Background: Strabismus diagnosis via the Alternate Cover Test (ACT) lacks quantitative standardization. This study proposes an AI-assisted framework using eye-tracking and machine learning for objective screening. Methods: Gaze coordinates were captured using a 60 Hz infrared eye tracker during ACT. Of the 291 initially screened individuals considered, 50 participants were ultimately included after quality filtering, yielding 335 valid samples. Seven algorithms were evaluated, with the dataset split into 294 training and 41 testing samples. Performance was measured by accuracy, sensitivity, specificity, PPV, and NPV. Results: Random Forest showed the best performance, achieving 97.56% accuracy (40/41) on the test set. It demonstrated a sensitivity of 1.00, specificity of 0.95, PPV of 0.95, and NPV of 1.00. The confusion matrix confirmed minimal false negatives, ensuring reliable clinical screening. Conclusions: The proposed system provides a robust, objective tool for strabismus diagnosis, standardizing ACT interpretation and reducing clinical bias.
The SARS-CoV-2 virus, responsible for COVID-19, often manifests symptoms akin to viral pneumonia, complicating early detection and potentially leading to severe COVID pneumonia and long-term effects. Particularly affecting young individuals, the elderly, and those with weakened i…
Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the exist…
Background: As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strateg…
Endometriosis is a chronic, debilitating condition affecting approximately 10–15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial intelligence (AI) offers innovative me…
Background: Myasthenia gravis (MG), a chronic autoimmune disorder with variable disease trajectories, presents considerable challenges for clinical stratification and acute care management. This systematic review evaluated machine learning models developed for prognostic assessme…
Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and dia…