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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;\u00a0AI 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\u201371%) 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\u201336%) 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\u20130.6). 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. 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