Applications of Artificial Intelligence in Corneal Nerve Images in Ophthalmology

TL;DR

Artificial intelligence is improving the analysis of corneal nerve images, helping diagnose eye conditions like dry eye disease and neuropathic corneal pain more accurately and efficiently.

Contribution

This paper reviews how AI enhances corneal nerve image analysis and its diagnostic applications in ophthalmology.

Findings

AI improves reproducibility and reduces manual effort in analyzing corneal nerve parameters from IVCM images.

AI-based algorithms show good performance in identifying and quantifying corneal nerve metrics.

AI improves diagnostic accuracy for dry eye disease and neuropathic corneal pain by analyzing specific nerve regions.

Abstract

Corneal nerves (CNs) are essential to maintain corneal epithelial integrity and ocular surface homeostasis. In vivo confocal microscopy (IVCM) enables the acquisition of high-resolution visualization of CNs, allowing visualization on a microscopic level. Traditionally, CN images must be analyzed by manual examination, which is time consuming and labor intensive. Artificial intelligence (AI) has facilitated reliable analysis of CN parameters, allowing for automatic and semiautomatic analysis of CNs. These include the identification, segmentation, and quantitative analysis of various CN parameters. This review summarizes the applications of AI-driven, automatic, and semiautomatic models in the CN analysis of IVCM images while also focusing on their diagnostic relevance in dry eye disease (DED) and neuropathic corneal pain (NCP). Recent advancements in AI have transformed IVCM image analysis by improving reproducibility and reducing operator dependency and time. The AI-based algorithm has been demonstrated to have good performance and sensitivity to identify and quantify the CN metrics. AI has also been utilized to improve the diagnostic accuracy of DED with IVCM scans, involving multiple portions of the CNs, such as the inferior whorl region. When employed with IVCM images of patients with NCP, AI-assisted identification of microneuromas and changes in CN metrics has provided an improvement in diagnostic accuracy. Despite promising advances and outcomes, the widespread implementation of these AI models in CN image analysis requires large-scale validation. Future integration of multimodal AI algorithms remains a promising endeavor to enhance diagnostic accuracy and disease stratification.

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