Artificial Intelligence for Diagnosing Cranial Nerve III, IV, and VI Palsies Using Nine-Directional Ocular Photographs

Eye movements are regulated by the ocular motor nerves (cranial nerves [CNs] III, IV, and VI), which control the six extraocular muscles of each eye. Palsies of CNs III, IV, and VI can restrict eye movements, resulting in strabismus and diplopia, and so clinical evaluations of eye movements are crucial for diagnosing CN palsies. This study aimed to develop an accurate artificial intelligence (AI) system for classifying CN III, IV, and VI palsies using nine-gaze ocular photographs. We analyzed 478 nine-gaze photographs comprising 70, 29, and 58 cases of CN III, IV, and VI palsies, respectively. The images were processed using MATLAB. For model training, each photograph of eye movements in the nine directions was numerically coded. A multinetwork model was employed to ensure precise analyses of paralytic strabismus. The AI system operates by referring data on minor abnormalities in the nine-gaze image to a network designed to detect CN IV abnormalities, which re-examines downward and lateral gazes to detect distinctions. Data on major abnormalities are directed to a different network trained to differentiate between CN III and VI abnormalities. EfficientNet-B0 was applied to reduce overfitting and improve learning efficiency in training with limited medical imaging data as the neural network architecture. The diagnostic accuracies of the proposed network for CN III, IV, and VI palsies were 99.31%, 97.7%, and 98.22%, respectively. This study has demonstrated the design of an AI model using a relatively small dataset and a multinetwork training system for analyzing nine-gaze photographs in strabismus patients with CN III, IV, and VI palsies, achieving an overall accuracy of 98.77%.