Methodological approaches in the study of pathological changes during ani-mal autopsies: a review of current practices

The article presents a systematic review of modern methodological approaches to the study of pathological changes in animal postmortems. The focus is on the integration of morphological, histological, cytological, immunohistochemical, molecular, instrumental, and digital analyses, which together provide reliable verification of pathological processes, clarification of diagnoses, and an in-depth understanding of pathogenesis. Emphasis is placed on a multidisciplinary approach that combines veterinary pathology, molecular biology, bioinformatics, and artificial intelligence (AI) technologies. A critical analysis of the literature is conducted, highlighting the effectiveness and feasibility of using combined methods in veterinary postmortem diagnostics. The morphological approach, as a fundamental diagnostic tool, acquires new value when combined with highly sensitive laboratory and imaging methods. Histological and cytological studies allow for the identification of microscopic manifestations of pathology, while immunohistochemistry enables the detection of specific protein markers associated with infectious, neoplastic, and immune processes. Molecular pathology, including PCR, FISH, and NGS, provides detailed diagnostics and facilitates the detection of etiological agents. Special attention is given to instrumental methods (electron, fluorescence, and confocal microscopy; CT; MRI; spectroscopy), which significantly enhance the informativeness of the study, particularly when integrated with digital platforms and AI systems. Modern AI tools such as PathAI, QuPath, VetCT-AI, and DeepPath demonstrate high efficiency in the automated analysis of histological sections, identification of pathological patterns, lesion visualization, and disease course prediction. The advantages of a combined approach-integrating the results of morphological, molecular, and instrumental analyses into a unified diagnostic model – are discussed, with particular emphasis on its effective implementation in multidisciplinary collaboration. These approaches contribute to building an evidence base for veterinary medicine, standardizing postmortem diagnostics, and improving pathological research. Prospective directions for further research include the expansion of multi-omics approaches, the application of deep machine learning for digital image analysis, the creation of national and international databases of pathological animal data, and the development of unified diagnostic algorithms based on digital pathology and AI.