Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning
Glaucoma is a condition characterized by unwarranted aqueous humor in the eye, leading to elevated intraocular pressure that can cause damage to the optic nerve. Current treatments for glaucoma are not highly effective and may have significant side effects. Monitoring intraocular pressure in real-ti...
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MDPI AG
2024-01-01
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| Series: | Engineering Proceedings |
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| author | Sivamani Chinnaswamy Vigneshwari Natarajan Selvi Samiappan Revathy Gurumurthy |
| author_facet | Sivamani Chinnaswamy Vigneshwari Natarajan Selvi Samiappan Revathy Gurumurthy |
| author_sort | Sivamani Chinnaswamy |
| collection | DOAJ |
| description | Glaucoma is a condition characterized by unwarranted aqueous humor in the eye, leading to elevated intraocular pressure that can cause damage to the optic nerve. Current treatments for glaucoma are not highly effective and may have significant side effects. Monitoring intraocular pressure in real-time and with accuracy is crucial, particularly for patients with severe glaucoma. Therefore, the development of wearable devices for continuous and precise intraocular pressure monitoring is a promising approach for diagnosing and treating glaucoma. However, existing intraocular pressure measurement and monitoring technologies face challenges in terms of scope, exactness, power feasting, and astuteness, which limit their suitability for glaucoma patients. To address these needs, this study focuses on the design and fabrication of an implantable, flexible intraocular pressure sensor capable of long-term continuous monitoring. This research investigates the working principle, structural design, fabrication process, measurement and control system, characterization, and performance testing of the intraocular pressure sensor. This research holds significant importance regarding achieving personalized and accurate treatment for glaucoma patients. Predictions are undertaken using Random forest, and results are obtained. Random forest has the highest accuracy when compared with other state-of-the-art models. |
| format | Article |
| id | doaj-art-a2f7066985ae49128cc91edf5f7d918a |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-a2f7066985ae49128cc91edf5f7d918a2025-08-20T02:42:45ZengMDPI AGEngineering Proceedings2673-45912024-01-0159117910.3390/engproc2023059179Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine LearningSivamani Chinnaswamy0Vigneshwari Natarajan1Selvi Samiappan2Revathy Gurumurthy3Department of Biomedical Engineering, KIT-KalaignarKarunanidhi Institute of Technology, Coimbatore 641402, Tamil Nadu, IndiaDepartment of Biomedical Engineering, KIT-KalaignarKarunanidhi Institute of Technology, Coimbatore 641402, Tamil Nadu, IndiaDepartment of Artificial intelligence and Data Science, Builders Engineering College, Tiruppur 638108, Tamil Nadu, IndiaDepartment of Computer Science, SRC, SASTRA Deemed University, Kumbakonam 613401, Tamil Nadu, IndiaGlaucoma is a condition characterized by unwarranted aqueous humor in the eye, leading to elevated intraocular pressure that can cause damage to the optic nerve. Current treatments for glaucoma are not highly effective and may have significant side effects. Monitoring intraocular pressure in real-time and with accuracy is crucial, particularly for patients with severe glaucoma. Therefore, the development of wearable devices for continuous and precise intraocular pressure monitoring is a promising approach for diagnosing and treating glaucoma. However, existing intraocular pressure measurement and monitoring technologies face challenges in terms of scope, exactness, power feasting, and astuteness, which limit their suitability for glaucoma patients. To address these needs, this study focuses on the design and fabrication of an implantable, flexible intraocular pressure sensor capable of long-term continuous monitoring. This research investigates the working principle, structural design, fabrication process, measurement and control system, characterization, and performance testing of the intraocular pressure sensor. This research holds significant importance regarding achieving personalized and accurate treatment for glaucoma patients. Predictions are undertaken using Random forest, and results are obtained. Random forest has the highest accuracy when compared with other state-of-the-art models.https://www.mdpi.com/2673-4591/59/1/179glaucomaintraocular pressuremicrocontrollerpressure sensortonometerrandom forest |
| spellingShingle | Sivamani Chinnaswamy Vigneshwari Natarajan Selvi Samiappan Revathy Gurumurthy Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning Engineering Proceedings glaucoma intraocular pressure microcontroller pressure sensor tonometer random forest |
| title | Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning |
| title_full | Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning |
| title_fullStr | Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning |
| title_full_unstemmed | Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning |
| title_short | Intraocular Pressure Monitoring System for Glaucoma Patients Using IoT and Machine Learning |
| title_sort | intraocular pressure monitoring system for glaucoma patients using iot and machine learning |
| topic | glaucoma intraocular pressure microcontroller pressure sensor tonometer random forest |
| url | https://www.mdpi.com/2673-4591/59/1/179 |
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