Association of 2 Lysyl Oxidase Gene Single Nucleotide Polymorphisms with Keratoconus

Niazi, Sana; Moshirfar, Majid; Alizadeh, Fatemeh; Doroodgar, Farideh; Baradaran-Rafii, Alireza; Filutowski, Oliver; Niazi, Feizollah; Ambrósio, Renato

doi:10.1016/j.xops.2022.100247

Cited by 5 publications

(4 citation statements)

References 54 publications

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“… 14 Pachymetric 41 and tomographic indices 42 integrate with biomechanical evaluation of cornea, 43 anterior corneal higher order aberrations (HOAs), 44 and ultra-high-resolution Optical Coherence Tomography (OCT) 45 not only use preoperatively, but also for postoperative (epithelial remodeling 46 and anterior stromal demarcation) management. 47 Furthermore, regarding genetic aspects 3 , 7 and metabolic imbalance of the disease, 48 recent reviews considered that micronutrients such as metal ions, vitamins, hormones, antioxidants, oral riboflavin, and Essential Fatty Acids, can deeply influence KC initiation and progression, particularly in the early stages of KC. 49 Hence, as early as holistic approaches is necessary to get rid of unfavorable circumstances and may prevent the further spread of the disease.…”

Section: Resultsmentioning

confidence: 99%

“…The advent of artificial intelligence in ophthalmology 1 neural network algorithms and deep learning strategies in keratoconus (KC) diagnosis, 2 genetic, 3 and molecular biologic tests 4 alter the natural history of KC. 5 – 7 This may allow us to think that KC is a different specialization of ophthalmology rather than just a clinical disorder.…”

Section: Introductionmentioning

confidence: 99%

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Keratoconus: exploring fundamentals and future perspectives – a comprehensive systematic review

Niazi,

Gatzioufas,

Doroodgar

et al. 2024

Ophthalmol Eye Dis

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Background: New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way. Objectives: This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies. Design: A multidimensional comprehensive systematic narrative review. Data sources and methods: A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed. Results: Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes. Conclusion: The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients. Trial registration: The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Keratoconus: exploring fundamentals and future perspectives – a comprehensive systematic review

Niazi,

Gatzioufas,

Doroodgar

et al. 2024

Ophthalmol Eye Dis

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“…Various methods were described for detecting keratoconus (KC) ( Figure 1 ), primarily utilizing corneal topographers or tomographers. However, as mentioned, KC is a multifactorial condition involving genetic factors [ 12 , 13 , 14 ], environmental factors such as atopy [ 15 ], and repetitive mechanical corneal trauma [ 12 ] in its development and progression [ 16 ]. Due to the complex nature of KC, there is potential benefit in utilizing artificial intelligence (AI) approaches, including corneal biomechanical information, which already showed promise in forecasting the progression of keratoconus [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review

Niazi,

Jiménez-García,

Findl

et al. 2023

Diagnostics

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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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“…There is strong evidence supporting the role of genetics in KC based on family history 5–8 and genetic composition. 9,10 Some families have shown an autosomal dominant pattern of inheritance, and family-based linkage studies have identified at least 19 candidate genetic loci that may harbor genetic mutations for KC. 11…”

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confidence: 99%

Association Between Family History and the Corneal Characteristics of Keratoconus in Japan

Itoi,

Itoi

2024

Eye &Amp; Contact Lens: Science &Amp; Clinical Practice

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Purpose: The aim of this study was to determine the association between family history and corneal characteristics of keratoconus in Japan. Methods: The clinical records of patients with keratoconus who were interviewed regarding family history of keratoconus were retrospectively reviewed. Corneal indices derived from anterior segment optical coherence tomography were compared between the positive and negative family history groups using multiple linear regression analysis adjusted with age, sex, and history of atopy and eye rubbing. Results: A total of 2,701 eyes, including 122 eyes with positive family history, were enrolled. Based on the adjusted multiple linear regression analysis, no significant differences were found between groups with and without a family history except for paracentral and mid-peripheral corneal thickness. The adjusted mean differences in paracentral and mid-peripheral corneal thicknesses between the two groups were −10.33 (95% confidence interval −20.29 to −0.37 P-value <0.05) and −9.78 (95% confidence interval −16.70 to −2.85, P-value <0.01), respectively. Conclusions: There was no significant association between family history and corneal indices in Japanese patients with keratoconus, except for paracentral and mid-peripheral corneal thicknesses. A Japanese patient with keratoconus who had a family history showed significantly lower paracentral and mid-peripheral corneal thicknesses than those with no family history.

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Association of 2 Lysyl Oxidase Gene Single Nucleotide Polymorphisms with Keratoconus

Cited by 5 publications

References 54 publications

Keratoconus: exploring fundamentals and future perspectives – a comprehensive systematic review

Keratoconus: exploring fundamentals and future perspectives – a comprehensive systematic review

Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review

Association Between Family History and the Corneal Characteristics of Keratoconus in Japan

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