The present study retrospectively analyzed the infectious agents associated with 829 abortion outbreaks occurring from 2011 to 2021 in northern Italy. Foetuses were subjected to necropsies, and organ samples were analyzed by direct PCR to screen for six swine pathogens. In 42.0% of the examined outbreaks, at least one infectious agent was found. Porcine reproductive and respiratory syndrome virus (PRRSV) (24.9%) and porcine circovirus-2 (PCV2) (11.5%) were the most frequently detected among the known abortion-inducing pathogens. Chlamydia spp. (5.6%), porcine parvovirus (PPV) (4.0%), and Leptospira spp. (2.6%) were less common. Although its role in swine reproductive disorders is still unclear, PCV3 was detected in 19.6% of the cases. Coinfections were detected in 25.0% of positive outbreaks, and the most frequent coinfection was represented by PRRSV and PCV2 (32.2%), followed by PRRSV and PCV3 (23%). PCV2 prevalence showed a slight but consistent reduction during the study period, while PCV3 increased in frequency. Our data suggest an overall reduction in abortion outbreaks during the study period. PRRSV was confirmed as the main abortion agent detected in the examined area, while PCV2 prevalence showed a decline. Conversely, PCV3 detection has been increasing, supporting its potential role as an abortion agent. Our results highlight the importance of implementing a consistent and standardized sampling procedure, as well as a thorough diagnostic protocol, to reduce the incidence of inconclusive diagnoses.

Accurate classification of lung lesions at necropsy is crucial for guiding the diagnostic process and ensuring effective management of porcine respiratory diseases. Post-mortem inspection of the lungs during slaughter also provides valuable insights into disease occurrence, offering useful feedback on the efficacy of on-farm prevention and control strategies. However, manual assessment protocols may be impaired by high slaughtering speeds and low inter-rater agreement, which limits continuous data collection and hinders comparability. Artificial intelligence, particularly computer vision (CV), may offer a promising alternative. This study aimed to train and test a commercial CV model for segmenting both anatomical structures and lesions in pig lungs. Overall, 1742 lungs were collected at slaughter, examined macroscopically, and photographed laterally. Two veterinarians with expertise in swine pathology manually annotated the acquired images to outline anatomical (i.e., lung, heart, lung lobes), pathological (i.e., bronchopneumonia, fibrinous pleuropneumonia, chronic pleuritis), and artefactual (i.e., parenchymal laceration, bronchoinhalation of blood) classes, forming the reference dataset for model training and testing. Model performance in segmenting these classes varied by confidence threshold, with sensitivity (36-84 %), positive predictive value (62-93 %) and F1 score (52-78 %) indicating imperfect yet improvable performance. Overall, anatomical structure segmentation outperformed lesion detection, likely due to class imbalance in the training dataset and the complexity of pulmonary pathology. Integrating standardized and real-time detection of lung lesions via digital imaging could improve respiratory health surveillance, thereby enhancing the role of abattoirs as strategic epidemiological observatories.

Respiratory diseases significantly affect intensive pig finishing farms, causing production losses and increased antimicrobial use (AMU). Lesion scoring at slaughter has been recognized as a beneficial practice to evaluate herd management. The integrated analysis of abattoir lesion scores and AMU data could improve decision-making by providing feedback to veterinarians and farmers on the effectiveness of antimicrobial treatments, thus rationalizing their use. This study compared lung and pleural lesion scores collected at Italian pig slaughterhouses with on-farm AMU, estimated through a treatment index per 100 days (TI100). Overall, 24,752 pig carcasses, belonging to 236 batches from 113 finishing farms, were inspected. Bronchopneumonia and chronic pleuritis were detected in 55% and 48% of the examined pigs, respectively. Antimicrobials were administered in 97% of the farms during the six months prior to slaughter (median TI100 = 5.2), notwithstanding compliance with the mandatory withdrawal period. EMA category B (critical) antimicrobials were administered in 15.2% of cases (median TI100 = 0.06). The lung score was not associated with the total AMU, but significant, positive associations were found with the past use of critical antimicrobials (p = 0.041) and macrolides (p = 0.044). This result highlights the potential of abattoir lung lesion monitoring to rationalize antimicrobial stewardship efforts, contributing to AMU reduction.

Respiratory diseases significantly affect intensive pig farming, causing production losses and increased antimicrobial use. Accurate classification of lung lesions is crucial for effective diagnostics and disease management. The integration of non-destructive and rapid techniques would be beneficial to enhance overall efficiency in addressing these challenges. This study investigates the potential of near-infrared (NIR) spectroscopy in classifying pig lung tissues. The NIR spectra (908–1676 nm) of 101 lungs from weaned pigs were analyzed using a portable instrument and subjected to multivariate analysis. Two distinct discriminant models were developed to differentiate normal (N), congested (C), and pathological (P) lung tissues, as well as catarrhal bronchopneumonia (CBP), fibrinous pleuropneumonia (FPP), and interstitial pneumonia (IP) patterns. Overall, the model tailored for discriminating among pathological lesions demonstrated superior classification performances. Major challenges arose in categorizing C lungs, which exhibited a misclassification rate of 30% with N and P tissues, and FPP samples, with 30% incorrectly recognized as CBP samples. Conversely, IP and CBP lungs were all identified with accuracy, precision, and sensitivity higher than 90%. In conclusion, this study provides a promising proof of concept for using NIR spectroscopy to recognize and categorize pig lungs with different pathological lesions, offering prospects for efficient diagnostic strategies.

Animal welfare monitoring is essential in pig production. On-farm animal welfare (AW) assessments may provide a comprehensive overview but are resource-intensive. Abattoir-based assessments allow pigs from multiple farms to be inspected in a single facility. However, data on the relationship between these assessments remain limited, especially for heavy pigs (160–170 kg). This study investigates these associations in Italian heavy pig production. At the abattoir, 18,333 pig carcasses from 185 batches across 86 farms were scored for tail, skin (cranial and caudal) and ear lesions. On-farm AW assessments (management, structures and animal-based measures) were obtained from the national surveillance system (ClassyFarm). Tail lesion scores were higher in pigs with intact tails, whereas ear scores showed the opposite trend, suggesting a substitution effect between tail and ear biting. This indicates that tail docking is insufficient to fully prevent abnormal behaviours. Higher skin and ear scores were associated with suboptimal management, but tail scores were not, likely due to the multifactorial nature of tail biting. Herd size had no significant effect on welfare indicators. These results highlight the complexity of assessing AW and the importance of combining abattoir and farm data to obtain a more integrated monitoring system.