Dissecting the Profile of Corneal Thickness With Keratoconus Progression Based on Anterior Segment Optical Coherence Tomography

The role of corneal epithelial thickness map in detecting early keratoconus

PURPOSE

To detect subtle changes in early keratoconus by evaluating corneal epithelial thickness differences among patients with binocular very asymmetric ectasia (VAE) and normal subjects.

METHODS

Corneal epithelial thickness was measured using the Fourier-domain AS-OCT system RTVue® 100 (Optovue, Fremont, CA, USA). 152 eyes from 76 patients were divided into three groups: Very asymmetry ectasia-ectasia (VAE-E, n = 38), Very asymmetry ectasia-normal topography (VAE-NT, n = 38), and Normal control (NC, n = 76). Discrimination capacity was assessed using areas under the curve (AUC) of receiver operator characteristic (ROC) curves.

RESULTS

In the keratoconus group, the epithelial Min (minimum), central, midperipheral I (inferior), midperipheral IT (inferior-temporal), peripheral IT, and midperipheral T (temporal) were thinner (all P < 0.05). The topography normal group had thinner midperipheral IN (inferior-nasal), peripheral IN, midperipheral T, and peripheral T, and larger Max-Min and Std. Dev (P < 0.05). For diagnosing typical keratoconus, Std. Dev (AUC = 0.982, sensitivity 97.4%, specificity 92.1%) had the highest diagnostic efficiency. Combining four variables (Minimum, Max-Min, Midperipheral IT, and Midperipheral I) performed well in distinguishing topography normal eyes (AUC = 0.896, sensitivity 76.3%, specificity 89.5%). Multivariable analysis using epithelial parameters combined with Pentacam random forest index (PRFI) yielded the best results (AUC = 0.951, sensitivity 90.6%, specificity 89.5%).

CONCLUSIONS

The corneal epithelial parameters play an important auxiliary role in the diagnosis of keratoconus and the screening of subclinical keratoconus. Combination of epithelial parameters and tomographic parameters can improve the sensitivity of early stage keratoconus detection.

Evaluation of choroid vascular layer thickness in wet age-related macular degeneration using artificial intelligence

PURPOSE

To facilitate the assessment of choroid vascular layer thickness in patients with wet age-related macular degeneration (AMD) using artificial intelligence (AI).

METHODS

We included 194 patients with wet AMD and 225 healthy participants. Choroid images were obtained using swept-source optical coherence tomography. The average Sattler layer-choriocapillaris complex thickness (SLCCT), Haller layer thickness (HLT), and choroidal thickness (CT) were auto-measured at 7 regions centered around the foveola using AI and subsequently compared between the 2 groups.

RESULTS

The SLCCT was lower in the AMD group than in the control group (P < 0.05). The HLT was significantly higher in the AMD group than in the control group at the Tparafovea and T-perifovea in the total population (P < 0.05) and in the ≤70-year subgroup (P < 0.05). The CT was higher in the AMD group than in the control group, particularly at the N-perifovea, T-perifovea, and T-parafovea in the ≤70-year subgroup; Interestingly, it was lower in the AMD group than in the control group at the Nparafovea, N-fovea, foveola, and T-fovea in the >70-year subgroup (P < 0.05).

CONCLUSION

This novel AI-based auto-measurement was more accurate, efficient, and detailed than manual measurements. SLCCT thinning was observed in wet AMD; however, CT changes depended on the interaction between HLT compensatory thickening and SLCCT thinning.

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Introduction

The application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.

Methods

We conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.

Results

Ninety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.

Discussion

Algorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

Corneal Epithelial Thickness Mapping: A Major Review

The corneal epithelium (CE) is the outermost layer of the cornea with constant turnover, relative stability, remarkable plasticity, and compensatory properties to mask alterations in the underlying stroma. The advent of quantitative imaging modalities capable of producing epithelial thickness mapping (ETM) has made it possible to characterize better the different patterns of epithelial remodeling. In this comprehensive synthesis, we reviewed all available data on ETM with different methods, including very high-frequency ultrasound (VHF-US) and spectral-domain optical coherence tomography (SD-OCT) in normal individuals, corneal or systemic diseases, and corneal surgical scenarios. We excluded OCT studies that manually measured the corneal epithelial thickness (CET) (e.g., by digital calipers) or the CE (e.g., by confocal scanning or handheld pachymeters). A comparison of different CET measuring technologies and devices capable of producing thickness maps is provided. Normative data on CET and the possible effects of gender, aging, diurnal changes, refraction, and intraocular pressure are discussed. We also reviewed ETM data in several corneal disorders, including keratoconus, corneal dystrophies, recurrent epithelial erosion, herpes keratitis, keratoplasty, bullous keratopathy, carcinoma in situ, pterygium, and limbal stem cell deficiency. The available data on the potential role of ETM in indicating refractive surgeries, planning the procedure, and assessing postoperative changes are reviewed. Alterations in ETM in systemic and ocular conditions such as eyelid abnormalities and dry eye disease and the effects of contact lenses, topical medications, and cataract surgery on the ETM profile are discussed.

Biomechanics of keratoconus: Two numerical studies

BACKGROUND

The steep cornea in keratoconus can greatly impair eyesight. The etiology of keratoconus remains unclear but early injury that weakens the corneal stromal architecture has been implicated. To explore keratoconus mechanics, we conducted two numerical simulation studies.

METHODS

A finite-element model describing the five corneal layers and the heterogeneous mechanical behaviors of the ground substance and lamellar collagen-fiber architecture in the anterior and posterior stroma was developed using the Holzapfel-Gasser-Ogden constitutive model. The geometry was from a healthy subject. Its stroma was divided into anterior, middle, and posterior layers to assess the effect of changing regional mechanical parameters on corneal displacement and maximum principal stress under intraocular pressure. Specifically, the effect of softening an inferocentral corneal button, the collagen-based tissues throughout the whole cornea, or specific stromal layers in the button was examined. The effect of simply disorganizing the orthogonally-oriented posterior stromal fibers in the button was also assessed. The healthy cornea was also subjected to eye rubbing-like loading to identify the corneal layer(s) that experienced the most tensional stress.

RESULTS

Conical deformation and corneal thinning emerged when the corneal button or the mid-posterior stroma of the button underwent gradual softening or when the collagen fibers in the mid-posterior stroma of the button were dispersed. Softening the anterior layers of the button or the whole cornea did not evoke conical deformation. Button softening greatly increased and disrupted the stress on Bowman's membrane while mid-posterior stromal softening increased stress in the anterior layers. Eye rubbing profoundly stressed the deep posterior stroma while other layers were negligibly affected.

DISCUSSION

These observations suggest that keratoconus could be initiated, at least partly, by mechanical instability/damage in the mid-posterior stroma that then imposes stress on the anterior layers. This may explain why subclinical keratoconus is marked by posterior but not anterior elevation on videokeratoscopy.