The cornea is the anterior, convex part of the eyeball and plays a critical role in the optical system of the human eye. Keratoconus is the most common form of corneal ectasia, typically manifesting in the second or third decade of life. It is a progressive, bilateral, and asymmetrical condition characterized by thinning and protrusion of the cornea, which leads to increased refractive errors and high-grade irregular astigmatism. As a result, affected individuals often experience significant visual impairment that cannot be adequately corrected with glasses.
Recent studies suggest that keratoconus has an inflammatory component and is associated with allergic or atopic conditions, a positive family history, as well as genetic and environmental factors. Chronic eye rubbing, often due to itching, may further contribute to biomechanical weakening of the cornea.
As the disease progresses, central or paracentral corneal thinning and steepening become more pronounced. Given the cornea’s central role in ocular optics, these changes cause irregular astigmatism and high-order aberrations, leading to a substantial reduction in visual quality. While typically bilateral, the disease progresses asymmetrically and may continue until the end of the third decade of life. A recent meta-analysis covering over 50 million individuals from 15 countries estimated the global prevalence of keratoconus at 138 cases per 100,000 people1. However, in some populations, prevalence rates may reach as high as 4.79%2. This variability is likely due to differences in diagnostic criteria, classification systems, study methodologies, and the imaging technologies used.
Keratoconus is characterized by physicochemical alterations in the corneal tissue, including changes in its viscoelastic and biomechanical properties. Disease progression is associated with disruption of the collagen lamellae that comprise the corneal extracellular matrix. These structural changes contribute to the characteristic thinning and protrusion of the cornea.
A key contributing factor is mechanical eye rubbing, which can trigger the release of multiple proinflammatory cytokines (IL-1β, IL-4, IL-5, IL-6, IL-8, IL-13, IL-17, TNF-α, IFN-γ) from the corneal epithelium. This cytokine cascade enhances the activity of tissue-degrading enzymes, particularly matrix metalloproteinase-9 (MMP-9), and reduces the expression of lysyl oxidase (LOX)—an essential enzyme involved in the formation of collagen cross-links. The combined effect of increased proteolysis and impaired collagen synthesis leads to progressive weakening of the corneal stroma.
Diagnosis of corneal ectasia is based on a combination of clinical evaluation, including medical history, refraction testing, slit-lamp examination, and advanced imaging techniques. Corneal topography and anterior segment optical coherence tomography (OCT) are particularly useful for assessing disease severity and progression. Additionally, changes in corneal biomechanics—such as those measured by dynamic Scheimpflug imaging—have emerged as valuable diagnostic markers, allowing earlier detection of subclinical keratoconus. Early diagnosis is essential, as it enables timely intervention with treatments such as corneal collagen cross-linking, which can stabilize the disease and prevent further visual deterioration.
The diagnosis of keratoconus is based on clinical examination and imaging techniques. During slit-lamp evaluation, clinicians should assess for characteristic signs such as corneal protrusion, scissors reflex, localized thinning, prominent corneal nerve fibers, Charleux’s oil droplet reflex, Fleischer’s ring, and Vogt’s striae3,4. While these features are useful in identifying advanced disease, their presence and severity do not always correlate consistently with disease progression. Early diagnosis and monitoring of keratoconus are critical for timely therapeutic intervention. In particular, detecting progression at an early stage enables the application of corneal collagen cross-linking, which can halt or slow disease advancement. Delayed diagnosis, on the other hand, often limits treatment options to corneal transplantation in cases of scarring or severe thinning, where keratoplasty may remain the only viable solution5. This challenge is especially pronounced in low-resource settings, where early detection is hindered by limited access to diagnostic technologies and a shortage of corneal donor tissue5.
In cases where keratoconus is diagnosed at an advanced stage, corneal transplantation may remain the only viable treatment option. This procedure involves prolonged waiting times for donor tissue, more complex surgical intervention, and the need for long-term—often lifelong—immunosuppressive therapy. Although still under active investigation, the viscoelastic properties of the cornea play a critical role in the diagnosis and management of corneal ectatic disorders. These biomechanical parameters are also valuable in preoperative screening for refractive surgery, treatment planning, outcome prediction, and risk assessment for postoperative complications.
Currently, early keratoconus diagnosis is primarily based on corneal topography, supplemented by pachymetry and aberrometry. These imaging techniques allow for the detection of subtle morphological abnormalities in corneal shape and curvature, even before clinical signs appear3,6. However, the major diagnostic challenge lies in identifying the most sensitive and specific parameters capable of detecting subclinical keratoconus—particularly when topographic patterns appear physiologically normal3,7.
Currently, the gold standard for screening, diagnosing, and assessing keratoconus is corneal topography and tomography. One of the most widely used diagnostic tools is the Pentacam, which utilizes a rotating Scheimpflug camera system. During the imaging process, the anterior segment of the eye is illuminated with monochromatic blue slit light, allowing the device to capture a sequence of cross-sectional images. These images are then reconstructed into a high-resolution three-dimensional model of the cornea. This enables detailed analysis of both anterior and posterior corneal surfaces, as well as the generation of pachymetric maps essential for evaluating corneal thickness distribution and detecting early ectatic changes.
Topographic imaging systems can be broadly classified based on their operating principle into two categories: reflection-based systems and slit-light projection systems8. Reflection-based systems assess anterior corneal topography by analyzing the reflection of Placido’s rings on the corneal surface8. These systems are particularly effective at detecting localized steepening of the anterior cornea, a hallmark feature of early-stage keratoconus5. However, they are limited by relatively low measurement repeatability and their inability to evaluate the posterior corneal surface3,5,8. In contrast, slit-light projection systems overcome these limitations by providing detailed imaging of both the anterior and posterior corneal surfaces. A unique variation of reflection-based systems—using multicolor light-emitting diodes (LEDs)—is represented by the Cassini Color LED Corneal Analyzer (i-Optics, The Netherlands), which enables more precise analysis of corneal curvature8. Slit-light-based systems operate using a two-step approach: projection of Placido rings followed by slit-light scanning. This enables comprehensive imaging of the entire anterior segment, including the posterior corneal curvature, which is often altered even in the earliest stages of keratoconus3,8. The implementation of such technologies has enabled the development of advanced diagnostic algorithms, such as the Belin/Ambrósio Enhanced Ectasia Display (BAD-D) available in the Pentacam (Oculus), and the Corneal Objective Risk of Ectasia Screening (CORE) developed for the Orbscan (Bausch & Lomb)5,9,10. These tools integrate multiple corneal parameters—such as posterior asphericity, anterior curvature, and pachymetric progression—into a comprehensive risk profile, revolutionizing the early diagnosis of keratoconus.
The Scheimpflug imaging technique, when combined with Placido disc analysis, enables the measurement and evaluation of up to 138,000 real elevation points, offering one of the highest-resolution assessments of the anterior segment. Each measurement generates a full cross-sectional scan of the corneal surface. Key advantages of rotational Scheimpflug imaging include precise central corneal measurement, correction for eye movements, straightforward patient positioning, and a rapid acquisition time—typically under two seconds.
The demand for early detection of keratoconus, particularly in its subclinical stages, has driven interest in alternative technologies capable of revealing subtle structural changes. One such approach is high-resolution ultrasound pachymetry, which enables the generation of detailed stromal and epithelial thickness maps. Notably, epithelial thickness profiling has been reported as the only method with 100% sensitivity for detecting preclinical keratoconus5,11. This technique identifies specific epithelial remodeling patterns—central thinning and compensatory peripheral thickening in an annular distribution—that are characteristic of early disease5,11.
Another promising modality is anterior segment optical coherence tomography (AS-OCT), which provides high-resolution, cross-sectional imaging of all corneal layers12. Compared to elevation-based maps, AS-OCT offers true 3D visualization of the cornea and enables reliable measurement of epithelial thickness, both centrally and peripherally—approaching the accuracy of high-resolution ultrasound5,10,11,12,13. These combined capabilities make AS-OCT a valuable tool in the detection of subclinical and early-stage keratoconus.
One important parameter that cannot be measured using standard non-invasive imaging techniques is corneal hysteresis5. This metric reflects the viscoelastic response of the cornea to mechanical stress—typically assessed through air-puff tonometry—and represents the difference between corneal deformation and recovery14,15. Reduced corneal hysteresis has been associated with biomechanical weakening and may serve as an early indicator of ectatic changes. The analysis of corneal biomechanics, including hysteresis, provides complementary data that can enhance and support the findings obtained from topography, tomography, and pachymetry5,14. Incorporating such biomechanical metrics into the diagnostic process may improve sensitivity for detecting subclinical keratoconus and monitoring its progression.
Recent studies from the past five years (2020–2025) highlight the increasing role of machine learning (ML) in the early detection and classification of keratoconus. A recent review16 emphasizes the potential of ML algorithms to improve early-stage diagnosis, while other works17 compare different classification techniques, illustrating the diagnostic gains possible with automated models. Various data sources have been used to build such models, including spectral-domain optical coherence tomography (SD-OCT), ultra-high-resolution OCT, air-puff tonometry, and Scheimpflug imaging18,19,20. Machine learning models have shown promising results in differentiating keratoconic from normal corneas19,21. However, many algorithms still struggle to identify form fruste keratoconus (FFKC), a very early and subtle stage of the disease19. Notably, Yang et al.20 report a model capable of successfully distinguishing FFKC from physiological corneas. A recent systematic review and meta-analysis by Bodmer et al.22 concluded that the overall diagnostic performance of deep learning models for keratoconus detection is strong, although many studies suffer from methodological limitations. Other detailed investigations23,24,25,26 further evaluate the performance of both classical ML algorithms and deep neural networks. For example, study23 demonstrated the high accuracy (up to 98%) of the Random Forest classifier in distinguishing normal eyes from those with keratoconus, as well as in grading disease severity. Subsequent research24 explored the use of convolutional neural networks (CNNs) and autoencoders to augment datasets and improve diagnostic accuracy. In a recent multicentre study, CNNs achieved 97.8% accuracy in detecting and grading keratoconus using colour-coded maps generated by Scheimpflug imaging, based on axial, elevation, and pachymetric data27. A hybrid approach25 combining CNNs with traditional ML techniques further improved performance in classifying corneal topographic patterns. Lastly, study26 demonstrated that complex deep learning models could effectively detect keratoconus based on subtle morphological changes in the corneal endothelium, achieving high scores across multiple performance metrics. These developments underscore the potential of ML and deep learning in improving keratoconus diagnostics. However, additional work is needed to compare algorithms using structured, interpretable data, particularly from widely used tools like the Pentacam. This motivates the present study, which aims to evaluate and compare several machine learning classifiers trained on topographic and biomechanical parameters obtained from the Pentacam device. A special focus is placed on identifying clinically relevant features and assessing their overlap with expert-defined diagnostic indicators.
The aim of this study was to differentiate healthy corneas from those affected by keratoconus using structured data obtained from the Pentacam device and a set of supervised machine learning algorithms. The analysis focused on evaluating the diagnostic performance of multiple classifiers across different groups of topographic and biomechanical parameters. An additional goal was to identify the most informative features contributing to classification performance and compare them with diagnostic indicators routinely used by clinicians, thereby improving model interpretability and clinical relevance.
