Otogalucosense: AI-Powered system for glaucoma and otitis media detection

This study introduces a non-invasive pressure monitoring device driven by artificial intelligence for the real-time identification of otitis media and glaucoma. For effective data collection and processing, the system combines tonometric intraocular pressure sensors and MEMS-based middle ear pressure sensors with an Arduino microcontroller. Support Vector Machines (SVM) and Neural Networks are two examples of machine learning algorithms that evaluate the gathered data to reliably and accurately categorize anomalies. The system's small, portable form makes it appropriate for both clinical and home usage. It allows for wireless transmission for remote monitoring and shows real-time findings on an LCD screen. This system helps patients and healthcare providers by promoting early diagnosis, decreasing the need for invasive treatments, and improving access to reasonably priced healthcare. This AI-driven system, which has applications in ophthalmology, otolaryngology, and specialized settings like diving and aviation, is a major breakthrough in medical diagnostics that will improve patient outcomes and encourage preventative treatment globally. By bridging the gap between clinical expertise and home-based monitoring, this AI-driven technology offers a substantial improvement in medical diagnosis. Modern sensor technologies and machine learning are used to provide an accessible, scalable, and efficient early illness detection system that will eventually improve patient outcomes and transform preventive healthcare.