Artificial Intelligence-Driven Telehealth Framework for Detecting Nystagmus

Le résumé fourni par la source

PURPOSE: This study reports the implementation of a proof-of-concept, artificial intelligence (AI)-driven clinical decision support system for detecting nystagmus. The system collects and analyzes real-time clinical data to assist in diagnosing, demonstrating its potential for integration into telemedicine platforms. Patients may benefit from the system's convenience, reduced need for travel and associated costs, and increased flexibility and increased flexibility through both in-person and virtual applications. METHODS: A bedside clinical test revealed vertigo during rightward body movement, and the patient was referred for videonystagmography (VNG). The VNG showed normal central ocular findings. During the right Dix-Hallpike maneuver, the patient demonstrated rotatory nystagmus accompanied by vertigo. Caloric tests revealed symmetric responses, with no evidence of unilateral or bilateral weakness. A cloud-based deep learning framework was developed and trained to track eye movements and detect 468 distinct facial landmarks in real time. Ten subjects participated in this study. RESULTS: The slow-phase velocity (SPV) value was verified for statistical significance using both VNG machine-generated graphs and clinician assessment. The average SPV was compared to the value generated by the VNG machine. The calculated statistical values were as follows: p < 0.05, a mean squared error of 0.00459, and a correction error of ±4.8%. CONCLUSION: This deep learning model demonstrates the potential to provide diagnostic consultation to individuals in remote locations. To some extent, it may supplement or partially replace traditional methods such as VNG. Ongoing advancements in machine learning within medicine will enhance the ability to diagnose patients, facilitate appropriate specialist referrals, and support physicians in post-treatment follow-up. As this was a proof-of-concept pilot study, further research with a larger sample size is warranted.

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