Closed-eye pupil monitoring system in patients with neurological disorders: a prospective, single-arm study

Objective This study aims to develop a non-invasive dynamic pupil monitoring system for patients with neurological disorders. Traditional methods (such as flashlight examination or infrared devices) rely on patients keeping their eyes open, making them unsuitable for comatose, sedated, or patients with abnormal eyelids, and they also have limitations in intermittent monitoring and subjective interpretation. This study leverages the ability of near-infrared light to penetrate eyelids, combined with deep learning technology, to design a novel closed-eye monitoring device that achieves dynamic capture through sequential infrared projection-eyelid reflection imaging. Methods A prospective single-arm trial design was adopted, enrolling 44 patients in the Department of Neurosurgery at Nanjing Drum Tower Hospital from April to July 2025. Safety was assessed by the incidence of adverse events, and technical accuracy was quantified using diameter prediction error and image segmentation performance. Results Results showed: the incidence of device-related adverse events was zero, the average error rate for pupil diameter prediction was 7.18%, and 92.5% of prediction values fell within the consistency range, indicating good model stability. However, image segmentation performance (Dice coefficient 0.47) and accuracy under extreme anatomical conditions still require optimization. Conclusion This system enables high-precision, safe, and non-invasive pupil monitoring for patients with neurological diseases, overcoming the reliance on patient cooperation in traditional methods, and providing an innovative solution for those unable to cooperate with examinations. Future studies should further validate reliability through multi-center, large-scale trials and optimize dynamic parameter quantification methods for specific neurological diseases.Registry: ChiCTR, TRN: ChiCTR2500105504, Registration date: 1 January 2024.