16 studies presented similar methods of automated bone age assessment from hand radiographs using neural networks. These CNNs are usually trained on large datasets and show very good performances with a discrepancy of only a couple of months compared to manual bone age assessment [30]. For example, Spamimpato et al. presented an algorithm with an average discrepancy between manual and automated age assessment of 0.8 years, which was considered to be a state-of-the-art performance [31]. Papez et al. published an article on infrared thermography based on artificial intelligence for diagnosis of Carpal Tunnel Syndrome diagnosis. The authors concluded that the method cannot be recommended as an adequate diagnostic tool for exact severity quantification, but could be useful as a screening tool for severe cases [24]. Table 3 gives an overview over publications on automated fracture detection in the hand and wrist and the reported performance metrics.
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