Three-class obstructive sleep apnea severity assessment: a parallel AHI and ODI explainable artificial intelligence framework using craniofacial-enriched clinical data.

Researchers developed an explainable artificial intelligence model using craniofacial measurements alongside standard clinical data to classify obstructive sleep apnea severity into three categories, achieving 87.2% accuracy with the Apnea-Hypopnea Index and demonstrating that anatomical features substantially improved predictions compared to demographic and questionnaire data alone.

A plain-English summary of the study's own findings — we don't add our own claims or interpretation. Read the full paper for the complete methods and results, and apply your own clinical judgement.