Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.
Bioengineering (Basel)
; 11(3)2024 Feb 22.
Article
in En
| MEDLINE
| ID: mdl-38534480
ABSTRACT
Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Bioengineering (Basel)
Year:
2024
Document type:
Article
Affiliation country:
United States
Country of publication:
Switzerland