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Purpose: There is currently no consensus on which keratoconus need cross-linking nor on how to establish progression. This study assessed the performance of diverse progression criteria and compared them with our clinical knowledge of keratoconus evolution., Methods: This was a retrospective, longitudinal, observational study. Habitual progression criteria, based on (combinations of) keratometry (K MAX ), front astigmatism (A F ), pachymetry (P MIN ), or ABCD progression display, from 906 keratoconus patients were analyzed. For each criterion and cutoff, we calculated %eyes flagged progressive at some point (R PROG ), individual consistency C IND (%examinations after progression detection still considered progressive), and population consistency C POP (% eyes with CIND >66%). Finally, other monotonic and consistent variables, such as front steep keratometry (K 2F ), mean radius of the back surface (R mB ), and the like, were evaluated for the overall sample and subgroups., Results: Using a single criterion (e.g., ∆K MAX >1D) led to high values of R PROG . When combining two, (K MAX and A F ) led to worse C POP and higher variability than (K MAX and P MIN ); alternative criteria such as (K 2F and R mB ) obtained the best C POP and the lowest variability ( P <0.0001). ABC, as defined by its authors, obtained R PROG of 74.2%. Using wider 95% confidence intervals (95% CIs) and requiring two parameters over 95CI reduced R PROG to 27.9%., Conclusion: Previous clinical studies suggest that 20% to 30% of keratoconus cases are progressive. This clinical R PROG value should be considered when defining KC progression to avoid overtreatment. Using combinations of variables or wider margins for ABC brings R PROG closer to these clinical observations while obtaining better population consistency than current definitions., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2023 Contact Lens Association of Ophthalmologists.)

Purpose: To establish a numerical spectral-domain optical coherence tomography (SD-OCT)-based keratoconus (KC) staging system and compare it with existing KC staging systems., Setting: Eye Hospital of Wenzhou Medical University, Wenzhou, China., Designs: Retrospective case-control study., Methods: Scheimpflug tomography, air-puff tonometry, and SD-OCT were performed on 236 normal and 331 KC eyes. All SD-OCT-derived parameters of the corneal epithelium and stroma were evaluated based on their receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity to discriminate between normal and KC eyes. The best performing parameters were subsequently used to create an OCT-based staging system, which was compared with existing tomographic and biomechanical staging systems., Results: 236 eyes from 236 normal patients and 331 eyes from 331 KC patients of different stages were included. The highest ranked AUC ROC SD-OCT parameters, derived from stroma and epithelium, were stroma overall minimum thickness (ST: AUC 0.836, sensitivity 90%, specificity 67%) and epithelium overall SD (EP: AUC 0.835, sensitivity 75%, specificity 78%). A numerical SD-OCT staging system called STEP including 2 parameters-"ST" and "EP"-with 5 stages was proposed., Conclusions: The new SD-OCT-based KC staging system is the first to take the epithelium with its sublayer stroma information into account, showing a strong agreement to the existing staging systems. This system could be incorporated into daily practice, potentially leading to an overall improvement in KC treatment and follow-up management., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of ASCRS and ESCRS.)

Purpose: To determine whether combinations of devices with different measuring principles, supported by artificial intelligence (AI), can improve the diagnosis of keratoconus (KC)., Methods: Scheimpflug tomography, spectral-domain optical coherence tomography (SD-OCT), and air-puff tonometry were performed in all eyes. The most relevant machine-derived parameters to diagnose KC were determined using feature selection. The normal and forme fruste KC (FFKC) eyes were divided into training and validation datasets. The selected features from a single device or different combinations of devices were used to develop models based on random forest (RF) or neural networks (NN) trained to distinguish FFKC from normal eyes. The accuracy was determined using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity., Results: 271 normal eyes, 84 FFKC eyes, 85 early KC eyes, and 159 advanced KC eyes were included. A total of 14 models were built. Air-puff tonometry had the highest AUC for detecting FFKC using a single device (AUC = 0.801). Among all two-device combinations, the highest AUC was accomplished using RF applied to selected features from SD-OCT and air-puff tonometry (AUC = 0.902), followed by the three-device combination with RF (AUC = 0.871) with the best accuracy., Conclusion: Existing parameters can precisely diagnose early and advanced KC, but their diagnostic ability for FFKC could be optimized. Applying an AI algorithm to a combination of air-puff tonometry with Scheimpflug tomography or SD-OCT could improve FFKC diagnostic ability. The improvement in diagnostic ability by combining three devices is modest., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

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., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 Contact Lens Association of Ophthalmologists.)

Purpose: To present a descriptive system for the elliptic demarcation area seen in keratoconus densitometry maps (obtained with a Scheimpflug tomographer) and to evaluate its suitability for clinical practice., Methods: The subjects were 30 keratoconus patients at different stages and 20 healthy subjects. The averaged densitometry maps ('two-layers' scan, with fixed layers 120 µm and endothelium) were analysed using a system of four categories (termed 'Brightness', 'Contrast', 'Decentration' and 'Octants surrounded by a dark line') that we created to characterise the demarcation area. Four examiners (three corneal specialists and one junior resident) used the system to classify the maps. The inter-rater agreement was calculated for two subgroups: (1) clinical keratoconus patients and (2) both healthy patients and forme fruste keratoconus patients. Intra-rater agreement was also determined., Results: Inter-rater agreement on classification was higher when analysing clinical keratoconus, reaching levels of substantial agreement. Despite this, only low levels of agreement were found in 'Decentration', penalized due to the skewness in the distribution of this descriptor. Almost perfect intra-rater agreement was obtained for all descriptors in the first subgroup of clinical keratoconus, whereas the agreement was generally moderate within the second subgroup of normal and forme fruste eyes. Agreement was slightly lower with the less experienced observer. At least three observers agreed on four forme fruste keratoconus eyes presenting abnormalities in the images. The observers reported that the 'Brightness' descriptor was subjective and redundant with 'Contrast'., Conclusions: The description of the area was repeatable and reproducible, and may be a valuable supplement when documenting clinical keratoconus stage and progression in daily practice. However, a minor learning curve was noticed and agreement was higher among the more experienced observers. Since the descriptor 'Brightness' was found to be subjective and redundant, it was excluded from the final proposed classification., (© 2021 The Authors Ophthalmic and Physiological Optics © 2021 The College of Optometrists.)

Purpose: To present the baseline data for a large cohort of patients with keratoconus enrolled in the Retrospective Digital Computer Analysis of Keratoconus Evolution (REDCAKE) study., Methods: Eight centers contributed the Scheimpflug tomographical data for 906 patients with keratoconus, 743 measured with a Pentacam and 163 with a Galilei. The stage of keratoconus at baseline, the location of the reference points, minimum pachymetry (Pmin), and maximum keratometry (Kmax) were analyzed. The intereye asymmetry was evaluated for Kmax (anterior and posterior), Pmin, and keratoconus stage. Average maps and elevation profiles were calculated for each degree of keratoconus., Results: Keratoconus was more frequently diagnosed in men (73%) than in women (27%). At baseline, 500/1155 eyes (43%) presented with moderate to severe changes in the posterior surface, whereas moderate/severe changes were only found in 252 and 63 eyes when evaluating anterior surface and pachymetry, respectively. The location of Pmin was usually inferotemporal (94% OD and 94% OS), while the location of Kmax showed more variability and significantly higher distance from apex (P < 0.05). The keratoconus presentation was chiefly asymmetric for all the parameters studied. Clear differences between stages could be identified in the maps and elevation profiles., Conclusions: The staging map set presented can be used as a graphical guidance to classify keratoconus stage. Keratoconus presented asymmetrically, and generally the posterior surface was more affected than the anterior surface or the thickness. Asymmetry is playing a role in KC detection. Although Pmin was almost invariably located inferotemporally, Kmax location showed higher variability and distance from the apex., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: To report an observation made while performing Scheimpflug densitometry analysis on the corneal region affected in keratoconus (KC) that seems to delineate the base of the cone., Methods: Scheimpflug densitometries of 20 healthy subjects and 90 patients with KC were examined. Corneal densitometry was analyzed using both "1-layer" and "2-layer" approaches. The first considers the corneal transparency layer by layer at different depths, whereas the second averages densitometry between 2 corneal layers selected by the examiner. Fixed layers, 120 μm depth, and endothelium were selected. Repeated same-day scans and longitudinal series of scans were also evaluated to see whether the findings evolved over time., Results: Eighty-eight of 90 KC cases displayed a bright area on the densitometry map that corresponded to the cone location. The area's characteristics, such as its brightness, contrast, and the presence of a delimiting arc correlated with KC severity and was more noticeable in advanced cases. No similar marks were found in any of the normal subjects. The shape, location, and extent of the mark were consistent over consecutive measures taken on the same day. Changes over time were also seen in eyes with known clinical progression but was also seen in eyes considered clinically stable., Conclusions: The densitometry mark seems to correspond with the zone most affected by KC and could be a supplementary tool for documenting KC stage, alongside conventional parameters. Further studies are required to ascertain whether it could prove useful in KC detection, to determine progression, and to relate it to corneal biomechanical behavior., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods., Methods: Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 after refractive surgery (PR), and 194 normal eyes (N). Twenty-two parameters were used for classification using a support vector machine algorithm developed in Weka, a machine-learning computer software. The cross-validation accuracy for 3 different classification tasks (KC vs. N, FF vs. N and all 5 groups) was calculated and compared with other known classification methods., Results: The accuracy achieved in the KC versus N discrimination task was 98.9%, with 99.1% sensitivity and 98.5% specificity for KC detection. The accuracy in the FF versus N task was 93.1%, with 79.1% sensitivity and 97.9% specificity for the FF discrimination. Finally, for the 5-groups classification, the accuracy was 88.8%, with a weighted average sensitivity of 89.0% and specificity of 95.2%., Conclusions: Despite using the strictest definition for FF KC, the present study obtained comparable or better results than the single-parameter methods and indices reported in the literature. In some cases, direct comparisons with the literature were not possible because of differences in the compositions and definitions of the study groups, especially the FF KC.

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. [ABSTRACT FROM AUTHOR]