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Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning

  • Babita Gupta , Rishabha Malviya and Sonali Sundram
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Digital Transformation in Healthcare 5.0
This chapter is in the book Digital Transformation in Healthcare 5.0

Abstract

Glaucoma is a prevalent neurological disease with a global impact, affecting about 65 million individuals. The condition is characterized by a gradual deterioration of the optic nerve, leading to permanent visual impairment that cannot be reversed. An increased intraocular pressure (IOP) in the eye is regarded to be one of the most significant preventable risk factors associated with the condition. The chapter presents a novel IOP monitoring system designed for individuals with glaucoma, which utilizes the capabilities of the Internet of things (IoT) and machine learning. IoT sensors monitor IOP noninvasively while transmitting real-time data to a hub and cloud servers. Early IOP intervention is possible using machine learning methods that identify minor IOP variations. When IOP rises over predetermined limits, an alarm system warns both patients and medical staff. Accessing data and receiving individualized suggestions via a simple interface encourages patient participation and treatment adherence. There are several benefits to using this system, including constant monitoring, early discovery, individualized treatment, and data portability. Minimizing in-person checkups may save individuals and healthcare systems money. The system’s clinical performance depends on addressing data security, sensor accuracy, user uptake, and EHR (electronic health record) integration. In conclusion, our IoT and Machine Learning-based IOP monitoring system might improve glaucoma therapy and patient outcomes and quality of life.

Abstract

Glaucoma is a prevalent neurological disease with a global impact, affecting about 65 million individuals. The condition is characterized by a gradual deterioration of the optic nerve, leading to permanent visual impairment that cannot be reversed. An increased intraocular pressure (IOP) in the eye is regarded to be one of the most significant preventable risk factors associated with the condition. The chapter presents a novel IOP monitoring system designed for individuals with glaucoma, which utilizes the capabilities of the Internet of things (IoT) and machine learning. IoT sensors monitor IOP noninvasively while transmitting real-time data to a hub and cloud servers. Early IOP intervention is possible using machine learning methods that identify minor IOP variations. When IOP rises over predetermined limits, an alarm system warns both patients and medical staff. Accessing data and receiving individualized suggestions via a simple interface encourages patient participation and treatment adherence. There are several benefits to using this system, including constant monitoring, early discovery, individualized treatment, and data portability. Minimizing in-person checkups may save individuals and healthcare systems money. The system’s clinical performance depends on addressing data security, sensor accuracy, user uptake, and EHR (electronic health record) integration. In conclusion, our IoT and Machine Learning-based IOP monitoring system might improve glaucoma therapy and patient outcomes and quality of life.

Chapters in this book

  1. Frontmatter I
  2. About the book V
  3. Preface VII
  4. Foreword IX
  5. Contents XI
  6. List of contributors XV
  7. Chapter 1 The impact of blockchain technology on the healthcare system 1
  8. Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
  9. Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
  10. Chapter 4 Role of artificial intelligence in disease diagnosis 89
  11. Chapter 5 Machine learning for twinning the human body 105
  12. Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
  13. Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
  14. Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
  15. Chapter 9 Nanorobots in healthcare 209
  16. Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
  17. Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
  18. Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
  19. Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
  20. Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
  21. Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
  22. Index 375
https://doi.org/10.1515/9783111398549-014

Chapters in this book

  1. Frontmatter I
  2. About the book V
  3. Preface VII
  4. Foreword IX
  5. Contents XI
  6. List of contributors XV
  7. Chapter 1 The impact of blockchain technology on the healthcare system 1
  8. Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
  9. Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
  10. Chapter 4 Role of artificial intelligence in disease diagnosis 89
  11. Chapter 5 Machine learning for twinning the human body 105
  12. Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
  13. Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
  14. Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
  15. Chapter 9 Nanorobots in healthcare 209
  16. Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
  17. Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
  18. Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
  19. Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
  20. Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
  21. Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
  22. Index 375
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