1. Investigación

Objective: To determine cutoff values for the diagnosis of atlantoaxial instability (AAI) based on cross-sectional imaging in small-breed dogs. Study design: Retrospective multicenter study. Sample population: Client-owned dogs (n = 123) and 28 cadavers. Methods: Dogs were assigned to three groups: a control group, a “potentially unstable” group, and an AAI-affected group, according to imaging findings and clinical signs. The ventral compression index (VCI), cranial translation ratio (CTR), C1-C2 overlap, C1-C2 angle, atlantoaxial distance, basion-dens interval, dens-to-axis length ratio (DALR), power ratio, and clivus canal angles were measured on CT or T2-weighted magnetic resonance (MR) images. Receiver operating characteristic (ROC) analysis was performed to define cutoff values in flexed (≥25 ) and extended (<25 ) head positions. Results: Cutoff values for the VCI of ≥0.16 in extended and ≥0.2 in flexed head positions were diagnostic for AAI (sensitivity of 100% and 100%, specificity of 94.54% and 96.67%, respectively). Cutoff values for the other measurements were defined with a lower sensitivity (75%-96%) and specificity (70%- 97%). A combination of the measurements did not increase the sensitivity and specificity compared with the VCI as single measurement. Conclusion: Cutoff values for several imaging measurements were established with good sensitivity and specificity. The VCI, defined as the ratio between the ventral and dorsal atlantodental interval, had the highest sensitivity and specificity in both head positions. Clinical significance: The use of defined cutoff values allows an objective diagnosis of AAI in small-breed dogs. The decision for surgical intervention, however, should remain based on a combination of clinical and imaging findings.

Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass − (−0.03 [intercept] − 0.29 * length2/resistance at 50 kHz + 1.07 * body mass − 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%–0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.