Video-Based Detection of squint and cataract for accessibility-aware adaptive web interface rendering

2026-07-08 · Source: Takara TLDR - Daily AI Papers · Field: Health & Wellbeing — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology, Web Accessibility · Depth: Expert, medium

Summary

Amar Ranjan Dash and Manas Ranjan Patra propose a real-time, video-based automated system for detecting squint and cataract, two major ocular disorders affecting visual perception. This low-cost system utilizes standard laptop or mobile cameras to record short video sequences. For squint detection, it employs a MediaPipe face-mesh, a 478-point facial landmark detection model, to extract geometric ocular features, achieving 98.39% accuracy in multi-class classification. Simultaneously, cataract presence and severity are estimated through grayscale intensity and histogram-based lens opacity analysis, demonstrating 96.90% classification accuracy. The framework is designed for large-scale deployment and future integration with adaptive user interfaces and web accessibility systems, aiming to assist individuals with visual impairments.

Key takeaway

For web accessibility developers or AI engineers building health screening applications, this system offers a robust, low-cost solution for real-time ocular disorder detection. You can integrate its 98.39% accurate squint detection and 96.90% accurate cataract classification into adaptive user interfaces. Consider deploying this video-based framework using standard device cameras to enhance digital accessibility and broaden screening reach for visually impaired users.

Key insights

Video-based computer vision can accurately detect squint and cataract for accessibility-aware web interfaces.

Principles

• Geometric facial features enable multi-class squint classification.
• Lens opacity analysis quantifies cataract presence and severity.
• Low-cost camera input supports large-scale ocular screening.

Method

The system uses MediaPipe face-mesh for geometric ocular features and grayscale/histogram analysis for lens opacity from video.

In practice

• Deploy real-time ocular screening on laptops or mobile devices.
• Integrate detection results into adaptive web accessibility systems.
• Use 478-point facial landmarks for precise squint analysis.

Topics

• Squint Detection
• Cataract Detection
• Computer Vision
• Web Accessibility
• MediaPipe Face-Mesh
• Ocular Disorders

Code references

• utkarshp1161/thickness-mapping-webapp

Best for: AI Scientist, Computer Vision Engineer, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.