Artificial Intelligence-Powered System for Detecting, Diagnosing, and Rehabilitating Strabismus Disorder

Artificial Intelligence (AI) and machine learning analyze strabismus via Electro-oculography (EOG) signals, addressing eye muscle irregularities. Detecting this disorder's manifestation in both eyes is challenging, impacting the vision and the 3-dimensional perception. To this purpose, the aim is to develop an ophthalmologist-assisting system that reduces costly interventions like eyewear, lasers, and drugs. EOG signals from electrodes on the face reveal insights, focusing on the Center of Mass (CoM) of the ocular movements. The CoM determines the ocular misalignment extent due to muscle irregularities. Using the same device, ophthalmologists guide patient recovery through gamified exercises. Results highlighted the system's efficacy in differentiating between normal and aberrant conditions, quantifying deviation, and treating strabismus. In essence, AI and machine learning with EOG signal analysis offer a transformative solution for strabismus, providing early detection, accurate classification, and tailored rehabilitation for this ocular condition. The results of this study also suggested that the K-Nearest Neighbors (KNN) and wavelet decomposition tree methods may be effective for the classification and feature extraction of EOG data in the detection and diagnosis of eye diseases. These methods showed high levels of accuracy, with 98.3 and 98.5% respectively. However, further research is needed to confirm and extend these findings, and to explore the potential of these methods in the context of EOG and eye disease detection.