The aim of this review was to identify all current applications of AI in hand surgery and rehabilitation and elucidate their potential value in daily hospital routine. Although the term is nowadays often used as a synonym for machine learning, we did not want to limit this review to applications of machine learning. Throughout the literature search we encountered difficulties deciding which articles were relevant for this review. In order of covering a broad spectrum of applications which might be interesting to hand surgeons and therapists, we chose rather general search keywords. The search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) [6].
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Some limitations of this study are the small dataset size and, since only image series with corresponding CT or MRI pictures were eligible, a selection bias (mostly hard to detect or occult scaphoid fractures). To improve the specificity, they suggested further refinement of the algorithm by adding information from physical examination or training of the CNN with a larger dataset [5]. These models are trained to yield optimal predictions on a so called training dataset. DL uses artificial neural networks in which multiple network layers are added to increase the levels of abstraction and performance, so-called convolutional neural networks (CNN).
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