Insight-Eye: A Smart System for Real-Time Eyesight Assessment using Computer Vision and NLP
InsightEye is a real-time eyesight testing system that combines computer vision and natural language processing (NLP) technologies. It captures and analyzes visual behaviors such as eye movements and fixation points to provide an accurate and objective measurement of an individual's eyesight health. The system can detect and classify different visual patterns in real-time and provide immediate feedback to the user or healthcare professionals. The NLP component further analyzes the data generated by the computer vision algorithms to offer insights into the individual's eyesight health status. This paper presents a novel approach to combining computer vision and NLP technologies for real-time eyesight testing.
In low-income and underprivileged regions, limited access to eye care services and resources leads to an increased risk of vision impairment, blindness, and related health complications. The scarcity of trained eye care professionals, particularly in rural areas, further aggravates the problem, leaving numerous individuals without sufficient support for their visual health requirements. To tackle this challenge, it is crucial to devise innovative, accessible, and cost-effective solutions that guarantee equitable access to high-quality eye care services for all. InsightEye empowers individuals to make informed decisions about seeking professional care. This system also supports healthcare professionals in remote settings by facilitating early intervention and diagnosis. With its user-friendly design, InsightEye democratizes access to high-quality eye care services, fostering awareness, early detection, and prompt treatment of vision-related issues. Ultimately, this contributes to the global endeavor to diminish preventable blindness and vision impairment..
The benefits of the Insight Eye’s real-time eyesight testing system include:
1- Early detection: Facilitates the early detection and prevention of eye diseases, reducing the risk of serious conditions. 2- Objective measurement: Provides reliable and accurate assessments, enhancing diagnostic precision and treatment effectiveness. 3- Real-time feedback: Offers instant feedback, allowing users to promptly adjust their visual habits for better eye health. 4- Convenience: Enables remote monitoring, saving time and effort spent on in-person appointments. 5- Cost-effective: Can be scaled to accommodate large populations, providing an affordable solution for healthcare organizations and public health initiatives. 6- Accessibility: User-friendly design ensures the system is available to those who face barriers to traditional eye exams. 7- Personalized care: Delivers tailored feedback, empowering users to make informed decisions about their eye health. 8- Public health impact: Contributes to improved public health outcomes by enabling early intervention and reducing the burden on healthcare systems. 9- InsightEye offers an accessible, affordable, and comprehensive solution for early-stage eye health assessment, ultimately enhancing the quality of life for individuals with vision impairments.
1- S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," in Proc. Advances in Neural Information Processing Systems, 2015, pp. 91-99. [Online]. Available: https://arxiv.org/abs/1506.01497 2- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779-788. [Online]. Available: https://arxiv.org/abs/1506.02640 3- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, "Attention is all you need," in Proc. Advances in Neural Information Processing Systems, 2017, pp. 5998-6008. [Online]. Available: https://arxiv.org/abs/1706.03762 4- T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, and J. Brew, "HuggingFace's Transformers: State-of-the-art natural language processing," arXiv preprint arXiv:1910.03771, 2019. [Online]. Available: https://arxiv.org/abs/1910.03771 5- C. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008. [Online]. Available: https://nlp.stanford.edu/IR-book/ 6- A. Rosebrock, Deep Learning for Computer Vision, PyImageSearch, 2018. [Online]. Available: https://www.pyimagesearch.com/deep-learning-computer-vision-python-book 7- Next.js Documentation. [Online]. Available: https://nextjs.org/docs 8- Google Cloud Vision API Documentation. [Online]. Available: https://cloud.google.com/vision/docs 9- Hugging Face Transformers Documentation. [Online]. Available: https://huggingface.co/transformers/