Ulnar variance detection from radiographic images using deep learning

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Abstract Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. It is a critical factor in helping to diagnose wrist disorders. The typical standard classification of length difference (ulnar variance) is divided into three major types: positive ulnar variance, negative ulnar variance, and neutral ulnar variance. Conventional or manual methods of measuring ulnar variance are long and time-consuming. With the urgent need for high efficiency and high speed, achieving more accurate diagnoses has become essential. In this paper, a deep learning-based methodology is used to automatically detect ulnar variance from radiographic images. Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. Specifically, U-Net is used in the segmentation of ulna and radius bones, while DenseNets are applied to classify the type of ulnar variance. The essential contribution of this work is collecting a dataset of fully annotated wrist radiographs that are specific to this topic, which can be used as a resource to train and validate our models. Another contribution of this paper is optimizing the DenseNets model's hyperparameters to enhance its performance. Our model achieved a segmentation accuracy of 97.7% and an ulna variance classification accuracy of 92.1%. It outperformed previous deep learning-based methods in automatically segmenting the ulna and radius. This advancement not only reduces diagnosis time but also improves result reliability.

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