Franco-Gonçalo Pedro, Moreira da Silva Diogo, Leite Pedro, Alves-Pimenta Sofia, Colaço Bruno, Ferreira Manuel, Gonçalves Lio, Filipe Vítor, McEvoy Fintan, Ginja Mário
Department of Veterinary Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal.
Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal.
Animals (Basel). 2022 Aug 26;12(17):2201. doi: 10.3390/ani12172201.
Accurate radiographic screening evaluation is essential in the genetic control of canine HD, however, the qualitative assessment of hip congruency introduces some subjectivity, leading to excessive variability in scoring. The main objective of this work was to validate a method-Hip Congruency Index (HCI)-capable of objectively measuring the relationship between the acetabulum and the femoral head and associating it with the level of congruency proposed by the Fédération Cynologique Internationale (FCI), with the aim of incorporating it into a computer vision model that classifies HD autonomously. A total of 200 dogs (400 hips) were randomly selected for the study. All radiographs were scored in five categories by an experienced examiner according to FCI criteria. Two examiners performed HCI measurements on 25 hip radiographs to study intra- and inter-examiner reliability and agreement. Additionally, each examiner measured HCI on their half of the study sample (100 dogs), and the results were compared between FCI categories. The paired t-test and the intraclass correlation coefficient (ICC) showed no evidence of a systematic bias, and there was excellent reliability between the measurements of the two examiners and examiners’ sessions. Hips that were assigned an FCI grade of A (n = 120), B (n = 157), C (n = 68), D (n = 38) and E (n = 17) had a mean HCI of 0.739 ± 0.044, 0.666 ± 0.052, 0.605 ± 0.055, 0.494 ± 0.070 and 0.374 ± 0.122, respectively (ANOVA, p < 0.01). Therefore, these results show that HCI is a parameter capable of estimating hip congruency and has the potential to enrich conventional HD scoring criteria if incorporated into an artificial intelligence algorithm competent in diagnosing HD.
准确的放射学筛查评估对于犬髋关节发育不良（HD）的遗传控制至关重要，然而，髋关节一致性的定性评估存在一定主观性，导致评分存在过度变异性。这项工作的主要目的是验证一种方法——髋关节一致性指数（HCI），该方法能够客观测量髋臼与股骨头之间的关系，并将其与国际犬业联合会（FCI）提出的一致性水平相关联，旨在将其纳入一个能够自主分类HD的计算机视觉模型中。总共随机选择了200只狗（400个髋关节）进行研究。所有X光片由一名经验丰富的检查人员根据FCI标准分为五类进行评分。两名检查人员对25张髋关节X光片进行了HCI测量，以研究检查人员内部和检查人员之间的可靠性及一致性。此外，每位检查人员对其研究样本的一半（100只狗）进行了HCI测量，并比较了FCI类别之间的结果。配对t检验和组内相关系数（ICC）均未显示出系统偏差的证据，两名检查人员的测量结果以及检查人员不同测量环节之间具有出色的可靠性。被评定为FCI A级（n = 120）、B级（n = 157）、C级（n = 68）、D级（n = 38）和E级（n = 17）的髋关节的平均HCI分别为0.739±0.044、0.666±0.052、0.605±0.055、0.494±0.070和0.374±0.122（方差分析，p < 0.01）。因此，这些结果表明HCI是一个能够估计髋关节一致性的参数，如果将其纳入能够诊断HD的人工智能算法中，有可能丰富传统的HD评分标准。
