Radiographic femoral measurements for sex and species classification in dogs and cats using machine learning

Sex and species estimation from skeletal remains is important in veterinary forensic medicine, comparative anatomy, and zooarchaeology. Radiographic osteometry has been studied in dogs and cats, but machine-learning approaches have not been well evaluated for this purpose.

To assess the performance of machine-learning models and a multilayer perceptron for estimating sex and species from radiographic femoral measurements in dogs and cats.

This retrospective study analyzed pelvic radiographs of 280 animals (140 dogs and 140 cats; 70 males and 70 females of each species) using 9 radiographic measurements. Random forest, decision tree, logistic regression, extra trees, linear discriminant analysis, quadratic discriminant analysis, and a multilayer perceptron were evaluated. Feature importance was explored with Shapley additive explanations.

For sex classification, the extra trees classifier showed the highest accuracy in both dogs (0.79) and cats (0.75). For species classification, logistic regression, quadratic discriminant analysis, and decision tree each achieved an accuracy of 0.89, whereas the multilayer perceptron reached 0.93 after 500 and 1,000 training cycles. The most influential variables were femoral length for sex classification in cats, left intercondylar fossa width for sex classification in dogs, and inter-femoral-head distance for species classification.

Radiographic femoral measurements permit moderate sex classification and high species classification in dogs and cats. These findings support the potential use of machine-learning analysis of femoral radiographs in veterinary forensic medicine and related morphometric fields.