The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.

Early and accurate detection of keratoconus progression is particularly important for the prudent, cost-effective use of corneal cross-linking and judicious timing of clinical follow-up visits. The aim of this study was to verify whether a progression could be predicted based on two prior tomography measurements and to verify the accuracy of the system when labelling the eye as stable or suspect progressive. Data from 743 patients measured by Pentacam (Oculus, Wetzlar, Germany) were available, and they were filtered and preprocessed to data quality needs. The time delay neural network received six features as input, measured in two consecutive examinations, predicted the future values, and determined the classification (stable or suspect progressive) based on the significance of the change from the baseline. The system showed a sensitivity of 70.8% and a specificity of 80.6%. On average, the positive and negative predictive values were 71.4% and 80.2%. Including data of less quality (as defined by the software) did not significantly worsen the results. This predictive system constitutes another step towards a personalized management of keratoconus. While the results obtained were modest and perhaps insufficient to decide on a surgical procedure, such as cross-linking, they may be useful to customize the timing for the patient’s next follow-up.

Objectives: To identify the most suitable parameters to describe keratoconus progression. Methods: Longitudinal retrospective cohort study. Monotonicity and consistency of over 250 parameters extracted from the Pentacam tomographies (Oculus, Germany) of 743 patients was analyzed. Repeatability was calculated for 69 patients (published elsewhere). The parameters were scored based on their performance for each desired feature and a global ranking was created. Results: Overall, parameters that average a higher number of corneal points performed better than single-point parameters. Zonal keratometries on areas surrounding the maximum curvature point and the steepest front keratometry performed best, followed by front best-fit sphere and mean keratometry of both surfaces. Platform-dependent indices (e.g., Belin-Ambrósio Deviation- or index height decentration-) obtained good scores, but platform-independent LOGIK performed slightly better. Finally, although minimum radius in both surfaces worked competently, minimum pachymetry (PachyMin) performed considerably poorer. Conclusions: We presented a list of parameters whose behavior was repeatable, monotonic and consistent, features desirable to describe change. The parameters normally used to follow keratoconus progression may not be the most adequate, as evidenced by the poor performance of PachyMin. Although calculated for a specific Scheimpflug device, most of the best-performing parameters are platform-independent variables, and results may be generalized, pending validation.