Artificial intelligence-assisted diagnosis of ocular surface diseases

Abstract

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

Diagnostic Imaging in Infectious Keratitis: A Review

“…The applications of artificial intelligence in health care are now a reality due to the advancement of computational power, refinement of learning algorithms and architectures, availability of big data and easy accessibility to deep neural networks by the public [41][42][43]. Deep learning algorithm mostly uses multimedia data (images, videos and sounds) and involves the application of large-scale neural networks such as artificial neural network (ANN), convolutional neural network (CNN) and recurrent neural network (RNN) [42]. The advantage of deep CNNs is learning the feature representation from data without human knowledge and the capability of processing large training data with high dimensionality [44].…”

“…For infectious keratitis, the use of DL with CNNs has shown to potentially be a more accessible diagnostic method via image recognition [4,45,46]. Many studies have evaluated DL methods for diagnosing IK using images taken with a handheld camara, camara mounted on a slit-lamp, or confocal microscopy [42,45,[47][48][49][50][51][52][53][54][55]. Several extremely efficient DL algorithms include RestNet-152 [56], DenseNet-169 [57], Mobile-Net V2 [58], and VGG-19_BN [59].…”

“…Several studies have also investigated the performance of DL CNNs methods in diagnosing FK using IVCM images [41,42]. Wu et al developed a new automatic hyphae detection method to diagnose FK which has two steps: texture classification of images and hyphae detection.…”

The Role of Artificial Intelligence in the Diagnosis of Neoplastic Diseases: A Systematic and Bibliometric Review

“…According to Arturo Montejo [71], an n-gram model is a type of probabilistic model that allows for statistical prediction [72] of the next element in a sequence of elements observed up to that point. An n-gram model can be defined as a Markov chain of order n-1 [73].…”

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis

“…There are four main approaches to screening patients for glaucoma: measuring IOP, examining the angle anatomy, evaluating the visual field (VF) and assessing optic nerve head (ONH) and nerve fiber layer (RNFL) [18][19] . Currently, AI is commonly used in glaucoma management including IOP, VF, false positive (FP), and OCT [20] . A prospective cross-sectional study demonstrated that automated IOP measurements using DL of Goldmann applanation tonometry (GAT) videos is comparable to standard GAT [21] .…”