Deep learning‐based diagnosis of stifle joint diseases in dogs

Shim, Hyesoo; Lee, Jongmo; Choi, Sun-Hye; Jeong, Jeongyun; Cho, Changhyun; Kim, Hyung Seok; Kim, Jee‐in; Kim, Hyoju; Eom, Ki-Dong

doi:10.1111/vru.13181

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Figure 5 shows a feature-based heat map provided by the gradient-weighted class activation mapping (Grad-CAM) 11 . The area highlighted in the Grad-Cam image is the medial patella.…”

Section: Resultsmentioning

confidence: 99%

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Detecting medial patellar luxation with ensemble deep convolutional neural network based on a single rear view image of the hindlimb

Jung,

Park,

Kim

2023

Sci Rep

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Medial patellar luxation (MPL) is a common orthopedic disease in dogs, which predisposes elderly and small-breed dogs. Unlike in humans, diagnosis in the early course of the disease is challenging because symptoms and joint-pain expression in canines are vague. Herein, we introduced a deep-learning system to diagnose MPL using a single rear-view hindlimb image. We believe that this is the first attempt to build a deep-learning system to diagnose MPL based on image analysis. Notably, 7689 images were collected from 2653 dogs in 30 private animal clinics between July 2021 and July 2022. Model performance was compared with ResNet50, VGG16, VGG19, Inception-V3, and veterinarian performance. For performance comparison, a professional veterinarian with > 10 years of experience selected images of 25 normal dogs and 25 dogs with MPL. The proposed model showed the highest performance, with 92.5% accuracy, whereas human experts showed an average accuracy of 55.2%. Therefore, our model can diagnose MPL using only a single rear-view hindlimb image. Furthermore, to solve the image uncertainty caused by the input image noise, we used a one-class SVM and ensemble learning methods to ensure model robustness. Our study will help diagnose MPL in clinical settings using a single rear-view hindlimb image.

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“…Figure 5 shows a feature-based heat map provided by the gradient-weighted class activation mapping (Grad-CAM) 11 . The area highlighted in the Grad-Cam image is the medial patella.…”

Section: Resultsmentioning

confidence: 99%

“…With advances in technology, canine PL has been successfully diagnosed using a region with convolutional neural networks (R-CNN) deep learning model and 2832 radiographic images of the canine stifle joint 11 . However, this model requires radiographic images taken by veterinarians, which can only be obtained in veterinary clinics.…”

Section: Introductionmentioning

confidence: 99%

Detecting medial patellar luxation with ensemble deep convolutional neural network based on a single rear view image of the hindlimb

Jung,

Park,

Kim

2023

Sci Rep

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Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations

Burti,

Banzato,

Coghlan

et al. 2024

Research in Veterinary Science

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Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning

Suksangvoravong,

Choisunirachon,

Tongloy

et al. 2024

Vet Radiology Ultrasound

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Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting‐edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you‐only‐look‐once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in‐house XXX platform. The results showed that the YOLO v4 tiny‐416 model had satisfactory performance in screening among the normal trachea, grade 1–2 tracheal collapse, and grade 3–4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision–recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P < .001), with all observers having excellent agreement (κ = 1.00, P < .001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs.

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Deep learning‐based diagnosis of stifle joint diseases in dogs

Cited by 3 publications

References 24 publications

Detecting medial patellar luxation with ensemble deep convolutional neural network based on a single rear view image of the hindlimb

Detecting medial patellar luxation with ensemble deep convolutional neural network based on a single rear view image of the hindlimb

Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations

Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning

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