Keratoconus

The cornea, as a dynamic and responsive tissue, constantly interacts with mechanical forces in order to maintain its structural integrity, barrier function, transparency and refractive power. Cells within the cornea sense and respond to various mechanical forces that fundamentally regulate their morphology and fate in development, homeostasis and pathophysiology. Corneal cells also dynamically regulate their extracellular matrix (ECM) with ensuing cell-ECM crosstalk as the matrix serves as a dynamic signaling reservoir providing biophysical and biochemical cues to corneal cells. Here we provide an overview of mechanotransduction signaling pathways then delve into the recent advances in corneal mechanobiology, focusing on the interplay between mechanical forces and responses of the corneal epithelial, stromal, and endothelial cells. We also identify species-specific differences in corneal biomechanics and mechanotransduction to facilitate identification of optimal animal models to study corneal wound healing, disease, and novel therapeutic interventions. Finally, we identify key knowledge gaps and therapeutic opportunities in corneal mechanobiology that are pressing for the research community to address especially pertinent within the domains of limbal stem cell deficiency, keratoconus and Fuchs' endothelial corneal dystrophy. By furthering our understanding corneal mechanobiology, we can contextualize discoveries regarding corneal diseases as well as innovative treatments for them.

PurposeCorneal biomechanics is an emerging field and the interest into physical and biological interrelations in the anterior part of the eye has significantly increased during the past years. There are many factors that determine corneal biomechanics such as hormonal fluctuations, hydration and environmental factors. Other factors that can affect the corneas are the age, the intraocular pressure and the central corneal thickness. The purpose of this review is to evaluate the factors affecting corneal biomechanics and the recent advancements in non-destructive, in vivo measurement techniques for early detection and improved management of corneal diseases.MethodsUntil recently, corneal biomechanics could not be directly assessed in humans and were instead inferred from geometrical cornea analysis and ex vivo biomechanical testing. The current research has made strides in studying and creating non-destructive and contactless techniques to measure the biomechanical properties of the cornea in vivo.ResultsResearch has indicated that altered corneal biomechanics contribute to diseases such as keratoconus and glaucoma. The identification of pathological corneas through the new measurement techniques is imperative for preventing postoperative complications.ConclusionsIdentification of pathological corneas is crucial for the prevention of postoperative complications. Therefore, a better understanding of corneal biomechanics will lead to earlier diagnosis of ectatic disorders, improve current refractive surgeries and allow for a better postoperative treatment.

BackgroundThe diagnosis of keratoconus, as the most prevalent corneal ectatic disorder, at the subclinical stage gained great attention due to the increased acceptance of refractive surgeries. This study aimed to assess the pattern of the corneal biomechanical properties derived from Corneal Visualization Scheimpflug Technology (Corvis ST) and evaluate the diagnostic value of these parameters in distinguishing subclinical keratoconus (SKC) from normal eyes.MethodsThis prospective study was conducted on 73 SKC and 69 normal eyes. Subclinical keratoconus eyes were defined as corneas with no clinical evidence of keratoconus and suspicious topographic and tomographic features. Following a complete ophthalmic examination, topographic and tomographic corneal assessment via Pentacam HR, and corneal biomechanical evaluation utilizing Corvis ST were done.ResultsSubclinical keratoconus eyes presented significantly higher Deformation Amplitude (DA) ratio, Tomographic Biomechanical Index (TBI), and Corvis Biomechanical Index (CBI) rates than the control group. Conversely, Ambrósio Relational Thickness to the Horizontal profile (ARTh), and Stiffness Parameter at the first Applanation (SPA1) showed significantly lower rates in SKC eyes. In diagnosing SKC from normal eyes, TBI (AUC: 0.858, Cut-off value: > 0.33, Youden index: 0.55), ARTh (AUC: 0.813, Cut-off value: ≤ 488.1, Youden index: 0.58), and CBI (AUC: 0.804, Cut-off value: > 0.47, Youden index: 0.49) appeared as good indicators.ConclusionsTBI, CBI, and ARTh parameters could be valuable in distinguishing SKC eyes from normal ones.

Keratoconus is a progressive corneal disease that requires early and accurate detection to prevent severe visual impairment. This study presents a deep learning-based classification model for distinguishing between healthy and keratoconic eyes using dynamic corneal imaging data from the CORVIS system. A hybrid CNN-RNN architecture was developed, combining a fine-tuned InceptionV3 network for spatial feature extraction with a recurrent LSTM module to capture temporal patterns across image sequences. To ensure robust evaluation, a 10-fold stratified cross-validation strategy was employed, with data splits performed at the patient level to avoid data leakage. The model achieved an average accuracy, precision, recall, and F1-score of approximately 0.90 across folds, demonstrating strong generalization performance. Boxplot visualizations of metric distributions further confirmed model stability and revealed minimal performance variance. Class-wise analysis showed high effectiveness in detecting both healthy and keratoconic cases, although slightly greater variability was observed in the classification of healthy eyes. These results indicate that the proposed method is a promising tool for keratoconus screening and may complement existing diagnostic workflows. Further validation on external datasets is recommended prior to clinical deployment.