Abstract
In this paper we introduce a novel approach for diagnosis and monitoring of ulnar nerve lesions, affecting the coordination of movement of the ring and little finger of the human hand. Based on data generated by ultrasound measurements, we developed suitable preprocessing methods for automatic extraction of relevant features from the movement pattern to be examined. The partial absence of class information even for the pattern in the training set requires the use of unsupervised methods for the learning and class assignment procedures. For that reason, we use a new dynamic and hierarchic neural network for the analysis of the generated pattern vectors. The dynamically structured architecture of the network satisfies the special needs of this medical task, such as providing variable levels of generalization and efficient retrieval of similar cases.
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© 1997 Springer-Verlag Berlin Heidelberg
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Rahmel, J., Blum, C., Hahn, P., Krapohl, B. (1997). Diagnosis and monitoring of ulnar nerve lesions. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029453
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DOI: https://doi.org/10.1007/BFb0029453
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