Artificial intelligence-assisted ultrasonographic evaluation of the superficial inguinal lymph node for the diagnosis of mastitis in dairy cows

Abstract

Mastitis is one of the most significant infectious diseases affecting dairy cattle. It has a drastic impact on the animals welfare and poses serious economic losses to dairy production. In addition to examining the milk, evaluating the mammary tissue, especially the superficial inguinal (supramammary) lymph nodes, are essential for diagnosis and prognosis. This study aims to assess the effectiveness of artificial intelligence-based deep learning models in detecting mastitis from ultrasonography images of superficial inguinal lymph nodes. The study was conducted on 252 Brown Swiss cows aged 3-6 years, which were classified into three groups according to the California Mastitis Test and clinical examination: clinical mastitis (n = 84), subclinical mastitis (n = 84) and a control group (n = 84). Six pre-trained deep learning architectures were used to process B-mode ultrasonographic images: MobileNetV3, EfficientNetV2B3, Xception, InceptionV3, NasNet, InceptionResNetV2 and ConvNeXtSmall. All models performed satisfactorily, with EfficientNetV2B3 achieving the highest accuracy (96.87 %), precision (97.53 %) and F1 score (96.79 %), with a area under the curve of (100 %). These results suggest that integrating ultrasonographic and echotextural data with an AI-based model could be a valuable resource for the early detection and accurate diagnosis of mastitis, in line with the goals of precision livestock farming.