Evidence-based review of transducer selection for lung ultrasound in dogs and cats with pulmonary pathology

Alveolar–interstitial syndrome (AIS) is a common ultrasonographic finding in dogs and cats with pulmonary pathology, including cardiogenic pulmonary edema, noncardiogenic pulmonary edema, pneumonia, acute respiratory distress syndrome, and pulmonary contusion. Point-of-care lung ultrasound (LUS) has emerged as a rapid, noninvasive, and repeatable diagnostic tool for identifying AIS in emergency and critical care patients. Despite its increasing clinical adoption, transducer selection remains largely operator-dependent, with limited evidence-based guidance for veterinary applications. This review synthesizes current veterinary and human literature to evaluate how transducer type influences the detection and characterization of AIS in small animals. The acoustic properties, field of view, penetration depth, and resolution trade-offs of linear, curvilinear, and microconvex transducers are discussed in relation to B-line visualization, pleural line assessment, and detection of subpleural consolidations. Comparative performance data, including the results of recent veterinary studies, are integrated to highlight differences in diagnostic sensitivity, specificity, and interobserver reliability. A clinical decision-making framework is proposed, outlining optimal transducer selection by patient size, suspected pathology, and clinical context (triage, monitoring, follow-up). Practical considerations such as transducer availability, equipment ergonomics, and operator training are addressed. Knowledge gaps and future research priorities, including the role of portable devices and AI-assisted image interpretation, are identified. By consolidating the available evidence, this review aims to standardize and optimize transducer selection in veterinary lung ultrasound, improving diagnostic accuracy, accelerating clinical decision-making, and ultimately enhancing patient outcomes in small animal emergency and critical care.