Strabismus Detection Module

Strabismus Detection Module is a fully open-source diagnostic system developed for the preliminary screening and visual rehabilitation of patients with ocular misalignment (strabismus). The system integrates computer vision and deep learning into a portable, low-cost embedded solution that runs entirely on a Raspberry Pi 4, requiring no specialized clinical hardware.

The software is implemented in Python using PyQt5 for the graphical interface, OpenCV for image acquisition and preprocessing, and TensorFlow Lite for lightweight neural inference. It incorporates a NASNetLarge-based convolutional model optimized for binary classification (strabismus vs. normal), supported by a graphical interface that guides the user through image capture, automated analysis, and PDF report generation. A gamified visual rehabilitation module is activated in positive cases, offering dynamic ocular fixation exercises adjustable in pattern, speed, size, and color.

The hardware includes a 3D-printed chin rest designed in SolidWorks to ensure standardized facial alignment during image acquisition. All mechanical parts are provided in both CAD (.SLDPRT, .SLDASM) and printable STL formats, and the electronic schematic is available in Fritzing format (.fzz), showing the basic wiring between the Raspberry Pi, camera, display, and power supply.

All source code, including the GUI logic (main.py), multilingual support (translations.py), trained model (Modelos/NASNETLARGE.tflite), and necessary classifiers (Clasificadores/), is provided under the GNU General Public License v3.0. Hardware designs (CAD, STL, and schematic files) are distributed under the CERN Open Hardware Licence v2 – Strongly Reciprocal (CERN-OHL-S v2.0).

This system has been validated on a proprietary dataset and through real-time testing with clinically diagnosed patients, achieving 96.30% accuracy. Its modular design and open licensing promote replicability, customization, and future expansion by the research and education communities.