Evaluation of the Applicability of Artificial Intelligence for the Prediction of Obstructive Sleep Apnoea

Abstract

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.