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Background: Systems for estimating body condition score (BCS) are currently used in canine practice to monitor fatness levels. These tools are cheap and easy to use but lack the necessary precision to monitor small changes in body fat, particularly during weight control treatments or in research. The present work aims to study the application of real-time ultrasonography (RTU) together with image analysis in the assessment of subcutaneous fat depots in dogs. Ultrasound images were collected from five anatomical locations (chest, flank, abdomen, thigh and lumbar) from 28 healthy dogs of different breeds and with a body weight (BW) ranging from 5.2 to 33.0 kg. BCS was collected by visual appraisal using a 5-point scale. Subcutaneous fat thickness (SFT) was estimated from RTU images, using the average of three measurements taken in fat deposits located above the muscles represented in each image. Correlations were established between SFT and BW or BCS as well as a classification of BCS-based fatness [overweight (BCS = 4), ideal (BCS = 3) and lean (BCS = 2)]. Results: SFT was found to differ between the five regions considered (P < 0.001). Abdomen and thigh were the areas displaying the widest variation for the different dogs included in the study and also those correlating most with BW, in contrast to the chest, which showed the least variation. Overall, a strong correlation was found between BCS and SFT. The highest correlations were established for the flank, abdomen and lumbar areas. In every anatomical area, a decrease in SFT was observed across all three BCS classes, ranging from 48 to 65% among overweight and ideal dogs, and from 46 to 83% among ideal and lean dogs. Conclusions: Preliminary data showed that within this population there was a strong correlation between BCS and SFT estimated from RTU images. It was also observed that RTU measurements for fat thickness differed among the anatomical points surveyed suggesting differences in their sensitivity to a change in BCS. The images displaying the best prediction value for fatness variations were those collected at the lumbar and abdomen areas. [ABSTRACT FROM AUTHOR]

Background: In equids, health and welfare depend on body composition. A growing number of equids are now used as leisure and companion animals, and often found overfeed. The need for a close monitoring of body fatness led to the search for tools allowing a rapid and non-invasive estimation of fatness. This study intends to assess realtime ultrasonography (RTU) usefulness in establishing a relationship between ultrasound measures of subcutaneous fat-plus-skin thickness (SF-Skin) and body condition score (BCS) in horses and donkeys. Forty-three healthy animals (16 donkeys and 27 horses) were used in this study to generate 95 records (RTU and BCS pairs), in multiple RTU sessions for 2 years. Using visual appraisal and palpation, BCS was graded in a 1-9 points scale. Real-time ultrasonography images were taken using a 7.5 MHz linear transducer, placed perpendicular to the backbone, over the 3rd lumbar vertebra. ImageJ was used to measure the SF-Skin on RTU images. The relation between BCS and SF-Skin measurements was tested by linear and polynomial regression analysis. Results: The BCS values were similar in horses (5.50; from 3 to 8 points) and donkeys (5.14; from 3 to 7 points). The SF-Skin measures show a similar trend (a mean of 7.1 and 7.7 mm in horses and donkeys, respectively). A polynomial regression among BCS and SF-Skin explained 92 and 77% of the variation in donkeys and horses respectively. The coefficient of determination was considerably higher for the regression developed for donkeys compared with that of horses (R2 = 0.92 vs. 0.77, respectively), which reduced the accuracy of the method in horses. Both the linear and polynomial models tested show a strong relationship among BCS and SF-Skin for donkeys (R2 > 0.91; P < 0.01) and horses (R2 > 0.74; P < 0.01), despite that the extremes for BCS did not existed in our sample. Conclusions: Our results showed the potential RTU usefulness to monitor body fat in equids. Using a high-frequency transducer and RTU together with image analysis allowed the identification of small SF-skin variations. This report will support further studies on the relationships between SF-Skin and BCS, particularly in extreme BCS scores. [ABSTRACT FROM AUTHOR]

Background: Obesity and emaciation in horses have major detrimental effects on health and morbidity, reproductive failure, work performance or carcass quality. Scoring is a current management tool used to assess and monitor equine body condition due to its simplicity and low cost. However, accurate assessment of obesity remains a challenge, even though a number of approaches have been tested, particularly for research purposes on adiposity. Their merit is usually validated by comparison with standard scoring methods. The overall aim of this study was to establish the correlation between post-mortem nape fat measurements obtained after photographic image analysis and cresty neck score (CNS) in horses. Data were collected from seventeen horses with a hot carcass weight of 165 ± 51 kg. Pre-slaughter CNS measurements were obtained using a six-point scale (from 0 to 5). Image capture was performed post-mortem, in the slaughter line; for each carcass, images of the dorsal and medial views were collected and afterwards transferred to a computer for analysis. After outlining the cresty neck fat, its area, major axis and thickness were determined. Correlation coefficients between nape fat measurements, CNS and carcass fatness were determined. Results: The horses in the study show similar variation for CNS and hot carcass weight [Coefficient of variation (CV) = 32 and 31%, respectively], but a high variation for carcass fattening (CV = 41%). The nape fat area measurement was the parameter exhibiting the greatest variation (CV = 50%). Correlations established between CNS and the variables tested revealed the existence of moderate to strong correlations among CNS, nape fat measurements, and carcass fatness. The highest correlation coefficients were found between CNS and nape fat thickness (r = 0.882; P < 0.01). The linear regression between CNS and nape fat thickness accounted for 77% of the recorded variation for nape fat thickness. Conclusions: The present study showed that there is a strong correlation between horse CNS and post-mortem nape fat measurements or carcass fatness. [ABSTRACT FROM AUTHOR]