Development of a Multimodal Obstructive Sleep Apnea Diagnostic Prediction Model Using Two-Dimensional Facial Images and Clinical Data

Obstructive sleep apnea (OSA) is a prevalent disorder in middle-aged and obese men with increased risk of cardiovascular disease, metabolic dysfunction, and neurocognitive impairments. Delays in diagnosis and treatment of patients with OSA increase long-term all-cause mortality. In this study, we developed a multimodal artificial intelligence (AI) model that utilizes two-dimensional facial photograph, cephalometric radiograph, and clinical data to enhance OSA screening. Clinical parameters of sex, age, body mass index, neck circumference, and abdominal circumference and sleep questionnaires were assessed. Data from 710 patients who underwent polysomnography were used to train and validate a deep learning model combining ShuffleNet-V2 for image feature extraction and a deep neural network for clinical data analysis. The model was evaluated using five-fold cross-validation and a holdout test set. Performance metrics were area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. The trimodal AI model significantly outperformed uni- and bimodal approaches, achieving an AUC of 0.859 in distinguishing moderate-severe OSA from normal-mild OSA. Subgroup analyses of data from obese, elderly, and male patients showed higher classification accuracy, and data from patients with smaller abdominal circumferences had the lowest sensitivity compared to other subgroups. The Grad-CAM analysis demonstrated that the model focused on airway structures and low part of face, aligning with clinical expectations. This study presents a novel AI-driven OSA screening approach that integrates data from facial images and clinical data. This AI-based OSA screening model may facilitate early diagnosis of OSA and improve patient outcomes.