AI-Powered Smartphone Diagnostics for Convergence Insufficiency

Background: This study innovatively combines Artificial Intelligence (AI) algorithms with smartphone technology, automatically detecting the Near Point of Convergence (NPC) and diagnosing Convergence Insufficiency (CI) without the need for extra diagnostic tools and, notably, without having to rely on the subject’s vocal response, marking an unprecedented approach in the field to the best of our knowledge. Methods: This was a prospective study that enrolled 86 participants. The real-time tracking of eye structures and movements was conducted using AI technologies integrated with a mobile application (MobileS). Participants brought the smartphone closer, focusing on a target displayed on the screen. The system calculated pupillary distance (PD) and phone-to-face distance, incorporating a unique feature called the exodeviation episode’s counter (ExoCounter) to determine the NPC. Additionally, participants underwent testing using the RAF Ruler test (RulerT), considering the ground truth. Results: MobileS demonstrated significant correlation with the RulerT, as evidenced by a Pearson correlation coefficient of 0.74 (p < 0.001) and an Intraclass Correlation Coefficient (ICC) of 0.73 (p < 0.001), highlighting its reliability and consistency with conventional ophthalmic testing. Additionally, the system exhibited notable sensitivity and specificity in diagnosing CI. Notably, user feedback indicated a preference for the MobileS, with 71% of participants favouring it for its ease of use and comfort. Conclusions: MobileS is a precise, user-friendly tool for independent NPC measurement, applicable in tele-ophthalmology and home-based care. Its versatility extends beyond CI diagnosis, marking a significant advancement in ophthalmic diagnostics for accessible and efficient eye care.