Automated Strabismus Screening Based on a Wearable Eye Tracker

With the global market for ophthalmic screening devices projected to reach $11 billion by 2030, the development of accessible screening solutions presents substantial socioeconomic value. The key to the application of ophthalmic screening in consumer electronics lies in achieving rapid, accurate, user-friendly, and cost-effective large-scale screening. To address these requirements, this paper presents a wearable strabismus screening device that utilizes eye-tracking technology. The screening device continuously captures near-infrared eye movement images and employs image processing techniques to extract multi-dimensional eye movement features based on pupil movements. Subsequently, a machine learning model is utilized to perform supervised classification and analyze the medical correlation of these features, providing a statistical foundation for clinical research into the etiology of strabismus. The method achieves a screening accuracy of 97.5% for 80 subjects (39 patients) and a 100% detection rate for strabismus patients. This product, created through the integration of consumer-grade hardware components, yields a portable, home-based screening solution with significant medical value and commercial potential.