Application of machine learning in ophthalmic imaging modalities

Abstract

In clinical ophthalmology, a variety of image-related diagnostic techniques have begun to offer unprecedented insights into eye diseases based on morphological datasets with millions of data points. Artificial intelligence (AI), inspired by the human multilayered neuronal system, has shown astonishing success within some visual and auditory recognition tasks. In these tasks, AI can analyze digital data in a comprehensive, rapid and non-invasive manner. Bioinformatics has become a focus particularly in the field of medical imaging, where it is driven by enhanced computing power and cloud storage, as well as utilization of novel algorithms and generation of data in massive quantities. Machine learning (ML) is an important branch in the field of AI. The overall potential of ML to automatically pinpoint, identify and grade pathological features in ocular diseases will empower ophthalmologists to provide high-quality diagnosis and facilitate personalized health care in the near future. This review offers perspectives on the origin, development, and applications of ML technology, particularly regarding its applications in ophthalmic imaging modalities.

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

“…Artificial intelligence (AI) is the fourth industrial revolution in mankind’s history, and deep learning (DL) is a class of state-of-the-art machine learning techniques that has sparked tremendous global interest in recent years [ 9 ]. In the field of ophthalmology, DL use for the diagnosis of diabetic retinopathy, glaucoma, age-related macular degeneration, and retinopathy of prematurity using fundus photographs and/or optical coherence tomography (OCT) have been developed [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. For corneal diseases, DL can predict the likelihood of the need for future keratoplasty treatment [ 17 ].…”

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“…AIassisted ophthalmic imaging modalities and analyses are quickly developing. 10 AI shows promise to favorably impact diagnostic accuracy, efficiency, access to care, health equity, and knowledge in the field of ophthalmology.…”

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“…A class of DL called convolutional neural networks (CNNs) can break down images into square matrices, which are subsequently computed into numerical values in order to build a computer vision model, allowing for the detection of abnormal changes in fundus images. 8 CNNs are composed of multiple layers, often known as artificial neurons. These neurons are interconnected and typically have highly specific purposes to identify features in an image, such as…”

“…While this is a high-level overview, there are excellent in-depth reviews of the applying CNNs in ophthalmology. 2,8 As previously mentioned with the first FDA-approved AI diagnostic software and Google's validated systems, ML-based DR diagnosis has become increasingly robust. While DR and fundus imaging have led the clinical adoption of AI integration, additional ophthalmic pathologies and imaging modalities are being developed with AI.…”

“…Many AI algorithms conducting this type of analysis employ a subfield of ML called deep learning (DL). A class of DL called convolutional neural networks (CNNs) can break down images into square matrices, which are subsequently computed into numerical values in order to build a computer vision model, allowing for the detection of abnormal changes in fundus images 8 . CNNs are composed of multiple layers, often known as artificial neurons.…”