Article

The purpose of this study was to construct an artificial intelligence application to assist
untrained physiotherapists in determining the appropriate physiotherapy exercises
to improve the quality of life of patients with cystic fibrosis. SUBJECTS: A
total of 42 children (21 boys and 21 girls), age range 6-18 years, participated
in a clinical survey between 2001 and 2005. METHODS: Data collected during the
clinical survey were entered into a neural network in order to correlate the
health state indicators of the patients and the type of physiotherapy exercise to
be followed. Cross-validation of the network was carried out by comparing the
health state indicators achieved after following a certain physiotherapy exercise
and the health state indicators predicted by the network. RESULTS: The lifestyle
and health state indicators of the survey participants improved. The network
predicted the health state indicators of the participants with an accuracy of
93%. The results of the cross-validation test were within the error margins of
the real-life indicators. CONCLUSION: Using data on the clinical state of
individuals with cystic fibrosis, it is possible to determine the most effective
type of physiotherapy exercise for improving overall health state indicators.
Mucoviscidose

Langue : ANGLAIS