Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.
artificial intelligence
deep learning
machine learning
medical software
sleep staging
Journal
Frontiers in artificial intelligence
ISSN: 2624-8212
Titre abrégé: Front Artif Intell
Pays: Switzerland
ID NLM: 101770551
Informations de publication
Date de publication:
2023
2023
Historique:
received:
16
08
2023
accepted:
14
11
2023
medline:
25
12
2023
pubmed:
25
12
2023
entrez:
25
12
2023
Statut:
epublish
Résumé
Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.
Identifiants
pubmed: 38145233
doi: 10.3389/frai.2023.1278593
pmc: PMC10739507
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1278593Informations de copyright
Copyright © 2023 Grassi, Daccò, Caldirola, Perna, Schruers and Defillo.
Déclaration de conflit d'intérêts
MG, SD, and GP are scientific consultants for Medibio Limited (Savage, MN, USA). AD holds the position of Chief Medical Officer at Medibio Limited (Savage, MN, USA). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.