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

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 bo...

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Bibliographic Details
Published in:Diagnostics (Basel) Vol. 14; no. 7; p. 694
Main Authors: Kryszan, Katarzyna, Wylęgała, Adam, Kijonka, Magdalena, Potrawa, Patrycja, Walasz, Mateusz, Wylęgała, Edward, Orzechowska-Wylęgała, Bogusława
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01-04-2024
Subjects:
Algorithms
Artificial intelligence
Classification
Cornea
Corneal diseases
Datasets
Deep learning
in vivo confocal microscopy
Information management
machine learning
Microscope and microscopy
Microscopy
Neural networks
Poland
deep learning
in vivo confocal microscopy
machine learning
artificial intelligence
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Summary: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.
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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14070694