Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications

Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications

DOI: 10.4018/979-8-3693-3661-8.ch006
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This chapter explores the growing applications of deep learning (DL) in the field of ophthalmology. Specifically, it examines the integration and efficacy of DL systems in enhancing patient outcomes, particularly in the diagnosis and management of conditions such as diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. It also outlines how DL algorithms are employed to analyze complex datasets and retinal images, enabling early detection, precise diagnosis, and effective treatment strategies. This chapter also addresses the challenges inherent in integrating AI into clinical practice, including issues related to data bias, algorithmic reliability, ethical concerns, and the need for diverse, representative datasets. It proposes a roadmap for the responsible implementation of DL in ophthalmology, emphasizing the importance of continuous research, development, and ethical considerations. Overall, this chapter presents a vision where these technologies not only enhance clinical practice but also promote improved health outcomes in the field of eye care.
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The development of computer graphical processing units, the advancement of mathematical models, and the accessibility of big data have enabled artificial intelligence (AI) to achieve strong performance in a variety of applications. These include social applications, the Internet of Things (IoT), the automotive industry, and the healthcare sector. (Liyakat, 2023; Liyakat & Liyakat, 2023a). Particularly, machine learning (ML) and deep learning (DL) systems offer enhanced capabilities in natural language processing (Garcia, 2020), image recognition (Maaliw et al., 2023), audio and motion recognition (Dixit & Kazi, 2015). In medicine, major advancements in AI, ML, and DL have been observed, particularly in image-centric fields such as pathology, ophthalmology (Grewal et al., 2018), radiology (Ramos, 2024), and dermatology (Esteva et al., 2017).

Recent research has demonstrated that ML and DL systems can accurately and efficiently identify cardiovascular risk factors and diseases from fundus photos (images) of conditions like diabetic retinopathy (DR), age-related macular degeneration (AMD), glaucoma, prematurity retinopathy, and refractive error (Ting et al., 2017; Ting et al., 2018). Additionally, there is growing interest in combining AI and DL systems to monitor the course of retinal illnesses, including diabetic macular edema and neovascular AMD using Optical Coherence Tomography (OCT) (Kaothanthong et al., 2023). However, few studies show how AI algorithms can anticipate the onset of clinical eye diseases, and none have demonstrated how these algorithms can use clinical data from electronic health records effectively. This article discusses the prevalence of eye diseases worldwide, unmet needs, and typical illnesses significant for public health and the application of DL systems (Patibandla et al., 2024). The potential challenges for clinical adoption are highlighted, along with the technical and clinical aspects of developing DL systems to meet those demands. Given the context of aging populations globally, AI and the growing applications of ML and DL will significantly impact the clinical practice of ophthalmology (Figure 1), with implications on the screening and diagnosis of the main causes of vision impairment.

Figure 1.

Application of ML and DL in ophthalmic imaging


Ophthalmology, a surgical area of medicine, is dedicated to diagnosing and treating conditions affecting the eyes. Long at the forefront of medical innovation, ophthalmology has a rich history of advancements in eye care. This field involves the in-depth study and investigation of various disorders, utilizing relevant technologies for effective treatment. As a branch of medicine, ophthalmology focuses on anatomy, physiology, and diseases of the eye. Ophthalmologists, medical specialists in this field, are skilled in both surgical and medical treatment. They are equipped to diagnose, treat, and prevent diseases of the eyes (Figure 2) and visual system, often examining the retina and optic nerve for early signs of conditions like cataracts or glaucoma. Ophthalmologists are adept at identifying and treating diseases, infections, and conditions affecting the eyes. Despite the promising applications of AI in this field, there is a notable gap in comprehensive studies reviewing these applications. There exists a significant need for a technical review to understand the full scope, limitations, and future potential of AI, ML, and DL in ophthalmology. Such a review is essential for guiding future research, identifying areas needing improvement, and facilitating the integration of these technologies into clinical practice (Chauhan et al., 2024; Ofosu-Ampong et al., 2024; Tariq, 2024a). This is especially crucial as the field of ophthalmology continues to evolve rapidly, with AI poised to play a pivotal role in its advancement.

Figure 2.

Corticosteroids in ophthalmology


Key Terms in this Chapter

Age-Related Macular Degeneration (AMD): AMD is a common eye condition that primarily affects central vision, which is crucial for seeing objects directly ahead. It is the leading cause of vision loss in individuals over the age of 50 in developed countries. The macula, a small but essential part of the retina responsible for sharp, central vision, deteriorates in AMD. This degeneration leads to blurred or distorted vision in the center of the visual field, impacting the ability to see fine details and perform tasks such as reading and driving.

Optical Coherence Tomography (OCT): OCT is a non-invasive medical imaging technique that utilizes light waves to produce high-resolution cross-sectional images of biological tissues. It is a groundbreaking technology in ophthalmology, with applications in cardiology, dermatology, and gastroenterology. OCT operates on low-coherence interferometry, measuring light reflection from tissue layers to create detailed 3D images of tissue structure, including thickness and abnormalities.

Diabetic retinopathy: This is a diabetes complication that affects the eyes. It's caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. However, it can eventually lead to blindness. The condition can develop in anyone who has type 1 or type 2 diabetes. The longer you have diabetes and the less controlled your blood sugar is, the more likely you are to develop this eye complication.

Retinopathy of Prematurity (ROP): ROP is an eye disorder potentially leading to blindness in premature infants, particularly those born before 31 weeks of gestation or weighing less than 2.75 pounds. It involves abnormal blood vessel growth in the retina, caused by the incomplete development of the retinal vasculature in premature infants. ROP's severity can vary, but in serious cases, it can lead to retinal detachment and vision loss.

Ophthalmology: Ophthalmology is the branch of medicine focused on anatomy, physiology, and diseases of the eye. The term is derived from the Greek words “opthalmos” meaning eye, and “logia” meaning study. Ophthalmologists are medical doctors specializing in the diagnosis and treatment of eye-related disorders. They are uniquely qualified for comprehensive eye care, being trained in both medicine and surgery.

Optical Coherence Tomography Angiography (OCTA): OCTA is a non-invasive imaging technique enhancing traditional OCT technology. It is vital in visualizing the retinal and choroidal vasculature without contrast dyes or invasive procedures. OCTA distinguishes itself by detecting red blood cell movement within vessels to map retinal and choroidal blood flow. The resulting 3D image offers a comprehensive view of microvasculature, invaluable for diagnosing and managing various ocular diseases.

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