AI ‐Assisted Video Monitoring for Tracheostomy‐Dependent Infants: A Proof‐Of‐Concept Study

Objective To retrospectively evaluate Eyes‐On, a patient‐personalized AI‐assisted video analysis system for frame‐level detection of tracheostomy tube status and visual distress in tracheostomy‐dependent infants. Methods In an IRB‐approved study, 25 tracheostomy‐dependent infants aged ≤ 2 years underwent video recording during routine tracheostomy care and tube changes. Data were partitioned at the patient level (22 infants for model development; 3 withheld for staged evaluation). From edited clips, 10,000 frames were extracted and annotated by two blinded pediatric otolaryngologists. A YOLOv11 detector was trained to detect cannulation status, and a facial distress classifier was built using facial features and action unit signals. Generalized pretrained models were tested on held‐out infants and then reevaluated after patient‐specific calibration. Individualized thresholds were selected using decision curve analysis. Results The pretrained cannulation detector achieved accuracy 0.736, sensitivity 0.806, specificity 0.667, mAP@50 0.645, and a 23.62% Not‐Detected rate ( n = 1200). After calibration, pooled evaluable‐frame cannulation performance improved to accuracy 0.940, sensitivity 0.997, and specificity 0.874 (AUROC 0.962; AUPRC 0.918). The pretrained distress classifier achieved accuracy 0.825, sensitivity 0.877, specificity 0.770, and a 21.8% face‐extraction failure rate. After calibration, evaluable‐frame distress accuracy increased to 0.960 with sensitivity 0.974 and specificity 0.946 (AUROC 0.993; AUPRC 0.993). Conclusion In this retrospective proof‐of‐concept study, patient‐personalized video‐based AI showed promising frame‐level classification of tracheostomy‐status and facial distress. These results reflect calibrated within‐patient deployment and do not validate event‐level outcomes, alarm thresholds, or standard monitoring modalities. Prospective real‐world evaluation is needed before clinical adoption. Level of Evidence 4.