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.

Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.

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X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with O(1) time complexity.

Background and Aim: Passive hip laxity (PHL) is considered the primary risk factor for canine hip dysplasia (HD) and is estimated, in stress hip radiographs, using the distraction index (DI). The study aimed to associate the early PHL using the hip Distractor of University of Trás-os-Montes and Alto Douro (DisUTAD) and the late HD grades. Materials and Methods: A total of 41 dogs (82 hips) were submitted to a follow-up study. First, between 4 and 12 months of age, dogs were radiographed using the DisUTAD hip distractor and were determined the DI for each hip joint. Then, after 12 months of age, dogs were reevaluated for HD using the conventional hip ventrodorsal projection and hips were evaluated for HD using the Fédération Cynologique Internationale (FCI) scoring system. Results: Hips of dogs' in the second examination with FCI grades of A (n=28), B (n=11), C (n=22), and D and E (n=21) had an early DI of 0.32±0.1, 0.38±0.08, 0.50±0.12, and 0.64±0.11, respectively. Statistical analysis using the general linear model univariate, with the DI as dependent variable and the FCI grades, side and sex as fixed factors, and the post hoc Bonferroni correction test showed significant differences among FCI grades (p<0.05). Conclusion: These results show the association between early DI and the late FCI HD grades and the DisUTAD is recommended for the early canine HD diagnosis.