EviOSAHS decomposes facial images into seven anatomical evidence cards plus clinical data for LLM-based binary OSAHS screening, reporting 88.47% accuracy and 94.86% sensitivity on 642 subjects while outperforming direct prompting baselines.
Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3d craniofacial scans.Sleep Medicine, 95:76–83, 2022
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Structured Visual Evidence Decomposition for Evidence-Grounded Multimodal Screening of Obstructive Sleep Apnea-Hypopnea Syndrome
EviOSAHS decomposes facial images into seven anatomical evidence cards plus clinical data for LLM-based binary OSAHS screening, reporting 88.47% accuracy and 94.86% sensitivity on 642 subjects while outperforming direct prompting baselines.