Abstract
Fractures of the proximal femur represent a critical entity in the western world, particularly with the growing elderly population. Such fractures result in high morbidity and mortality, reflecting a significant health and economic impact on our society. Different treatment strategies are recommended for different fracture types, with surgical treatment still being the gold standard in most of the cases. The success of the treatment and prognosis after surgery strongly depends on an accurate classification of the fracture among standard types, such as those defined by the AO system. However, the classification of fracture types based on x-ray images is difficult as confirmed by low intra- and inter-expert agreement rates of our in-house study and also in the previous literature. The presented work proposes a fully automatic computer-aided diagnosis (CAD) tool, based on current deep learning techniques, able to identify, localize and finally classify proximal femur fractures on x-rays images according to the AO classification. Results of our experimental evaluation show that the performance achieved by the proposed CAD tool is comparable to the average expert for the classification of x-ray images into types ‘‘A’’, ‘‘B’’ and ‘‘normal’’ (precision of 89%), while the performance is even superior when classifying fractures versus ‘‘normal’’ cases (precision of 94%). In addition, the integration of the proposed CAD tool into daily clinical routine is extensively discussed, towards improving the interface between humans and AI-powered machines in supporting medical decisions.
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URL
http://arxiv.org/abs/1902.01338