Relevance of sleep for wellness: New trends in using artificial intelligence and machine learning

Table 1 Use of technology for diagnosis of sleep disorders[23]

Type of sensor	Number of studies	Percentage	Sample size	AUC1	Evidence
Bed/mattress sensors	11	27%	10-366	AUC: 0.94-1.00	Strong
Wearables	10	24%	20-404	AUC: 0.80-1.00	Strong
Smartphones	7	17%	15-620	AUC: 0.61-0.95	Moderate to strong
Nasal airflow sensors	5	12%	5-288	AUC: 0.77-0.91	Moderate to strong
Other digital tools (could not be classified)	8	20%	10-359	AUC: 0.85-1.00	Strong

¹Area under curve measures discrimination power of the predictive classification model. AUC: Area under curve.