Advances in Veterinary Imaging: From Anatomical Precision to AI Algorithms

Diagnostic imaging in veterinary medicine has evolved exponentially, driven by high-resolution modalities (radiography, ultrasound, high-field MRI, multidetector CT, contrast-enhanced ultrasound, and nuclear medicine) and the emerging integration of artificial intelligence (AI).Despite these technological breakthroughs, diagnostic accuracy, longitudinal therapeutic monitoring, and clinical decision-making remain inherently dependent on rigorous anatomical precision. Achieving high repeatability and reproducibility continues to be a major challenge due to breed variations and inter-species diversity, subjective landmark identification and operator bias, and misinterpretation of normal anatomical variants, developmental anomalies, or age-related changes.Establishing easily detectable, standardized anatomical reference points is essential to eliminate diagnostic subjectivity, validate AI algorithms, refine measurement protocols, and ensure consistent, evidence-based interpretation. Integrating clinical and imaging anatomy bridges classical anatomical knowledge with digital health, facilitating tailored treatment plans and monitoring, precise surgical planning, orthopaedic interventions, nerve blocks, and pain management across companion, exotic, and farm animals.This Research Topic aims to provide an update on the most recent advances in the integration of clinical anatomy with diagnostic imaging in the era of digital health and AI, by:• Enhancing diagnostic rigor by demonstrating how detailed anatomical knowledge improves image acquisition protocols, refines quantitative metrics, and enhances lesion localization. • Supporting AI and digital tools by providing the clear anatomical foundations required to train, validate, and refine AI diagnostic algorithms and 3D reconstructions. • Promoting standardization through the establishment of robust landmarks and nomenclature to minimize inter- and intra-operator variability across diverse animal breeds and species. • Broaden clinical impact by deepening the understanding of disease pathophysiology, assessing treatment efficacy, improving outcomes, and advancing veterinary education through digital models.This Research Topic focuses on bridging clinical anatomy and advanced veterinary imaging. We welcome: Original Research, Systematic Review, Review, Mini Review, Clinical Trial, Case Report, Data Report, Brief Research Report spanning all imaging modalities (radiography, ultrasound, CT, MRI, and nuclear medicine). Specific themes of interest include, but are not limited to:• Applied morphometry in interventional radiology, surgical planning, and orthopedic surgery.• Role of clinical anatomy in accurate nerve blocks and effective surgical intervention paths.• Identification, validation, and standardization of landmarks for reproducible measurements.• Intra- and inter-operator variability studies in quantitative imaging interpretation.• Application of detailed anatomy in training, validating, and refining AI diagnostic tools.• Cross-sectional and 3D anatomical mapping for CT/MRI interpretation, disease staging, and treatment monitoring.• Species-specific and breed-specific anatomical variations and reference ranges in diagnostic imaging.• Comparative anatomical studies across companion, production, and wildlife species.• Advances in anatomical teaching tools, 3D modeling, and digital models for veterinary imaging. Topic editors: Sofia Alves-Pimenta, Bruno Jorge Antunes Colaço, Rita Furtado Source: https://www.frontiersin.org/research-topics/85650/advances-in-veterinary-imaging-from-anatomical-precision-to-ai-algorithms