A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images.

Purpose: Accurate identification of corneal layers with in vivo confocal microscopy (IVCM) is essential for the correct assessment of corneal lesions. This project aims to obtain a reliable automated identification of corneal layers from IVCM images.

Methods: A total of 7957 IVCM images were included for model training and testing.

An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images.

Purpose: Fungal keratitis is a common cause of blindness worldwide. Timely identification of the causative fungal genera is essential for clinical management. In vivo confocal microscopy (IVCM) provides useful information on pathogenic genera.

The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis Using Confocal Microscopy Images.

Artificial intelligence (AI) has great potential to detect fungal keratitis using confocal microscopy images, but its clinical value remains unclarified. A major limitation of its clinical utility is the lack of explainability and interpretability. An explainable AI (XAI) system based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided Grad-CAM was established.

A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.

Purpose: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images.