Artificial intelligence in corneal diseases: A narrative review

Nguyen, Tuan; Ong, Joshua; Masalkhi, Mouayad; Waisberg, Ethan; Zaman, Nasif; Sarker, Prithul; Aman, Sarah; Lin, Haotian; Luo, Mingjie; Ambrosio, Renato; Machado, Aydano P.; Ting, Darren S.J.; Mehta, Jodhbir S.; Tavakkoli, Alireza; Lee, Andrew G.

doi:10.1016/j.clae.2024.102284

Contact Lens and Anterior Eye

2024

DOI: 10.1016/j.clae.2024.102284

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Artificial intelligence in corneal diseases: A narrative review

Tuan Nguyen,

Joshua Ong,

Mouayad Masalkhi

et al.

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2024

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Automated Characterization of Intrastromal Corneal Cuts Induced by Two Femtosecond Laser Systems Using OCT Imaging

Mehrjoo,

Khamar,

Darzi

et al. 2024

Photonics

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Optical coherence tomography (OCT) has gained momentum on segmenting anterior corneal substructures, such as treatment-induced flaps and lenticule cuts created by femtosecond lasers. However, recent semi-automated methods rely on manual markers, which can introduce bias and variability. In this work, we present an automated algorithm designed to overcome the limitations in the geometric quantification of intrastromal cuts produced by two different femtosecond lasers, using a unique imaging system. Our analysis, conducted on peri-operative segmentations of goat eyes, aims to demonstrate the method’s potential for contributing to ongoing efforts and enhancing clinical outcomes in refractive surgery treatment.

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Automated Characterization of Intrastromal Corneal Cuts Induced by Two Femtosecond Laser Systems Using OCT Imaging

Mehrjoo,

Khamar,

Darzi

et al. 2024

Photonics

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Artificial Intelligence in Dry Eye Disease: A Narrative Review

Nair,

Keskar,

Borghare

et al. 2024

Cureus

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Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.

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Artificial intelligence in corneal diseases: A narrative review

Cited by 2 publications

References 92 publications

Automated Characterization of Intrastromal Corneal Cuts Induced by Two Femtosecond Laser Systems Using OCT Imaging

Automated Characterization of Intrastromal Corneal Cuts Induced by Two Femtosecond Laser Systems Using OCT Imaging

Artificial Intelligence in Dry Eye Disease: A Narrative Review

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