Valeris-Chacin Robert, Garcia-Morante Beatriz, Sibila Marina, Canturri Albert, Ballarà Rodriguez Isaac, Bernal Orozco Ignacio, Jordà Casadevall Ramon, Muñoz Pedro, Pieters Maria
Veterinary Education, Research, and Outreach (VERO), Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, Canyon, TX, USA.
Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de La Universitat Autònoma de Barcelona (UAB), 08193, Bellaterra, Catalonia, Spain.
Vet Res. 2025 Jan 13;56(1):9. doi: 10.1186/s13567-024-01432-5.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods. Therefore, this study aimed to compare CVPC scores provided by a computer vision system (CVS; AI DIAGNOS) from lung images obtained at slaughter, with scores assigned by human evaluators. In addition, intra- and inter-evaluator variability were assessed and compared to intra-CVS variability. A total of 1050 dorsal view images of swine lungs were analyzed. Total lung lesion score, lesion score per lung lobe, and percentage of affected lung area were employed as outcomes for the evaluation. The CVS showed moderate accuracy (62-71%) in discriminating between non-lesioned and lesioned lung lobes in all but the diaphragmatic lobes. A low multiclass classification accuracy at the lung lobe level (24-36%) was observed. A moderate to high inter-evaluator variability was noticed depending on the lung lobe, as shown by the intraclass correlation coefficient (ICC: 0.29-0.6). The intra-evaluator variability was low and similar among the different outcomes and lung lobes, although the observed ICC slightly differed among evaluators. In contrast, the CVS scoring was identical per lobe per image. The results of this study suggest that the CVS AI DIAGNOS could be used as an alternative to the manual scoring of CVPC during slaughter inspections due to its accuracy in binary classification and its perfect consistency in the scoring.
颅腹侧肺实变（CVPC）是在屠宰猪的肺部观察到的常见病变，通常与猪肺炎支原体感染有关。有必要实施简单、快速且有效的CVPC评分方法。因此，本研究旨在比较计算机视觉系统（CVS；AI DIAGNOS）根据屠宰时获取的肺部图像给出的CVPC评分与人类评估者给出的评分。此外，还评估了评估者内部和评估者之间的变异性，并与CVS内部的变异性进行比较。总共分析了1050张猪肺的背视图图像。将全肺病变评分、每个肺叶的病变评分以及受影响肺区域的百分比作为评估结果。除膈叶外，CVS在区分无病变和有病变的肺叶方面显示出中等准确性（62 - 71%）。在肺叶水平观察到较低的多类分类准确性（24 - 36%）。如组内相关系数所示（ICC：0.29 - 0.6），根据肺叶不同，评估者之间的变异性为中度到高度。评估者内部的变异性较低，且在不同结果和肺叶之间相似，尽管观察到的ICC在评估者之间略有不同。相比之下，CVS对每张图像每个肺叶的评分是相同的。本研究结果表明，由于其在二元分类中的准确性和评分的完美一致性，CVS AI DIAGNOS可在屠宰检查期间用作CVPC手动评分的替代方法。
