Artificial intelligence techniques: An efficient new approach to challenge the assessment of complex clinical fields such as airway clearance techniques in patients with cystic fibrosis?

Abstract

OBJECTIVE: 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.