Auto-Detection of Tibial Plateau Angle in Canine Radiographs Using a Deep Learning Approach
This work addresses a domain-specific veterinary diagnostic challenge by automating TPA measurement, potentially reducing diagnosis time for canine lameness.
The paper tackled the problem of diagnosing stifle joint issues in dogs by developing a deep learning method to automatically detect the Tibial Plateau Angle (TPA) from radiographs, achieving successful prediction within the normal range for 80% of images.
Stifle joint issues are a major cause of lameness in dogs and it can be a significant marker for various forms of diseases or injuries. A known Tibial Plateau Angle (TPA) helps in the reduction of the diagnosis time of the cause. With the state of the art object detection algorithm YOLO, and its variants, this paper delves into identifying joints, their centroids and other regions of interest to draw multiple line axes and finally calculating the TPA. The methods investigated predicts successfully the TPA within the normal range for 80 percent of the images.