Veterinary Sciences

Respiratory diseases represent a leading cause of veterinary consultations in dogs and cats, yet their detection remains challenging due to clinical variability and subjective interpretation of traditional diagnostic methods. In recent years, artificial intelligence (AI) has emerged as a promising tool to augment veterinary diagnostics through automated analysis of imaging and physiological data. This systematic review synthesizes and critically evaluates 24 studies published from 2019 onward that explore AI applications to support the detection of respiratory diseases in dogs and cats, focusing on three complementary modalities: audio-based (e.g., respiratory sounds), image-based (e.g., chest radiographs), and multimodal approaches. Our findings indicate that deep learning models, particularly convolutional neural networks (CNNs) and transformer architectures, achieve clinically relevant accuracy in detecting conditions such as cardiomegaly, alveolar patterns, and Brachycephalic Obstructive Airway Syndrome (BOAS). However, significant barriers remain, including data scarcity, lack of standardized datasets, and limited real-world validation. This review highlights the transformative potential of AI in veterinary respiratory diagnostics while underscoring the need for collaborative efforts in data sharing, methodological standardization, and clinical integration to realize its full impact in practice.

Both captive and free-living reptiles are susceptible to infection by fungi from the Nannizziopsis genus and are prone to developing severe lesions that can affect skin and deeper tissues, becoming multi-systemic. With Australia having close to 1000 reptile species—of which 93% are endemic—the prevalence, distribution and susceptibility of reptiles to this emerging fungal infection remain unknown. In this case series, captive skinks originating from the states of Queensland and Western Australia were presented to wildlife veterinarians with dermatitis. Nannizziopsis pluriseptata was identified via fungal isolation, PCR (polymerase chain reaction) and sequencing, confirming the first record of this pathogen in the country.

Hepatitis E virus (HEV) is a significant public health concern and a leading cause of acute viral hepatitis worldwide. In recent years, HEV has been increasingly recognized as a wildlife-associated zoonotic pathogen, with numerous free-ranging species contributing to its maintenance and transmission. While domestic pigs remain the primary reservoir for human infection globally, growing evidence indicates that wild animals, particularly wild boars, cervids, lagomorphs, rodents, and carnivores, play a critical role in the ecology of HEV. Wild boars are the principal wildlife reservoir, with HEV seroprevalence in Europe ranging from less than 5% to more than 50%, including some of the highest levels reported in the Balkans. In addition to the frequent detection of HEV RNA in wild boar liver and muscle, viral RNA has also been identified in several other game species, most consistently in red deer, roe deer, and, in some regions, hares and wild rabbits, highlighting food safety risks associated with the consumption of raw or undercooked game meat. In regions such as the Balkans, where hunting activities and handling of wild game are widespread, these practices may further increase occupational and dietary exposure to HEV. Rodents may further complicate the epidemiological landscape through environmental contamination of water, soil, and farm surroundings, thereby facilitating indirect transmission pathways. As wildlife populations expand and human–animal interfaces intensify, understanding HEV dynamics in free-ranging species is essential for assessing zoonotic risks and implementing a strengthened One Health approach. This narrative review synthesizes and critically examines current evidence on HEV prevalence, molecular characteristics, and transmission pathways in wildlife, with particular emphasis on Europe and focused consideration of the Balkans as an epidemiologically heterogeneous and underrepresented subregion; examines associated public health implications; and highlights the importance of integrating wildlife into food safety and One Health surveillance frameworks. Existing knowledge gaps and limitations in wildlife surveillance are also discussed.