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Abtahi MA, Beheshtnejad AH, Latifi G, Akbari-Kamrani M, Ghafarian S, Masoomi A, Sonbolastan SA, Jahanbani-Ardakani H, Atighechian M, Banan L, Nouri H, Abtahi SH. Corneal Epithelial Thickness Mapping: A Major Review. J Ophthalmol 2024; 2024:6674747. [PMID: 38205099 PMCID: PMC10776199 DOI: 10.1155/2024/6674747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/27/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The corneal epithelium (CE) is the outermost layer of the cornea with constant turnover, relative stability, remarkable plasticity, and compensatory properties to mask alterations in the underlying stroma. The advent of quantitative imaging modalities capable of producing epithelial thickness mapping (ETM) has made it possible to characterize better the different patterns of epithelial remodeling. In this comprehensive synthesis, we reviewed all available data on ETM with different methods, including very high-frequency ultrasound (VHF-US) and spectral-domain optical coherence tomography (SD-OCT) in normal individuals, corneal or systemic diseases, and corneal surgical scenarios. We excluded OCT studies that manually measured the corneal epithelial thickness (CET) (e.g., by digital calipers) or the CE (e.g., by confocal scanning or handheld pachymeters). A comparison of different CET measuring technologies and devices capable of producing thickness maps is provided. Normative data on CET and the possible effects of gender, aging, diurnal changes, refraction, and intraocular pressure are discussed. We also reviewed ETM data in several corneal disorders, including keratoconus, corneal dystrophies, recurrent epithelial erosion, herpes keratitis, keratoplasty, bullous keratopathy, carcinoma in situ, pterygium, and limbal stem cell deficiency. The available data on the potential role of ETM in indicating refractive surgeries, planning the procedure, and assessing postoperative changes are reviewed. Alterations in ETM in systemic and ocular conditions such as eyelid abnormalities and dry eye disease and the effects of contact lenses, topical medications, and cataract surgery on the ETM profile are discussed.
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Affiliation(s)
| | | | - Golshan Latifi
- Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Sadegh Ghafarian
- Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Masoomi
- Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | | | - Laleh Banan
- Sunshine Coast University Hospital, Brisbane, Queensland, Australia
| | - Hosein Nouri
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed-Hossein Abtahi
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Niazi S, Jiménez-García M, Findl O, Gatzioufas Z, Doroodgar F, Shahriari MH, Javadi MA. Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review. Diagnostics (Basel) 2023; 13:2715. [PMID: 37627975 PMCID: PMC10453081 DOI: 10.3390/diagnostics13162715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023] Open
Abstract
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
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Affiliation(s)
- Sana Niazi
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran P.O. Box 1336616351, Iran;
| | - Marta Jiménez-García
- Department of Ophthalmology, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
- Department of Medicine and Health Sciences, University of Antwerp, 2000 Antwerp, Belgium
| | - Oliver Findl
- Department of Ophthalmology, Vienna Institute for Research in Ocular Surgery (VIROS), Hanusch Hospital, 1140 Vienna, Austria
| | - Zisis Gatzioufas
- Department of Ophthalmology, University Hospital Basel, 4031 Basel, Switzerland;
| | - Farideh Doroodgar
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran P.O. Box 1336616351, Iran;
- Negah Aref Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 1544914599, Iran
| | - Mohammad Hasan Shahriari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 1971653313, Iran
| | - Mohammad Ali Javadi
- Ophthalmic Research Center, Labbafinezhad Hospital, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 19395-4741, Iran
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