Keratoconus: imaging modalities and management.

Background: Keratoconus (KCN) is characterized by gradual thinning and steepening of the cornea, which can lead to significant vision problems owing to high astigmatism, corneal scarring, or even corneal perforation. The detection of KCN in its early stages is crucial for effective treatment. In this review, we describe current advances in the diagnosis and treatment of KCN. Methods: This narrative review focuses on recent advancements in the diagnosis and treatment of KCN, especially evolving approaches and strategies. To ensure the inclusion of the most recent literature, relevant publications discussing advanced imaging techniques and treatment options for KCN were extensively gathered from the PubMed/MEDLINE and Google Scholar databases. The following index terms and keywords were used for the online search: keratoconus, diagnosis of keratoconus, advances in the diagnosis of keratoconus, topography or tomography, anterior segment optical coherence tomography, treatment of keratoconus, advances in the treatment of keratoconus, collagen crosslinking, intrastromal ring, keratoplasty, and new techniques in keratoconus. Results: Various screening methods such as corneal topography, tomography, anterior segment optical coherence tomography, and assessment of corneal biomechanics have been developed to identify KCN in its early stages. After diagnosis, KCN management focuses on preventing disease progression. Corneal collagen crosslinking is a minimally invasive treatment that can slow or stop the progression of the condition. Recent research has also explored the use of copper sulfate eye drops (IVMED-80) as a noninvasive treatment to prevent the progression of KCN. Current treatment options for visual improvement include scleral lenses, intracorneal ring segments, corneal allogeneic intrastromal ring segments, and deep anterior lamellar keratoplasty. Recently, novel alternative procedures, such as isolated Bowman layer transplantation, either as a corneal stromal inlay or onlay, have demonstrated encouraging outcomes. Artificial intelligence has gained acceptance for providing best practices for the diagnosis and management of KCN, and the science of its application is contentiously debated; however, it may not have been sufficiently developed. Conclusions: Early detection and advancements in screening methods using current imaging modalities have improved diagnosis of KCN. Improvement in the accuracy of current screening or diagnostic tests and comparison of their validities are achievable by well-designed, large-scale, prospective studies. The safety and effectiveness of emerging treatments for KCN are currently being investigated. There is an ongoing need for studies to track progress and evaluate clinicians' knowledge and practices in treating patients with KCN. Artificial intelligence capabilities in management approach considering the currently available imaging modalities and treatment options would best benefit the patient.