Difference between revisions of "Artificial Neural Networks: Automatic Neuromuscular Diagnostic"

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 Considering the particularly encouraging results, a second version of the system was developed, aimed at differentiating within pathological cases between myopathies and neuropathies. This is currently under evaluation in a sample of patients with neuromuscular pathologies in the orofacial region.
-===Conclusions===
-After achieving a good level of training on the example case set (error = 0.03), a test was conducted on the 30 randomly selected test cases. The results were decidedly encouraging, as all new cases were correctly classified.
+'''Conclusions:''' After achieving a good level of training on the example case set (error = 0.03), a test was conducted on the 30 randomly selected test cases. The results were decidedly encouraging, as all new cases were correctly classified.
 Subsequent tests were carried out using only the electromyographic data and only the acousto-myographic data, with the observation that the network predominantly uses the information contained in the EMG signal (>90%) and that the contribution of the acoustic signal, although consistently low (<10%), complements the diagnostic accuracy. (Fig. 4).

Artificial Neural Networks: Automatic Neuromuscular Diagnostic (view source)