Artificial intelligence–based stratification of demographic, ocular surface high-risk factors in progression of keratoconus

Purpose: The purpose of this study was to identify and analyze the clinical and ocular surface risk factors influencing the progression of keratoconus (KC) using an artificial intelligence (AI) model. Methods: This was a prospective analysis in which 450 KC patients were included. We used the random forest (RF) classifier model from our previous study (which evaluated longitudinal changes in tomographic parameters to predict “progression” and “no progression”) to classify these patients. Clinical and ocular surface risk factors were determined through a questionnaire, which included presence of eye rubbing, duration of indoor activity, usage of lubricants and immunomodulator topical medications, duration of computer use, hormonal disturbances, use of hand sanitizers, immunoglobulin E (IgE), and vitamins D and B12 from blood investigations. An AI model was then built to assess whether these risk factors were linked to the future progression versus no progression of KC. The area under the curve (AUC) and other metrics were evaluated. Results: The tomographic AI model classified 322 eyes as progression and 128 eyes as no progression. Also, 76% of the cases that were classified as progression (from tomographic changes) were correctly predicted as progression and 67% of cases that were classified as no progression were predicted as no progression based on clinical risk factors at the first visit. IgE had the highest information gain, followed by presence of systemic allergies, vitamin D, and eye rubbing. The clinical risk factors AI model achieved an AUC of 0.812. Conclusion: This study demonstrated the importance of using AI for risk stratification and profiling of patients based on clinical risk factors, which could impact the progression in KC eyes and help manage them better

Impact of crossplay between ocular aberrations and depth of focus in topo-guided laser-assisted in situ keratomileusis outcomes

Purpose: To develop a nomogram in cases with mismatch between subjective and Topolyzer cylinder, and based on the magnitude of the mismatch, customize a treatment plan to attain good visual outcomes post?laser?assisted in situ keratomileusis (LASIK) surgery. Methods: The patients were evaluated preoperatively using corneal tomography with Pentacam. Five optimal corneal topography scans were obtained from the Topolyzer Vario were used for planning the LASIK treatment. For the nomogram purpose, the patients were divided into three categories based on the difference between the subjective cylinder and Topolyzer (corneal) cylinder. The first group (group 1) consisted of eyes of patients, where the difference was less than or equal to 0.4 D. The second group (group 2) consisted of eyes, where the difference was more than 0.4 D and the subjective cylinder was lesser than the Topolyzer cylinder. The third group (group 3) included eyes where the difference was more than 0.4 D but the subjective cylinder was greater than the Topolyzer cylinder. LASIK was performed with the WaveLight FS 200 femtosecond laser and WaveLight EX500 excimer laser. Assessment of astigmatism correction for the three groups was done using Aplins vector analysis. For comparison of proportions, Chi?square test was used. A P value less than 0.05 was considered statistically significant. Results: The UDVA was statistically significantly different when compared between groups 1 and 2 (P = 0.02). However, the corrected distance visual acuity (CDVA) was similar among all the three groups (P = 0.1). Group 3 showed an increase of residual cylinder by ?0.25 D, which was significant at intermediate and near reading distances (P < 0.05). Group 3 showed significantly higher target?induced astigmatism (TIA) compared to groups 1 and 2 (P = 0.01). The mean surgically induced astigmatism (SIA) was the least in group 2, which was statistically significant (P < 0.01). Conclusion: The outcomes for distance vision using our nomogram postoperatively were excellent, but further refinement for improving the near vision outcomes is required

A cross‑sectional study to compare intraocular pressure measurement by sequential use of Goldman applanation tonometry, dynamic contour tonometry, ocular response analyzer, and Corvis ST.

Objective: To study the correlation and effect of sequential measurement of intraocular pressure (IOP) with Goldmann applanation tonometer (GAT), ocular response analyzer (ORA), dynamic contour tonometer (DCT), and Corvis ST. Setting and Design: Observational cross‑sectional series from the comprehensive clinic of a tertiary eye care center seen during December 2012. Methods: One hundred and twenty‑five study eyes of 125 patients with normal IOP and biomechanical properties underwent IOP measurement on GAT, DCT, ORA, and Corvis ST; in four different sequences. Patients with high refractive errors, recent surgeries, glaucoma, and corneal disorders were excluded so as to rule out patients with evident altered corneal biomechanics. Statistical Analysis: Linear regression and Bland–Altman using MedCalc software. Results: Multivariate analysis of variance with repeated measures showed no influence of sequence of device use on IOP (P = 0.85). Linear regression r2 between GAT and Corvis ST, Corvis ST and Goldmann‑correlated IOP (IOPg), and DCT and Corvis ST were 0.37 (P = 0.675), 0.63 (P = 0.607), and 0.19 (P = 0.708), respectively. The Bland–Altman agreement of Corvis ST with GAT, corneal compensated IOP, and IOPg was 2 mmHg (−5.0 to + 10.3), −0.5 mmHg (−8.1 to 7.1), and 0.5 mmHg (−6.2 to 7.1), respectively. Intraclass correlation coefficient for repeatability ranged from 0.81 to 0.96. Conclusions: Correlation between Corvis ST and ORA was found to be good and not so with GAT. However, agreement between the devices was statistically insignificant, and no influence of sequence was observed.

Adaptive optics imaging of the retina.

Adaptive optics is a relatively new tool that is available to ophthalmologists for study of cellular level details. In addition to the axial resolution provided by the spectral-domain optical coherence tomography, adaptive optics provides an excellent lateral resolution, enabling visualization of the photoreceptors, blood vessels and details of the optic nerve head. We att empt a mini review of the current role of adaptive optics in retinal imaging. PubMed search was performed with key words Adaptive optics OR Retina OR Retinal imaging. Conference abstracts were searched from the Association for Research in Vision and Ophthalmology (ARVO) and American Academy of Ophthalmology (AAO) meetings. In total, 261 relevant publications and 389 conference abstracts were identified.

Keratoconus: A biomechanical perspective on loss of corneal stiffness.

Keratoconus (KC) is progressive disease of corneal thinning, steepening and collagen degradation. Biomechanics of the cornea is maintained by the intricate collagen network, which is responsible for its unique shape and function. With the disruption of this collagen network, the cornea loses its shape and function, resulting in progressive visual degradation. While KC is essentially a stromal disease, there is evidence that the epithelium undergoes significant thinning similar to the stroma. Several topographical approaches have been developed to detect KC early. However, it is now hypothesized that biomechanical destabilization of the cornea may precede topographic evidence of KC. Biomechanics of KC has been investigated only to a limited extent due to lack of in vivo measurement techniques and/or devices. In this review, we focus on recent work performed to characterize the biomechanical characteristics of KC.