Keratoconus is a progressive disease of the cornea which can lead to blindness as irregular astigmatism increases. Currently, a variety of methods are available for the treatment of keratoconus, and in certain cases, it may be difficult to choose the most appropriate option. This article reviews available treatment modalities for keratoconus to provide the practitioner with practical and useful information for selecting the most suitable option for each individual patient. To review treatment methods for different stages of keratoconus, PubMed (United States National Library of Medicine) and Scopus (Elsevier BV) databases were searched using the keywords "keratoconus", "contact lens", "cross-linking", "Intacs", "keratoplasty", "gene therapy", and "irregular astigmatism", and related articles were reviewed based on disease assessment parameters and treatment methods. Various methods are available for the treatment of keratoconus: eyeglasses and contact lenses in the early stages, cross-linking for stabilizing disease progression, intrastromal corneal ring segments (ICRS) for reducing refractive errors or flattening the cornea, and penetrating keratoplasty (PK) and deep anterior lamellar keratoplasty (DALK), conductive keratoplasty, gene therapy and more recently, bowman layer transplantation (BL transplantation) in advanced stages of the disease. To achieve optimum results, it is essential to choose the best option for each individual patient. A commonality of the reviewed papers was the advancement of novel diagnostic and treatment methods in ophthalmology, which can delay the need for corneal grafting. A better understanding of keratoconus treatment options can help enhance visual rehabilitation and prevent blindness in keratoconus patients.

Telemedicine is an emerging field in recent medical achievements with rapid development. The “smartphone” availability has increased in both developed and developing countries even among people in rural and remotes areas. Tele-based services can be used for screening ophthalmic diseases and also monitoring patients with known diseases. Electronic ophthalmologic records of the patients including captured images by smartphones from anterior and posterior segments of the eye will be evaluated by ophthalmologists, and if patients require further evaluations, they will be referred to experts in the relevant field. Eye diseases such as cataract, glaucoma, age-related macular degeneration, diabetic retinopathy, and retinopathy of prematurity are the most common causes of blindness in many countries and beneficial use of teleophthalmology with smartphones will be a good way to achieve the aim of VISION 2020 all over the world. Numerous studies have shown that teleophthalmology is similar to the conventional eye care system in clinical outcomes and even provides more patient satisfaction as it saves time and cost. This review explains how teleophthalmology helps to improve patient outcomes through smartphones.

Purpose: To compare the agreement between artificial intelligence (AI)-based classifiers and clinical experts in categorizing normal cornea from ectatic conditions. Methods: Prospective diagnostic test study at Noor Eye Hospital. Two hundred twelve eyes of 212 patients were categorized into three groups of 92 normal, 52 subclinical keratoconus (SKCN), and 68 KCN eyes based on clinical findings by 3 independent expert examiners. All cases were then categorized using four different classifiers: Pentacam Belin/Ambrosio enhanced ectasia total deviation value (BADD) and Topographic Keratoconus Classification (TKC), Sirius Phoenix, and OPD-Scan III Corneal Navigator. The performance of classifiers and their agreement with expert opinion were investigated using the sensitivity, specificity, and Kappa index (κ). Results: For detecting SKCN, Phoenix had the highest agreement with the clinical diagnosis (sensitivity, specificity, and κ of 84.62%, 90.0%, and 0.70, respectively) followed by BADD (55.56%, 86.08%, 0.42), TKC (26.92%, 97.50%, 0.30), and Corneal Navigator (30.77%, 93.75%, 0.29). For KCN diagnosis, the highest agreement with expert opinion was seen for Phoenix (80.02%, 96.60%, 0.79), BADD (95.59%, 85.42%, 0.75), TKC (95.59%, 84.03%, 0.73), and Corneal Navigator (67.65%, 96.45%, 0.68). Analysis of different classifiers showed that Phoenix had the highest accuracy for differentiating KCN (91.24%) and SKCN (88.68%) compared to other classifiers. Conclusions: Although AI-based classifiers, especially Sirius Phoenix, can be very helpful in detecting early keratoconus, they cannot replace clinical experts’ opinions, particularly for decision-making before refractive surgery. Albeit, there may be concerns about the accuracy of clinical experts as well.