Research on Precision Diagnosis and Treatment Decision of Common Eye Diseases Based on Artificial Intelligence
Research on Precision Diagnosis and Treatment Decision of Common Eye Diseases Based on Artificial Intelligence
The goal of this observational study is to investigate the clinical features of congenital ptosis in children by analyzing eyelid movement captured through smartphone videos. The main question it aims to answer is:
Can video-based eyelid measurements and levator muscle function grading provide useful guidance for the treatment of congenital ptosis in children?
Participants are children diagnosed with congenital ptosis. Their blinking behavior will be recorded using a smartphone, and eyelid morphology and muscle strength will be assessed from the video data.
In this study, we developed a two-module system for the evaluation of congenital ptosis in children based on smartphone video recordings. The first module performs morphological measurements of eyelid parameters (e.g., palpebral fissure height, margin reflex distance) extracted from blink videos. The accuracy of this module was assessed through agreement analysis between automated measurements and manual measurements performed by clinicians.
The second module focuses on levator function grading. The automated grading results were compared against clinical assessments by senior oculoplastic specialists, which served as the gold standard, to evaluate the model's accuracy. Additionally, we compared the model's performance to that of junior ophthalmology residents to assess the system's clinical assistive value in real-world scenarios.
Based on these modules, we developed a smartphone-based application for remote ptosis assessment. A multi-center clinical validation study was designed to evaluate the feasibility, consistency, and generalizability of the system in different clinical settings.
Inclusion Criteria:
Exclusion Criteria: