Automated Diagnosis Of Glaucoma Using Ai Techniques

Glaucoma is a major cause of blindness globally. It damages the optic nerve cells that transmit visual information to the brain. Intra-Ocular Pressure (IOP) is the most significant danger reason to develop glaucoma. Even though a number of variables, including various optic disc parameters, have been used to discover early glaucoma damage, there is a real need for computer-aided detection (CAD) methods that can identify early glaucomatous development so that treatment to avoid further progression can be started especially in mass screenings, because ophthalmologists have limited time to assess mass number of fundus images.This thesis focused on the description of a system based on image processing and classification techniques for the estimation of quantitative parameters to classify fundus images into two classes: glaucoma patients and normal patients.

Glaucoma is the second leading cause of blindness globally. Early detection and treatment can prevent its progression to avoid total blindness. This book discusses and reviews current approaches for detection and examines new approaches for diagnosing glaucoma using CAD system. Computer-Aided Glaucoma Diagnosis System, Chapter 1 provides a brief introduction of the disease and current methodology used to diagnose it today. Chapter 2 presents a review of the medical background of the disease, followed by a theoretical and mathematical background used in fundus image processing. Chapter 3 is a literature review about segmentation and feature extraction. Chapter 4 describes the formulation of the proposed methodology. In Chapter 5, the results of optic disc and optic cup segmentation algorithm are presented, the feature extraction and selection method, experimental results and performance evaluations of the classifier are given. Chapter 6 presents the conclusions and discussion of the future potential for the diagnostic system. This book is intended for biomedical engineers, computer science students, ophthalmologists and radiologists looking to develop a reliable automated computer-aided diagnosis system (CAD) for detecting glaucoma and improve diagnosis of the disease. Key Features Discusses a reliable automated computer-aided diagnosis system (CAD) for detecting glaucoma and presents an algorithm that detects optic disc and optic cup Assists ophthalmologists and researchers to test a new diagnostic method that reduces the effort and time of the doctors and cost to the patients Discusses techniques to reduce human error and minimize the miss detection rate and facilitate early diagnosis and treatment Presents algorithms to detect cup and disc color, shape features and RNFL texture features Dr. Arwa Ahmed Gasm Elseid is an assistant professor, Department of Biomedical Engineering, Sudan University of Science and Technology, Khartoum, Sudan. Dr. Alnazier Osman Mohammed Hamza is professor of Medical Imaging, College of Engineering, Sudan University of Sciences and Technology, Khartoum, Sudan.

This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm. Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.

Glaucoma, the group of eye diseases is characterized by increased intraocular pressure, optic neuropathy and visual field defect patterns. Early and correct diagnosis of glaucoma can prevent irreversible vision loss and glaucomatous structural damages to the eye. However, greater chances of misdiagnosis by the currently used conventional methods for diagnosis open up ways for more advanced techniques like the use of artificial in- telligence (AI). Artificial intelligence coupled with optical coherence tomography imaging creates an algorithm that can be effectively used to make a model of complex data for detection as well as diagnosis of glaucoma. The Book is an attempt to provide state-of-the-art information on various AI techniques used in the diag- nosis and assessment of glaucoma. The second part of the Book is focused on understanding how the AI along with machine learning (ML) can be potentially used to be subjected for software as a medical device (SaMD) in precise diagnosis or early detection of disease conditions.

This book presents an overview of how machine learning and data mining techniques are used for tracking and preventing diseases. It covers several aspects such as stress level identification of a person from his/her speech, automatic diagnosis of disease from X-ray images, intelligent diagnosis of Glaucoma from clinical eye examination data, prediction of protein-coding genes from big genome data, disease detection through microscopic analysis of blood cells, information retrieval from electronic medical record using named entity recognition approaches, and prediction of drug-target interactions. The book is suitable for computer scientists having a bachelor degree in computer science. The book is an ideal resource as a reference book for teaching a graduate course on AI for Medicine or AI for Health care. Researchers working in the multidisciplinary areas use this book to discover the current developments. Besides its use in academia, this book provides enough details about the state-of-the-art algorithms addressing various biomedical domains, so that it could be used by industry practitioners who want to implement AI techniques to analyze the diseases. Medical institutions use this book as reference material and give tutorials to medical experts on how the advanced AI and ML techniques contribute to the diagnosis and prediction of the diseases.

Glaucoma is considered as a group of eye diseases characterized by optic neuropathy, a specific pattern of visual field defect and increased intraocular pressure. The rise in intraocular pressure leads to compres-sion of the retina and optic nerve that causes progressive, permanent loss of eyesight (Yadav et al., 2019). Glaucoma is known as the major cause of irreversible blindness worldwide (Gupta and Yadav, 2019). It is estimated that approximately 64.3 million individuals are affected by this disease and this number is expected to reach approximately 112 million by 2040 (Tham et al., 2014). Detection of glaucoma disease in its early stages may prevent blindness (Yadav et al., 2020). Undiagnosed glaucoma cases still account for more than 50 % of the cases worldwide the count of undiagnosed glaucoma cases is around 50 % (Gupta et al., 2016). Diagnosis of glaucoma is related to the assessment of the optic disc and the adjacent retinal nerve fibre layer by a trained specialist. Conventional methods for detection of glaucoma have higher chances of being misdiagnosed. Therefore, there is a need for an automated system which prevents misdiagnosis and makes correct use of the available medical data. As electronic medical records are becoming mainstream along with advancement in digital imaging and data storage, it is more important to appropriately utilize clinical data and digital images at this juncture than ever before.

This book reveals the applications of AI and IoT in smart healthcare and medical systems. It provides core principles, algorithms, protocols, emerging trends, security problems, and the latest e-healthcare services findings. The book also provides case studies and discusses how AI and IoT applications such as wireless devices, sensors, and deep learning could play a major role in assisting patients, doctors, and pharmaceutical staff. It focuses on how to use AI and IoT to keep patients safe and healthy and, at the same time, empower physicians to deliver superlative care. This book is written for researchers and practitioners working in the information technology, computer science, and medical equipment manufacturing industry for products and services having basic- and high-level AI and IoT applications. The book is also a useful guide for academic researchers and students.

Introduction We mark the eighth consecutive year for the World Glaucoma Association Glaucoma Consensus with Consensus VIII. Our topic is the Progression of Glaucoma. Global experts were invited and assembled by our international co-Chairs beginning in January 2011, to participate in the Project Forum E-Room, a unique online opportunity to facilitate discussion of each of the consensus meetings. Participants then were engaged in the discussion of five topical areas to reach consensus on key issues that surround and permeate all aspects of the progression of glaucoma. The results of these thoughtful discussions then were summarized by each of the sections with preliminary consensus statements. The Draft of the Consensus Report, including the preliminary consensus statements, was distributed to the Societies and Partners for review and comments prior to the Consensus Meeting that took place in Paris on Tuesday, June 28, 2011. On this day, relevant stakeholders engaged in a stimulating, educational, and thought-provoking session that highlighted the review and revision of the consensus statements. The Consensus Report then was finalized by Consensus co- Chairs and Editors. Consensus statements were reviewed and finalized by the expert Consensus Panel. Robert N. Weinreb, Editor