Role of Artificial Intelligence in Enhancing Canine Hip Dysplasia Diagnosis

Advancements in artificial intelligence (AI), particularly deep learning (DL), have transformed medical imaging across disciplines. In veterinary orthopedics, these technologies are increasingly being applied to improve the accuracy, efficiency, and consistency of Canine Hip Dysplasia (CHD) diagnosis. By integrating automated systems with established radiographic metrics, AI offers a promising pathway toward more objective and reliable assessments. 

Evolution of AI in CHD Imaging 

Early computer-aided detection (CAD) systems faced limitations in precision and reliability. However, the introduction of deep learning algorithms has significantly improved performance, achieving near-human accuracy in image analysis tasks². 

Initial models focused on basic classification tasks, such as distinguishing hip radiographs from non-hip images, with moderate error rates. Subsequent developments incorporated convolutional neural networks (CNNs) to identify regions of interest and classify hips as normal or dysplastic, although sensitivity remained limited in early implementations¹. 

Performance of Advanced Deep Learning Models 

More recent models have demonstrated substantial improvements. For instance, the EfficientNet-based model achieved an area under the curve (AUC) of 0.964 and classification accuracy of 89.1%, highlighting its strong diagnostic capability. Similarly, 3D CNN approaches using magnetic resonance imaging achieved accuracy rates approaching 89.7%, emphasizing the potential of volumetric analysis¹. 

Despite these advancements, challenges remain, particularly regarding model interpretability. The “black-box” nature of deep learning systems can limit clinical trust, as the reasoning behind predictions is often unclear³. 

Automated Measurement of Objective Metrics 

To address these concerns, recent research has focused on automating the measurement of objective radiographic parameters. Metrics such as the Hip Congruency Index (HCI) and Femoral Neck Thickness Index (FNTi) have been successfully quantified using DL-based segmentation models^3,4,5,6. 

These systems utilize advanced architectures, including U-Net and YOLO networks, to accurately delineate anatomical structures and compute relevant indices. High segmentation accuracy, with Dice scores up to 0.98, demonstrates strong agreement with expert evaluations [22]. Similarly, automated FNTi measurements showed excellent reliability, with significant correlation to CHD severity^5,6. 

Development and Validation of the FHC/DAE Automated System 

Building on these advancements, the automated FHC/DAE measurement system represents a significant step toward improving diagnostic transparency. Unlike classification-focused models, this system provides quantifiable measurements, enhancing interpretability. 

The system demonstrated strong agreement with expert examiners, with kappa values indicating almost perfect concordance and high inter-rater reliability (ICC = 0.97)¹. Additionally, it maintained consistent performance across both common and less frequent classification categories, supporting its clinical applicability. 

Clinical Efficiency and Workflow Optimization 

One of the most notable advantages of AI integration is the significant reduction in processing time. The automated system can analyze radiographs in under 0.5 seconds, compared to 1–1.5 minutes required for manual assessment. This improvement enhances workflow efficiency, particularly in high-volume screening scenarios¹. 

The Role of AI in Clinical Practice 

Importantly, AI systems are not intended to replace clinical expertise. While they provide valuable support through consistent and objective analysis, they cannot replicate the nuanced judgment and contextual understanding of experienced clinicians. 

Instead, these technologies should be viewed as decision-support tools that enhance, rather than replace, professional evaluation. Integrated platforms combining multiple metrics may further improve diagnostic accuracy and reliability. 

Conclusion 

The integration of AI into CHD diagnosis marks a significant advancement in veterinary imaging. By automating complex measurements and improving diagnostic consistency, these systems offer substantial clinical benefits. However, their optimal use lies in complementing expert judgment, ensuring that technological innovation enhances, rather than replaces, clinical care. 

References: 

1. Franco-Gonçalo P, Leite P, Alves-Pimenta S, Colaço B, Gonçalves L, Filipe V, McEvoy F, Ferreira M, Ginja M. A Computer-Aided Approach to Canine Hip Dysplasia Assessment: Measuring Femoral Head–Acetabulum Distance with Deep Learning. Applied Sciences. 2025 May 3;15(9):5087. https://www.mdpi.com/2076-3417/15/9/5087 

2. Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N. Deep learning in medical imaging. Neurospine. 2019 Dec 31;16(4):657. https://pmc.ncbi.nlm.nih.gov/articles/PMC6945006/pdf/ns-1938396-198.pdf 

3. Buhrmester V, Münch D, Arens M. Analysis of explainers of black box deep neural networks for computer vision: A survey. Machine Learning and Knowledge Extraction. 2021 Dec 8;3(4):966-89. https://www.mdpi.com/2504-4990/3/4/48 

4. Franco-Gonçalo P, Moreira da Silva D, Leite P, Alves-Pimenta S, Colaço B, Ferreira M, Gonçalves L, Filipe V, McEvoy F, Ginja M. Acetabular coverage area occupied by the femoral head as an Indicator of hip congruency. Animals. 2022 Aug 26;12(17):2201. https://www.mdpi.com/2076-2615/12/17/2201 

5. Franco-Gonçalo P, Pereira AI, Loureiro C, Alves-Pimenta S, Filipe V, Gonçalves L, Colaço B, Leite P, McEvoy F, Ginja M. Femoral neck thickness index as an indicator of proximal femur bone modeling. Veterinary Sciences. 2023 May 24;10(6):371. https://www.mdpi.com/2306-7381/10/6/371 

6. Loureiro C, Gonçalves L, Leite P, Franco-Gonçalo P, Pereira AI, Colaço B, Alves-Pimenta S, McEvoy F, Ginja M, Filipe V. Deep learning-based automated assessment of canine hip dysplasia. Multimedia Tools and Applications. 2025 Jun;84(19):21571-87. https://link.springer.com/content/pdf/10.1007/s11042-024-20072-7.pdf