Background Due to the large number of undiagnosed obstructive sleep apnoea (OSA) patients,
our aim was to investigate the applicability of artificial intelligence (AI) in preliminary
screening, based on simple anthropometric, demographic and questionnaire parameters.
Methods Based on the results of the polysomnography performed, the 100 patients in
the study were grouped as follows: non-OSA, mild OSA and moderately severe–severe
OSA. Anthropometric measurements were performed, and the Berlin and Epworth questionnaires
were completed. Results OSA prediction based on body mass index (BMI), gender and
age was accurate in 81% of cases. With the completion of the questionnaires, accuracy
rose to 83%. The Epworth questionnaire alone yielded a correct OSA prediction in 75%,
while the Berlin questionnaire was correct in 62% of all cases. The best results for
categorization by severity were obtained by combining BMI, gender and age parameters,
together with responses to the questionnaires (71%). Supplemented with neck circumference,
this result improves slightly (73%). Conclusion Based on the results, it can be concluded
that OSA can be effectively and easily categorized using AI by combining anthropometric
and demographic parameters, as well as questionnaire data.
