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This research was funded by Laboratorios HIPRA. The funder's role in the design of the study and collection, analysis, interpretation of data, and writing of the manuscript was outlined in the author contributions section (IBR, IBO, RJC, and PM).

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Scoring of swine lung images: a comparison between a computer vision system and human evaluators

Publicado en:Veterinary Research. 56 (1): 9- - 2025-01-13 56(1), DOI: 10.1186/s13567-024-01432-5

Autores: Valeris-Chacin, Robert; Garcia-Morante, Beatriz; Sibila, Marina; Canturri, Albert; Ballara Rodriguez, Isaac; Bernal Orozco, Ignacio; Jorda Casadevall, Ramon; Munoz, Pedro; Pieters, Maria

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Resumen

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.

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