Artificial Intelligence-Powered Craniofacial Photogrammetry Analysis of Pediatric Obstructive Sleep Apnea.

OBJECTIVES

To develop an artificial intelligence (AI) system for craniofacial morphology analysis in pediatric obstructive sleep apnea (OSA) using photogrammetry.

STUDY

DESIGN

Prospective, cross-sectional study.

SETTING

Tertiary medical hospital.

METHODS

Children aged 3 to 18 years with OSA-related symptoms were enrolled and underwent overnight polysomnography (PSG) and standardized craniofacial photogrammetry. Moderate-to-severe OSA in children was defined as an apnea-hypopnea index (AHI) ≥ 5 events/h in PSG. An AI model using the Dlib tool identified facial landmarks, and the Hough transform calculated variables from these coordinates. Measurements by humans, the AI model, and a manually adjusted AI model were compared. Random forest identified the top 10 variable importance for the OSA prediction model.

RESULTS

Forty-three children with moderate-to-severe OSA and 43 age-, gender-, and obesity-matched controls were included. Shared predictors across models were mandibular plane angle, maxillary-mandibular relationship, lower facial length, and lower facial proportion. Additional predictors for the AI model included lower- and mid-face projection, while the adjusted AI model added mid-face projection, retrusive mandible, and cervicomental angle. The area under the curve (AUC) values for moderate-to-severe OSA prediction were similar in human, AI model, and adjusted AI model (0.74 vs 0.71 vs 0.70, P for ΔAUC > 0.05). The AI model significantly reduced measurement time (human vs AI vs adjusted AI = 511.4 vs 0.85 vs 15.8 seconds, P < .001).

CONCLUSION

The AI-powered photogrammetry analysis system is a rapid and reliable tool with comparable performance to human measurements in evaluating pediatric OSA.