The article "Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging" by Clerimar Paulo Bragança, José Manuel Torres, Luciano Oliveira Macedo, and Christophe Pinto de Almeida Soares, discusses the progress of artificial intelligence (AI) algorithms in digital image processing and automatic diagnosis of glaucoma. Glaucoma is a multifactorial neuropathy affecting the eye, causing gradual vision loss and blindness in severe cases. Traditional diagnosis methods, such as fundus examinations, tonometry, and visual field tests, are highly sensitive but can be costly and time-consuming. The silent and slow progression of the disease often leads to late-stage diagnosis, contributing to high underdiagnosis rates. The authors explore how AI algorithms, particularly deep learning techniques like convolutional neural networks (CNNs), have been developed to aid in early diagnosis through population screening. These algorithms can analyze digital fundus images to detect early signs of glaucoma, such as changes in the cup-to-disc ratio (CDR) and patterns in the optic disc region. The article reviews the main types of glaucoma, traditional diagnostic methods, and the global epidemiology of the disease. It also discusses the challenges and limitations of current diagnostic methods, including the lack of uniform definitions and the need for more accurate and diverse datasets. The review highlights the potential of AI in reducing costs, improving early detection, and enhancing population screening. However, it also points out the need for further research to address issues such as data labeling accuracy, database homogeneity, and the integration of AI into clinical practice. The article concludes by emphasizing the importance of combining functional and structural exams and the ongoing efforts to develop more effective and reliable AI-based diagnostic tools for glaucoma.The article "Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging" by Clerimar Paulo Bragança, José Manuel Torres, Luciano Oliveira Macedo, and Christophe Pinto de Almeida Soares, discusses the progress of artificial intelligence (AI) algorithms in digital image processing and automatic diagnosis of glaucoma. Glaucoma is a multifactorial neuropathy affecting the eye, causing gradual vision loss and blindness in severe cases. Traditional diagnosis methods, such as fundus examinations, tonometry, and visual field tests, are highly sensitive but can be costly and time-consuming. The silent and slow progression of the disease often leads to late-stage diagnosis, contributing to high underdiagnosis rates. The authors explore how AI algorithms, particularly deep learning techniques like convolutional neural networks (CNNs), have been developed to aid in early diagnosis through population screening. These algorithms can analyze digital fundus images to detect early signs of glaucoma, such as changes in the cup-to-disc ratio (CDR) and patterns in the optic disc region. The article reviews the main types of glaucoma, traditional diagnostic methods, and the global epidemiology of the disease. It also discusses the challenges and limitations of current diagnostic methods, including the lack of uniform definitions and the need for more accurate and diverse datasets. The review highlights the potential of AI in reducing costs, improving early detection, and enhancing population screening. However, it also points out the need for further research to address issues such as data labeling accuracy, database homogeneity, and the integration of AI into clinical practice. The article concludes by emphasizing the importance of combining functional and structural exams and the ongoing efforts to develop more effective and reliable AI-based diagnostic tools for glaucoma.