective approach for predicting broiler chicken body composition

Two trials were carried out to develop and validate linear regression equations for body composition prediction using Dual-energy X-ray absorptiometry (DEXA). In Trial 1, 300 Cobb500 male chickens raised from 1 to 42 d of age were scanned in DEXA to estimate total weight, fat mass, soft lean tissue (SLT) mass, bone mineral content (BMC), and fat percentage. DEXA estimates were compared to body ash, crude fat, SLT (sum of protein and water) and scale body weight. The dataset was split, with 70% used for prediction equations development and 30% for testing, and the 5k-fold cross-validation analysis was used to optimize the equations. The R2, mean absolute error (MAE), and root-mean-squared error (RMSE) were used as precision and accuracy indicators. A negative correlation (ρ = -0.27) was observed for ash content, while no correlation was observed for protein content (P > 0.05). Predictive linear equations were developed to assess broiler weight (R2 = 0.999, MAE = 25.12, RMSE = 38.99), fat mass (R2 = 0.981, MAE = 13.87, RMSE = 21.28), ash mass (R2 = 0.956, MAE = 3.98, RMSE = 5.61), SLT mass (R2 = 0.997, MAE = 35.73, RMSE = 52.45), water mass (R2 = 0.997, MAE = 29.56, RMSE = 43.94), protein mass (R2 = 0.989, MAE = 12.94, RMSE = 19.05), fat content (R2 = 0.855, MAE = 0.81, RMSE = 1.05), SLT content (R2 = 0.658, MAE = 1.01, RMSE = 1.28), and water content (R2 = 0.678, MAE = 0.99, RMSE = 1.27). All equations passed the test. In Trial 2, 395 Cobb500 male chickens were raised from 1 to 42 d of age and used for validation of prediction equations. The equations developed for weight, fat mass, ash mass, SLT mass, water mass, and protein mass were validated. In conclusion, DEXA was found to be an effective approach for measuring the body composition of broilers when using predictive equations validated in this study for estimate calibration.

Dietary calcium and nonphosphate phosphorus interaction influences tibiotarsus development and related gene expression of broilers from 1 to 21 days of age

The dietary needs of calcium (Ca) and phosphorus (P) are interdependent, thus accurate evaluation of Ca and P requirements of broilers to support skeleton health and optimal growth is critical. The present study was carried out to investigate the effects of dietary Ca and nonphytate P (NPP) levels and their interactions on growth performance, tibiotarsus characteristics, tibiotarsus metabolism-related enzyme and proteins, and their gene expression of broilers, so as to provide a rational recommendation for Ca and NPP levels in diet. A total of 540 one-day-old Arbor Acres male broilers were randomly allotted to 1 of 15 treatments with 6 replicate cages of 6 birds per cage for each treatment in a completely randomized design involving a 5 × 3 factorial arrangement of treatments (5 levels of Ca × 3 levels of NPP). The birds were fed the corn-soybean meal diet containing 0.60%, 0.70%, 0.80%, 0.90%, or 1.00% Ca and 0.35%, 0.40%, or 0.45% NPP for 21 d. Dietary Ca level affected (P < 0.03) the bone mineral density, bone mineral content (BMC), breaking strength, ash percentage and ash Ca contents in tibia, which showed linear (P < 0.006) responses to dietary Ca levels. Dietary NPP level affected (P < 0.05) tibia BMC, ash percentage, and FGF23 mRNA level. Broilers that received 0.40% and 0.45% NPP had higher (P < 0.04) tibia BMC and ash percentage than those that received 0.35% NPP, but no differences (P > 0.05) were found between 0.40% and 0.45% NPP. Broilers that received 0.40% NPP had higher (P = 0.02) tibia FGF23 mRNA level than those that received 0.35% NPP, but no differences (P > 0.05) were detected between 0.40% and 0.45% NPP or 0.45% and 0.35% NPP. The interactions between dietary Ca and NPP affected (P < 0.05) ADG, ALP activity, bone gal protein, FGF23 contents, and the mRNA expression levels ALP and bone gal protein in tibia of broilers. Results from the present study indicate that dietary Ca and NPP interaction influences growth, tibiotarsus development, and related gene expression of broiler chickens. Considering all the criteria, the dietary levels of 0.90% Ca and 0.45% NPP would be optimal for both growth and tibiotarsus development of broilers fed a conventional corn-soybean meal diet from 1 to 21 d of age.

Assessing dual-energy X-ray absorptiometry prediction of intramuscular fat content in beef longissimus steaks

This study assessed the capability of dual-energy X-ray absorptiometry (DEXA) to predict intramuscular fat (IMF) content of beef longissimus steaks against chemical IMF as the gold standard. DEXA performance of fat% prediction was assessed using a leave-one-out cross validation method among Angus and Nellore steaks, which generated a chemical fat% range of 14.05-36.82% and 2.46-7.84%, respectively, and using pooled data. There was a significant positive association between DEXA predicted fat and chemical fat content. However, higher precision was found for pooled data (R² = 0.95, RMSECV = 1.95) and Angus (R² = 0.75, RMSECV = 2.39) than Nellore (R² = 0.15, RMSECV = 1.22) group. Accuracy also had the same response with average slope values close to 1 for pooled data and Angus and a lower value (0.42) for Nellore group. DEXA precisely predicts IMF content across a wide range of fat content. However, its precision and accuracy of prediction within low-fat content samples are lower than in high-fat content.

Cross-calibration of two dual-energy X-ray absorptiometry devices for the measurement of body composition in young children

This study aimed to cross-calibrate body composition measures from the GE Lunar Prodigy and GE Lunar iDXA in a cohort of young children. 28 children (mean age 3.4 years) were measured on the iDXA followed by the Prodigy. Prodigy scans were subsequently reanalysed using enCORE v17 enhanced analysis ("Prodigy enhanced"). Body composition parameters were compared across three evaluation methods (Prodigy, Prodigy enhanced, iDXA), and adjustment equations were developed. There were differences in the three evaluation methods for all body composition parameters. Body fat percentage (%BF) from the iDXA was approximately 1.5-fold greater than the Prodigy, whereas bone mineral density (BMD) was approximately 20% lower. Reanalysis of Prodigy scans with enhanced software attenuated these differences (%BF: - 5.2% [95% CI - 3.5, - 6.8]; and BMD: 1.0% [95% CI 0.0, 1.9]), although significant differences remained for all parameters except total body less head (TBLH) total mass and TBLH BMD, and some regional estimates. There were large differences between the Prodigy and iDXA, with these differences related both to scan resolution and software. Reanalysis of Prodigy scans with enhanced analysis resulted in body composition values much closer to those obtained on the iDXA, although differences remained. As manufacturers update models and software, researchers and clinicians need to be aware of the impact this may have on the longitudinal assessment of body composition, as results may not be comparable across devices and software versions.

Interactive effects of dietary amino acid density and environmental temperature on growth performance and expression of selected amino acid transporters, water channels, and stress-related transcripts

Exposure to heat stress (HS) is one of the challenges facing the broiler industry worldwide. Various nutritional strategies have been suggested, such as altering dietary concentrations of some nutrients. Thus, we evaluated feeding different amino acid (AA) densities on live performance, Pectoralis (P.) muscles, and expression of selected AA transporters, water channels, and stress-related transcripts in a fast-growing broiler strain. Ross 308 chicks (n = 576) were randomly assigned to 4 dietary treatments (24 reps, 6 chicks per rep), differing in AA density (110, 100, 90, and 80% of a breeder's AA specifications). During 24 to 36 days of age, half of the birds were kept at a thermoneutral (TN) temperature of 20°C, whereas the other half were subjected to HS at 32° C for 8 h daily, making the treatment design a 4 × 2. The results revealed no interaction between housing temperature and AA density on growth performance or P. muscles weights. Feeding 80% AAs depressed BWG, FCR, and P. muscles at 36 d (P < 0.001). There was an interaction (P < 0.001) between AA density and temperature on the expression of all examined genes. Reducing the AA density beyond 100% upregulated the expression of AA transporter (CAT1, B⁰AT, b^0,+AT, SNAT1, LAT1), HSP70, HSP90, glucocorticoid receptor (GR), and AQP3 in the TN birds’ jejunum. Whereas in the HS birds, inconsistent expressions were observed in the jejunum, of which CAT1, B⁰AT, and LAT1 were markedly downregulated as AA density was reduced. In P. major of TN birds, reducing AA density resulted in upregulating the expression of all AA transporters, HSP70, GR, and AQP1, while downregulating HSP90 and AQP9. In contrast, AA reduction markedly downregulated CAT1, B⁰AT, and LAT1 in the P. major of HS birds. These findings indicate that the dietary AA level alters the expression of various genes involved in AA uptake, protein folding, and water transport. The magnitude of alteration is also dependent on the housing temperature. Furthermore, the results highlight the importance of adequate AA nutrition for fast-growing chickens under HS.

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

This study determined the potential of computer vision systems, namely the whole-side carcass camera (HCC) compared to the rib-eye camera (CCC) and dual energy X-ray absorptiometry (DXA) technology to predict primal and carcass composition of cull cows. The predictability (R²) of the HCC was similar to the CCC for total fat, but higher for lean (24.0%) and bone (61.6%). Subcutaneous fat (SQ), body cavity fat, and retail cut yield (RCY) estimations showed a difference of 6.2% between both CVS. The total lean meat yield (LMY) estimate was 22.4% better for CCC than for HCC. The combination of HCC and CCC resulted in a similar prediction of total fat, SQ, and intermuscular fat, and improved predictions of total lean and bone compared to HCC/CCC. Furthermore, a 25.3% improvement was observed for LMY and RCY estimations. DXA predictions showed improvements in R² values of 26.0% and 25.6% compared to the HCC alone or the HCC + CCC combined, respectively. These results suggest the feasibility of using HCC for predicting primal and carcass composition. This is an important finding for slaughter systems, such as those used for mature cattle in North America that do not routinely knife rib carcasses, which prevents the use of CCC.