Changes in corneal nerve morphology and function in patients with dry eyes having type 2 diabetes

Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review

Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the "black-box" nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.

The impact of dry eye disease on corneal nerve parameters: A systematic review and meta-analysis

PURPOSE

Dry eye disease (DED) is a growing global health problem with a significant impact on the quality of life of patients. While neurosensory abnormalities have been recognised as a contributor to DED pathophysiology, the potential role of in vivo corneal confocal microscopy in detecting nerve loss or damage remains unclear. This systematic review with meta-analysis (PROSPERO registered CRD42022381861) investigated whether DED has an impact on sub-basal corneal nerve parameters.

METHODS

PubMed, Embase and Web of Science Core Collection databases were searched from inception to 9 December 2022. Studies using laser scanning confocal microscopy to compare corneal nerve parameters of DED with healthy eyes were included. Study selection process and data extraction were performed by two independent members of the review team.

RESULTS

Twenty-two studies with 916 participants with DED and 491 healthy controls were included, with 21 of these studies included in subsequent meta-analyses. There was a decrease in total corneal nerve length (-3.85 mm/mm2 ; 95% CI -5.16, -2.55), corneal main nerve trunk density (-4.81 number/mm2 ; 95% CI -7.94, -1.68) and corneal nerve branch density (-15.52 number/mm2 ; 95% CI -27.20, -3.84) in DED eyes compared with healthy eyes, with subgroup analysis demonstrating that these differences were more evident in studies using NeuronJ software, a semi-automated procedure. While this review found evidence of loss of corneal nerve parameters in eyes with DED compared with healthy controls, particularly with the use of a semi-automated image analysis method, it is evident that there is substantial heterogeneity between studies in terms of corneal nerve imaging methodology.

CONCLUSIONS

Standardisation is required in terms of terminology and analysis, with more research needed to potentially improve the clinical applicability and practicality of corneal nerve imaging. Further investigation is also required to confirm the diagnostic accuracy of this imaging modality and its potential for monitoring DED treatment efficacy.