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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/14/7/694 |
| _version_ | 1797212754001526784 |
|---|---|
| author | Katarzyna Kryszan Adam Wylęgała Magdalena Kijonka Patrycja Potrawa Mateusz Walasz Edward Wylęgała Bogusława Orzechowska-Wylęgała |
| author_facet | Katarzyna Kryszan Adam Wylęgała Magdalena Kijonka Patrycja Potrawa Mateusz Walasz Edward Wylęgała Bogusława Orzechowska-Wylęgała |
| author_sort | Katarzyna Kryszan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:47:24Z |
| format | Article |
| id | doaj.art-19f563190de84a4486e41ca1d933831b |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-24T10:47:24Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-19f563190de84a4486e41ca1d933831b2024-04-12T13:16:42ZengMDPI AGDiagnostics2075-44182024-03-0114769410.3390/diagnostics14070694Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A ReviewKatarzyna Kryszan0Adam Wylęgała1Magdalena Kijonka2Patrycja Potrawa3Mateusz Walasz4Edward Wylęgała5Bogusława Orzechowska-Wylęgała6Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, PolandChair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, PolandChair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, PolandDepartment of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, PolandDepartment of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, PolandChair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, PolandDepartment of Pediatric Otolaryngology, Head and Neck Surgery, Chair of Pediatric Surgery, Medical University of Silesia, 40-760 Katowice, PolandArtificial 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.https://www.mdpi.com/2075-4418/14/7/694artificial intelligencedeep learningmachine learningin vivo confocal microscopy |
| spellingShingle | Katarzyna Kryszan Adam Wylęgała Magdalena Kijonka Patrycja Potrawa Mateusz Walasz Edward Wylęgała Bogusława Orzechowska-Wylęgała Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review Diagnostics artificial intelligence deep learning machine learning in vivo confocal microscopy |
| title | Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review |
| title_full | Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review |
| title_fullStr | Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review |
| title_full_unstemmed | Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review |
| title_short | Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review |
| title_sort | artificial intelligence enhanced analysis of in vivo confocal microscopy in corneal diseases a review |
| topic | artificial intelligence deep learning machine learning in vivo confocal microscopy |
| url | https://www.mdpi.com/2075-4418/14/7/694 |
| work_keys_str_mv | AT katarzynakryszan artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview AT adamwylegała artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview AT magdalenakijonka artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview AT patrycjapotrawa artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview AT mateuszwalasz artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview AT edwardwylegała artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview AT bogusławaorzechowskawylegała artificialintelligenceenhancedanalysisofinvivoconfocalmicroscopyincornealdiseasesareview |