Subclinical Keratoconus Detection and Characterization Using Motion-Tracking Brillouin Microscopy.

PURPOSE: To characterize focal biomechanical alterations in subclinical keratoconus (SKC) using motion-tracking (MT) Brillouin microscopy and evaluate the ability of MT Brillouin metrics to differentiate eyes with SKC from normal control eyes. DESIGN: Prospective cross-sectional study. PARTICIPANTS: Thirty eyes from 30 patients were evaluated, including 15 eyes from 15 bilaterally normal patients and 15 eyes with SKC from 15 patients. METHODS: All patients underwent Scheimpflug tomography and MT Brillouin microscopy using a custom-built device. Mean and minimum MT Brillouin values within the anterior plateau region and anterior 150 µm were generated. Scheimpflug metrics evaluated included inferior-superior (IS) value, maximum keratometry (Kmax), thinnest corneal thickness, asymmetry indices, Belin/Ambrosio display total deviation, and Ambrosio relational thickness. Receiver operating characteristic (ROC) curves were generated for all Scheimpflug and MT Brillouin metrics evaluated to determine the area under the ROC curve (AUC), sensitivity, and specificity for each variable. MAIN OUTCOME MEASURES: Discriminative performance based on AUC, sensitivity, and specificity. RESULTS: No significant differences were found between groups for age, sex, manifest refraction spherical equivalent, corrected distance visual acuity, Kmax, or KISA% index. Among Scheimpflug metrics, significant differences were found between groups for thinnest corneal thickness (556 µm vs. 522 µm; P < 0.001), IS value (0.29 diopter [D] vs. 1.05 D; P < 0.001), index of vertical asymmetry (IVA; 0.10 vs. 0.19; P < 0.001), and keratoconus index (1.01 vs. 1.05; P < 0.001), and no significant differences were found for any other Scheimpflug metric. Among MT Brillouin metrics, clear differences were found between control eyes and eyes with SKC for mean plateau (5.71 GHz vs. 5.68 GHz; P < 0.0001), minimum plateau (5.69 GHz vs. 5.65 GHz; P < 0.0001), mean anterior 150 µm (5.72 GHz vs. 5.68 GHz; P < 0.0001), and minimum anterior 150 µm (5.70 GHz vs. 5.66 GHz; P < 0.001). All MT Brillouin plateau and anterior 150 µm mean and minimum metrics fully differentiated groups (AUC, 1.0 for each), whereas the best performing Scheimpflug metrics were keratoconus index (AUC, 0.91), IS value (AUC, 0.89), and IVA (AUC, 0.88). CONCLUSIONS: Motion-tracking Brillouin microscopy metrics effectively characterize focal corneal biomechanical alterations in eyes with SKC and clearly differentiated these eyes from control eyes, including eyes that were not differentiated accurately using Scheimpflug metrics. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Biomechanical properties analysis of forme fruste keratoconus and subclinical keratoconus.

PURPOSE: To analyze the biomechanical properties of the eye in patients with unilateral keratoconus with normal (forme fruste keratoconus [FFKC]) or abnormal topography (subclinical keratoconus [SKC]). METHODS: This study included 153 eyes of 153 participants, including 95 eyes of patients with unilateral keratoconus, and 58 eyes of 58 healthy controls. Contralateral eyes with unilateral keratoconus were divided into two groups according to clinical manifestations and global consensus: FFKC (n = 30) and SKC (n = 65). The biomechanical characteristics were analyzed using non-parametric tests; further analysis thereof was performed after adjusting for confounding factors (i.e., intraocular pressure, age, and corneal thickness). Receiver operating characteristic curve (ROC) was used to analyze the ability of the biomechanical parameters to distinguish FFKC from SKC. RESULTS: Statistically significant differences between the FFKC and SKC groups were found in 9 of the 18 corneal biomechanical parameters analyzed using non-parametric tests. After adjusting for confounding factors, the multivariate analysis still revealed significant statistical differences in A1-time (P = 0.017), integrated radius (IR) (P = 0.024), and tomographic and biomechanical index (TBI, P < 0.001) between the FFKC and SKC groups. Stiffness parameter at first applanation (SP-A1) (Area under ROC [AUROC] = 0.765) demonstrated the strongest distinguishing ability, except for TBI (AUROC = 0.858) and Corvis Biomechanical Index (AUROC = 0.849), however, there was no statistically significant difference in SP-A1 (P = 0.366) between FFKC and SKC. CONCLUSIONS: Biomechanical parameters A1-time and IR have a high diversity between FFKC and SKC, besides TBI, and may reflect more subtle changes in corneal biomechanical properties (BPs) preceding SP-A1. The BPs of SKC are weaker than FFKC, which might be a basic and clue for the classification and diagnosis of the severity of early keratoconus in terms of biomechanics.

Corneal biomechanics in normal and subclinical keratoconus eyes.

BACKGROUND: The 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. METHODS: This 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. RESULTS: Subclinical 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. CONCLUSIONS: TBI, CBI, and ARTh parameters could be valuable in distinguishing SKC eyes from normal ones.

Evaluation of corneal topography, pachymetry and higher order aberrations for detecting subclinical keratoconus.

PURPOSE: To compare corneal topography, pachymetry and higher order aberrations in keratoconic and normal eyes; to investigate their association in keratoconic eyes; and to determine their diagnostic ability for detecting subclinical keratoconus in a Nepalese population. METHODS: Ninety-six eyes of 48 keratoconus patients and 50 normal eyes of 50 control subjects were included in this study. The eyes of keratoconus patients were classified into four different study groups: subclinical, stage 1, stage 2 and advanced stage keratoconus. In each eye, corneal topography, pachymetry and corneal aberrometry indices were measured using a Sirius corneal tomographer. The study parameters of keratoconic eyes were compared with normal eyes, and the possible association of corneal aberrometry with topography and pachymetry indices was investigated. The area under curve (AUC) of receiver operating characteristic (ROC) curves along with optimal cutoff values with best sensitivity and specificity were also determined for each index to detect subclinical keratoconus. RESULTS: All the indices except average keratometry measurements (Kavg and mmavg ) and spherical aberration (SA) were found to be significantly different in subclinical keratoconus compared to the control group (p < 0.05). In keratoconic eyes, all corneal aberrations were significantly correlated with the topography and pachymetry indices (range of ρ: -0.25 to 0.96; all p < 0.05) except for trefoil and minimum corneal thickness (Thkmin ). All the indices except Kavg , mmavg and SA showed excellent diagnostic ability (AUC > 0.90) in detecting subclinical keratoconus. The cutoff values proposed for the asymmetry index of the corneal back surface (SIb ), Strehl ratio of point spread function (PSF), coma and Baiocchi-Calossi-Versaci index of corneal back surface (BCVb ) each showed excellent sensitivity (100%) and specificity (≥97%). CONCLUSIONS: Corneal higher order aberrations were found to be significantly elevated in subclinical keratoconus compared to healthy controls. SIb , PSF, coma and BCVb were identified as the most powerful Sirius indices for the detection of subclinical keratoconus.

Increased epithelial backscatter: A novel finding in subclinical and clinical keratoconus.

BACKGROUND: To assess alterations in backscatter from the corneal epithelium, anterior stroma and lens surface in eyes with subclinical, mild and moderate keratoconus (KC). METHODS: In this single-centre, cross-sectional study involving 24 eyes with subclinical KC, 107 eyes with manifest KC (mild = 40 and moderate = 67 eyes) and 90 controls, line densitometry was performed with Pentacam (Oculus Optikgeräte GmbH, Wetzlar, Germany) to obtain simultaneous backscatter values for the corneal epithelium, anterior stroma and anterior lens surface. Backscatter values and Pentacam parameters were used in subsequent statistical analyses. RESULTS: Eyes with subclinical, mild and moderate KC had similar epithelial and stromal backscatter (P > 0.05) that was significantly increased compared with the controls (P < 0.05). Although anterior lens surface backscatter did not differ between the control and KC groups (P > 0.05), it was significantly higher in the mild and moderate KC groups than in the subclinical KC group (P < 0.05). In the KC group (n = 131) epithelial backscatter was strongly correlated with stromal backscatter (r = 0.911, P < 0.0001). CONCLUSIONS: Increased epithelial backscatter and a strong correlation with anterior stromal backscatter in the KC groups were consistent with the epithelium-stroma interaction involved in KC pathogenesis. Single-point backscatter analysis can be used with point clouds to construct epithelial and stromal backscatter maps in Pentacam to aid the detection of KC as a novel feature.

Evaluation of topographic, tomographic, topometric, densitometric, and aberrometric features of cornea with pentacam HR system in subclinical keratoconus.

PURPOSE: To investigate topographic, tomographic, topometric, densitometric, and aberrometric parameters in subclinical keratoconus with the Pentacam HR imaging system. METHODS: Data of 3128 patients were evaluated, finding in 108 patients clinical keratoconus in one eye and subclinical keratoconus in the other. Corneal topographic, tomographic, topometric, densitometric, and aberrometric values obtained using the Pentacam HR imaging system were compared between clinical keratoconus, subclinical keratoconus, and normal eyes. RESULTS: Comparing eyes with subclinical keratoconus and the control group, while flat K, horizontal coma, horizontal trefoil, and vertical trefoil values were similar (p > 0.05 for each), all other parameters were significantly different (p < 0.05 for each). Densitometry values of eyes with subclinical keratoconus were significantly higher in all layers of the 0-2 mm annular area and in the anterior and central layers of the 2-6 mm annular area compared to the control group (p < 0.05 for each). According to the receiver operating characteristic curve analysis, the densitometry region with the largest area under the curve was the anterior layer of the 0-2 mm annular area. The sensitivity in this region was 79.4% and the specificity 73.2% in distinguishing eyes with subclinical keratoconus from normal eyes when 19.3 GSU was considered the threshold. CONCLUSION: Corneal densitometry values in the 0-2 and 2-6 mm annular areas, especially in the anterior layers, are parameters that can be used to predict and distinguish subclinical keratoconus from normal eyes.

Early diagnosis of subclinical keratoconus by wavefront parameters using Scheimpflug, Placido and Hartmann-Shack based devices.

PURPOSE: To compare the accuracy of three devices (Pentacam, Sirius and OPD-Scan III) to differentiate subclinical keratoconus from normal corneas by wavefront parameters. METHODS: Two hundred and seventeen patients were enrolled in three groups [68 normal, 79 subclinical keratoconus (SKCN) and 70 KCN eyes] in this prospective diagnostic test study. Wavefront indices were evaluated between the groups using Pentacam, Sirius and OPD-Scan III. The accuracy of the parameters was determined by measuring the area under the receiver operating characteristic curve (AUC) for each group. RESULTS: Front Baiocchi-Calossi-Versaci (BCV) index with Sirius (sensitivity = 87.7%, specificity = 83%, AUC = 0.887), front Vertical Coma (Z3-1) with Pentacam (sensitivity = 75%, specificity = 100%, AUC = 0.857) and Corneal Z3-1 with OPD-Scan III (sensitivity = 100%, specificity = 78.6%, AUC = 0.857) had the highest AUC values for the diagnosis of subclinical KCN. In the KCN group, the highest AUC values were obtained for front higher-order aberration (HOA), front/back Z3-1 and front Secondary Vertical Coma (Z5-1) with Pentacam (sensitivity = 100%, specificity = 100%, AUC = 1.00 for all three), front root mean square values per unit area (RMS/A), HOA, Residual HOA, BCV, RMS Trefoil and RMS Coma with Sirius (sensitivity = 100%, specificity = 100%, AUC = 1.00 for all) and Corneal HOA, RMS total Coma and Z3-1 with OPD-Scan III (sensitivity = 100%, specificity = 93%, AUC = 0.96 for all three). CONCLUSION: Corneal wavefront indices generated from different devices have acceptable validity for differentiating normal cornea from the early form of KCN, and this can be very useful for preoperative screening before refractive surgery. The front BCV with Sirius was the most accurate parameter for diagnosis of SKCN followed by Z3-1 with Pentacam and OPD-Scan III.

Corneal layer thickness in keratoconus using optical coherence tomography.

CLINICAL RELEVANCE: Accurate thickness measurement of corneal layers using anterior segment OCT can be used to improve visual outcomes. Understanding its applications is essential for optometric practices to enhance eye care procedures. BACKGROUND: To evaluate the thicknesses of different corneal layers for identifying keratoconus (KCN) and subclinical keratoconus (SKCN) using spectral-domain optical coherence tomography (SD-OCT). METHODS: This prospective study analyzed 60 eyes with KCN, 48 eyes with SKCN, and 53 normal eyes. The central corneal thickness (CCT) and thicknesses of the epithelium, Bowman, stroma, and Descemet-endothelium layers were measured using SD-OCT. One way analysis of variance and the area under the curve (AUC) were used to evaluate the parameters. The Delong method was used to compare AUCs. RESULTS: In KCN, CCT and thicknesses of epithelium, Bowman, stroma, and Descemet-endothelium layers were 495.5 ± 41.7, 52.6 ± 6.4,11.5 ± 1.4, 415.5 ± 38.9, and 12.3 ± 1.7 µm, respectively. These thickness values were respectively 524.5 ± 33.3, 56.8 ± 6.8, 11.5 ± 1.6, 439.8 ± 30.6, and 12.4 ± 1.7 µm in SKCN and 563.8 ± 37.9, 57.7 ± 6.9, 12.2 ± 1.6, 469.5 ± 33.7, and 12.8 ± 2.1µm in normal group. Total cornea and stroma in KCN and SKCN, and epithelium in KCN were significantly thinner compared to the normal group (P < 0.001). The highest AUC values were observed for CCT in KCN (AUC 0.90) and SKCN (AUC 0.782). The diagnostic accuracy was significantly higher for stromal thickness in KCN (sensitivity 81.7%, specificity 73.6%, AUC 0.871) and SKCN (sensitivity 80.0%, specificity 56.6%, AUC 0.751) than other individual corneal layers (Delong, P < 0.001) . CONCLUSION: CCT can accurately distinguish keratoconus from normal eyes. However, central corneal stromal thinning was the most sensitive diagnostic index for early detection of SKCN. Developing standardized stromal maps may be helpful for detecting SKCN.

Longitudinal Analysis of Corneal Biomechanics of Suspect Keratoconus: A Prospective Case-Control Study.

BACKGROUND: To evaluate the corneal biomechanics of stable keratoconus suspects (Stable-KCS) at 1-year follow-up and compare them with those of subclinical keratoconus (SKC). METHODS: This prospective case-control study included the eyes of 144 patients. Biomechanical and tomographic parameters were recorded (Corvis ST and Pentacam). Patients without clinical signs of keratoconus in both eyes but suspicious tomography findings were included in the Stable-KCS group (n = 72). Longitudinal follow-up was used to evaluate Stable-KCS changes. Unilateral keratoconus contralateral eyes with suspicious tomography were included in the SKC group (n = 72). T-tests and non-parametric tests were used for comparison. Multivariate general linear models were used to adjust for confounding factors for further analysis. Receiver operating characteristic (ROC) curves were used to analyze the distinguishability. RESULTS: The biomechanical and tomographic parameters of Stable-KCS showed no progression during the follow-up time (13.19 ± 2.41 months, p > 0.05). Fifteen biomechanical parameters and the Stress-Strain Index (SSI) differed between the two groups (p < 0.016). The A1 dArc length showed the strongest distinguishing ability (area under the ROC = 0.888) between Stable-KCS and SKC, with 90.28% sensitivity and 77.78% specificity at the cut-off value of -0.0175. CONCLUSIONS: The A1 dArc length could distinguish between Stable-KCS and SKC, indicating the need to focus on changes in the A1 dArc length for keratoconus suspects during the follow-up period. Although both have abnormalities on tomography, the corneal biomechanics and SSI of Stable-KCS were stronger than those of SKC, which may explain the lack of progression of Stable-KCS.

Revisiting Pentacam Parameters in the Diagnosis of Subclinical and Mild Keratoconus Based on Different Grading System Definitions

Objectives: To retest the performance of Pentacam parameters in the detection of eyes with subclinical keratoconus (KC) and mild KC based on different definitions from the Amsler-Krumeich (AK), Collaborative Longitudinal Evaluation of Keratoconus (CLEK), and ABCD systems. Materials and Methods: This cross-sectional university-based study comprised 24 eyes with subclinical KC, 144 eyes with mild KC (based on AK in 101 eyes, CLEK in 28 eyes, and ABCD in 15 eyes), and 70 controls. Diagnostic ability of the thinnest point (TP) pachymetry, KISA% index, inferior-superior asymmetry, corneal aberrations, Pentacam indices, front/back elevations, pachymetric progression index, Ambrósio-Relational Thickness (ARTmax), and Belin/Ambrósio Enhanced Ectasia Display scores (Df, Db, Dp, Dt, Da, and D-final) were evaluated. Results: ARTmax (83.3% sensitivity/74.3% specificity) had the highest ability in distinguishing subclinical KC from normal, followed by TP pachymetry, Dt, and Da. D-final showed excellent sensitivity/specificity in mild KC diagnosis based on AK (98%/100%) and CLEK (97.4%/100%) descriptions. In the mild KC-ABCD group, index of vertical asymmetry accurately detected all eyes with mild KC and 97.1% of the controls. Conclusion: This study points out the gray zone in the detection of eyes with subclinical and mild KC due to overlapping terminology and grading criteria. Pentacam parameters seem to have modest capability in subclinical KC detection, indicating the necessity for additional diagnostic modalities. However, eyes with mild KC can be diagnosed with high accuracy using Pentacam parameters, although the strongest parameters may vary according to the definition of "mild KC." Nevertheless, uniform and definitive criteria for subclinical and clinical KC classification are required for a diagnostic and therapeutic consensus in KC.

Could Corneal Densitometry be a Progression Criterion for Subclinical Keratoconus?

Objectives: The objective of this study is to investigate the changes in topometry, tomography, and corneal densitometry in subclinical keratoconus (SK) at the 6-month interval. Methods: The clinical keratoconus and SK groups included 25 eyes; the control group included 22 eyes from 22 patients. Corneal topographic, tomographic, topometric, and densitometric values obtained using the Pentacam HR imaging system were analyzed. Results: Posterior elevation (PE), Keratoconus index (KI), index of height asymmetry (IHA), index of height decentration (IHD), Dp, Da, Final D, maximum pachymetric progression index (PPImax), and maximum Ambrósio relational thickness parameters showed significant changes between the baseline and the 6th-month follow-up in SK group (p<0.05 for all values). There were significant changes in all zones except a central layer of 6-10 zone, anterior, and central layer of 10-12 zone between the baseline and the 6th-month follow-up in the SK group (p<0.05, for all values). The changes in mean±standard deviation of KI, IHA, IHD, PPImax parameters, and corneal densitometry values of the posterior layer of 0-2 mm and 2-6 mm zones were significant in the SK group compared to the controls (p<0.05, for all values). Conclusion: PE, KI, IHA, IHD, and PPImax parameters as well as increasing corneal light backscatter of the posterior central layer might be useful for follow-up of progression of SK. New multimeric parameters created by combinations of topometric, tomographic, and corneal densitometry parameters could be the future of SK follow-up.

Prevalence of subclinical keratoconus and impact on adults undergoing routine, uncomplicated age-related cataract extraction.

Aim: To determine the prevalence of subclinical keratoconus (SKCN) among individuals undergoing routine, uncomplicated age-related cataract surgery and its impact on visual and refractive outcomes. Patient and Methods: At a major academic ophthalmology department in the United States, we reviewed records of patients aged 50 years and older who underwent surgery from January 2011 to June 2022. We excluded patients who had poor-quality or unreliable tomographic data, previous corneal surgery, keratorefractive procedures, and significant vision-limiting ocular pathology. We defined SKCN if an eye had a Belin-Ambrósio enhanced ectasia index (BAD-D) ≥1.7, which was based on the results of a meta-analysis of large studies. In addition to the BAD-D cutoff, the eye had to deviate significantly on at least one of seven additional parameters: 1) posterior elevation at thinnest point, 2) index of vertical asymmetry, 3) index of surface variation, 4) total front higher order aberrations, 5) front vertical coma, 6) front secondary vertical coma, 7) back vertical coma. An individual had SKCN if at least one eye met the tomography-based classification and did not have manifest KCN in either eye. Visual and refractive outcomes data were acquired from patients of one experienced cataract surgeon with cases done from July 2021 to June 2022. Statistical significance was set at p < 0.05. Results: Among 5592 eyes from 3828 individuals, the prevalence of SKCN was 24.7% (95% CI, 23.4 - 26.1, 945 individuals), and the prevalence of KCN was 1.9% (95% CI, 1.6 - 2.4, 87 individuals). The prevalence of SKCN did not increase with age and was more prevalent among females and non-white races. Median post-operative month one distance-corrected visual acuity (DCVA) and proportion of eyes with improvement in DCVA were similar between normal and SKCN eyes. The proportion of eyes reaching ±0.5 and ±1.0 diopter within the refractive target were similar between normal and SKCN eyes. Conclusion: SKCN is highly prevalent and should be detected but is unlikely to have a significant deleterious effect on outcomes in routine, uncomplicated cataract surgery.

Role of corneal epithelial mapping, Corvis biomechanical index, and artificial intelligence-based tomographic biomechanical index in diagnosing spectrum of keratoconus.

AIM: The aim of the study was to evaluate the utility of epithelial mapping, Corvis biomechanical index (CBI), and tomographic biomechanical index (TBI) in diagnosing the spectrum of keratoconus (KC). METHODS: This was a retrospective study where KC subjects with an age between 11 and 50 years were enrolled. Subjects with ocular diseases, history of previous corneal surgery, corneal scars or hydrops, ocular trauma, ocular surface disorder, systemic disease, and poor-quality scans were excluded. KC was classified using Belin ABCD classification system. Epithelial thickness, corneal tomography, and corneal biomechanical measurements were recorded using Fourier-domain optical coherence tomography Avanti with corneal adaptor module, Pentacam HR, and Corvis® ST, respectively. To understand the utility of various corneal parameters in diagnosing spectrum of keratoconus, cutoff values for epithelial thickness at the thinnest location and its standard deviation (SD) were considered 45 and 3 microns, respectively, CBI of 0.5 and TBI of 0.29 was considered. RESULTS: Sixty-five eyes (45 - KC, 10 - subclinical KC (SBKC), and 10 - forme fruste [FF]) of 34 patients with a mean ± SD age of 30.73 ± 5.71 were included. In our keratoconic sample, epithelial mapping alone helped diagnose the 77.77% of cases, however, when combined with CBI, it helped diagnose 95.5% cases and when combined with TBI, it was useful in diagnosing 100% of cases. In SBKC group, 40% of cases were detected by epithelial mapping alone, and when combined with CBI, it helped diagnose 70% of cases and TBI further helped diagnose 90% of cases. We did not see any role of epithelial mapping in detecting FFKC cases whereas CBI and TBI helped diagnose 60% and 90% of cases, respectively. CONCLUSION: The utility of epithelial mapping as a solitary tool is limited in detecting the spectrum of KC, especially SB and FFKC. However, combining it with corneal biomechanical parameters could help improve the efficacy of diagnosis of KC.

The application of corneal biomechanical interocular asymmetry for the diagnosis of keratoconus and subclinical keratoconus.

Purpose: To evaluate the interocular consistency of biomechanical properties in normal, keratoconus (KC) and subclinical keratoconus (SKC) populations and explore the application of interocular asymmetry values in KC and SKC diagnoses. Methods: This was a retrospective chart-review study of 331 ametropic subjects (control group) and 207 KC patients (KC group, including 94 SKC patients). Interocular consistency was evaluated using the intraclass correlation coefficient (ICC). Interocular asymmetry was compared between the control and KC groups and its correlation with disease severity was analyzed. Three logistic models were constructed using biomechanical monocular parameters and interocular asymmetry values. The diagnostic ability of interocular asymmetry values and the newly established models were evaluated using receiver operating characteristic curves and calibration curves. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also estimated. Results: The interocular consistency significantly decreased and the interocular asymmetry values increased in KC patients compared with those in control individuals. In addition, the interocular asymmetry values increased with respect to the severity of KC. The binocular assisted biomechanical index (BaBI) had an area under the curve (AUC) of 0.998 (97.8% sensitivity, 99.2% specificity; cutoff 0.401), which was statistically higher than that of the Corvis biomechanical index [CBI; AUC = 0.935, p < 0.001 (DeLong's test), 85.6% sensitivity]. The optimized cutoff of 0.163 provided an AUC of 0.996 for SKC with 97.8% sensitivity, which was higher than that of CBI [AUC = 0.925, p < 0.001 (DeLong's test), 82.8% sensitivity]. Conclusion: Biomechanical interocular asymmetry values can reduce the false-negative rate and improve the performance in KC and SKC diagnoses.

Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus.

Background: Keratoconus (KC) occurs at puberty but diagnosis is focused on adults. The early diagnosis of pediatric KC can prevent its progression and improve the quality of life of patients. This study aimed to evaluate the ability of corneal tomographic and biomechanical variables through machine learning analysis to detect subclinical keratoconus (SKC) in a pediatric population. Methods: Fifty-two KC, 52 SKC, and 52 control pediatric eyes matched by age and gender were recruited in a case-control study. The corneal tomographic and biomechanical parameters were measured by professionals. A linear mixed-effects test was used to compare the differences among the three groups and a least significant difference analysis was used to conduct pairwise comparisons. The receiver operating characteristic (ROC) curve and the Delong test were used to evaluate diagnostic ability. Variables were used in a multivariate logistic regression in the machine learning analysis, using a stepwise variable selection to decrease overfitting, and comprehensive indices for detecting pediatric SKC eyes were produced in each step. Results: PE, BAD-D, and TBI had the highest area under the curve (AUC) values in identifying pediatric KC eyes, and the corresponding cutoff values were 12 µm, 2.48, and 0.6, respectively. For discriminating SKC eyes, the highest AUC (95% CI) was found in SP A1 with a value of 0.84 (0.765, 0.915), and BAD-D was the best parameter among the corneal tomographic parameters with an AUC (95% CI) value of 0.817 (0.729, 0.886). Three models were generated in the machine learning analysis, and Model 3 (y = 0.400*PE + 1.982* DA ratio max [2 mm]-0.072 * SP A1-3.245) had the highest AUC (95% CI) value, with 90.4% sensitivity and 76.9% specificity, and the cutoff value providing the best Youden index was 0.19. Conclusion: The criteria of parameters for diagnosing pediatric KC and SKC eyes were inconsistent with the adult population. Combined corneal tomographic and biomechanical parameters could enhance the early diagnosis of young patients and improve the inadequate representation of pediatric KC research.

Prevalence of Keratoconus on Screening of Egyptian LASIK Candidates: A Retrospective Multicenter Study.

PURPOSE: This is a retrospective multicenter study to report the incidental discovery of keratoconus (clinical and subclinical) in a screening of laser vision correction (LVC) surgery candidates. METHODS: This retrospective multicenter study was conducted on patients presenting for LVC in four Egyptian governorates (Cairo-Giza-6th of October-Beni Suef) during the year 2018. The patients were examined using the Pentacam HR (OCULUS Optikgeräte GmbH, Wetzlar, Germany) or Sirius (Costruzione Strumenti Oftalmici, Italy). The following parameters were evaluated: the axial curvature map, keratometry (Kmax and K2 on the posterior surface), minimum corneal thickness, anterior elevation, posterior elevation, Baiocchi-Calossi -Versaci index (Sirius), index of height decentration, and BAD-D (Pentacam). The prevalence of keratoconus cases was reported and data were analyzed. RESULTS: A total of 46 out of 782 candidates presenting for LVC in 2018 were incidentally discovered as clinical or subclinical keratoconus cases and were excluded from performing the LVC procedure. CONCLUSION: Screening of LVC candidates for keratoconus is a crucial tool to detect the incidence of the disease in the Egyptian population.

Performances of Corneal Topography and Tomography in the Diagnosis of Subclinical and Clinical Keratoconus.

Aim: The purpose of the study was to assess the efficacy of topographical and tomographical indices given by the Pentacam (pachymetric, tomopetric, and aberometric) in clinical and subclinical keratoconus (KCN) diagnosis. Material and Methods: In this observational analytic retrospective study, patients with abnormal findings in topography and tomography maps but with no signs on clinical examination (subclinical KCN group, sKCN), patients with clinical keratoconus (KCN group), and healthy subjects (Control group) were evaluated. Results: The KCN group proved significantly different (p < 0.001) values of the investigated parameters than the Control group. Eleven out of 28 investigated parameters proved significantly different in the sKCN group compared to controls (p < 0.001). Two topographic measurements, namely I-S (cut-off = 1.435, a large value indicates the presence of KCN) and CCT (cut-off = 537, a small value indicates the presence of KCN), showed AUCs equal to 1 [0.999 to 1]. Six other Pentacam measurements, including Back maximum keratometry (Back Kmax) proved to be excellent parameters for case-finding and screening. In distinguishing sKCN from normal eyes, Pentacam index of vertical asymmetry (IVA), inferior-superior difference (I-S) value, thinnest point (TP), Belin Ambrosio Enhanced Ectasia Display (BAD_D) and root mean square total (RMS total) performed best. Conclusions: In distinguishing sKCN from normal eyes, Back Kmax, IVA, I-S, and RMS total values demonstrated higher accuracy and utility. Six indices, namely ISV, IVA, KISA, PRC, RMS-HOA, and Back Kmax demonstrate excellent utility in case-finding and screening for clinical KCN.

Comparison of Artificial Intelligence-Based Machine Learning Classifiers for Early Detection of Keratoconus.

PURPOSE: To compare the agreement between artificial intelligence (AI)-based classifiers and clinical experts in categorizing normal cornea from ectatic conditions. METHODS: Prospective diagnostic test study at Noor Eye Hospital. Two hundred twelve eyes of 212 patients were categorized into three groups of 92 normal, 52 subclinical keratoconus (SKCN), and 68 KCN eyes based on clinical findings by 3 independent expert examiners. All cases were then categorized using four different classifiers: Pentacam Belin/Ambrosio enhanced ectasia total deviation value (BADD) and Topographic Keratoconus Classification (TKC), Sirius Phoenix, and OPD-Scan III Corneal Navigator. The performance of classifiers and their agreement with expert opinion were investigated using the sensitivity, specificity, and Kappa index (κ). RESULTS: For detecting SKCN, Phoenix had the highest agreement with the clinical diagnosis (sensitivity, specificity, and κ of 84.62%, 90.0%, and 0.70, respectively) followed by BADD (55.56%, 86.08%, 0.42), TKC (26.92%, 97.50%, 0.30), and Corneal Navigator (30.77%, 93.75%, 0.29). For KCN diagnosis, the highest agreement with expert opinion was seen for Phoenix (80.02%, 96.60%, 0.79), BADD (95.59%, 85.42%, 0.75), TKC (95.59%, 84.03%, 0.73), and Corneal Navigator (67.65%, 96.45%, 0.68). Analysis of different classifiers showed that Phoenix had the highest accuracy for differentiating KCN (91.24%) and SKCN (88.68%) compared to other classifiers. CONCLUSIONS: Although AI-based classifiers, especially Sirius Phoenix, can be very helpful in detecting early keratoconus, they cannot replace clinical experts' opinions, particularly for decision-making before refractive surgery. Albeit, there may be concerns about the accuracy of clinical experts as well.

Evaluation of corneal topographic, tomographic and biomechanical indices for detecting clinical and subclinical keratoconus: a comprehensive three-device study.

AIM: To evaluate the diagnostic ability of topographic and tomographic indices with Pentacam and Sirius as well as biomechanical parameters with Corvis ST for the detection of clinical and subclinical forms of keratoconus (KCN). METHODS: In this prospective diagnostic test study, 70 patients with clinical KCN, 79 patients with abnormal findings in topography and tomography maps with no evidence on clinical examination (subclinical KCN), and 68 normal control subjects were enrolled. The accuracy of topographic, tomographic, and biomechanical parameters was evaluated using the area under the receiver operating characteristic curve (AUC) and cross-validation analysis. The Delong method was used for comparing AUCs. RESULTS: In distinguishing KCN from normal, all parameters showed statistically significant differences between the two groups (P<0.001). Indices with the perfect diagnostic ability (AUC≥0.999) were Sirius KCN vertex of back (KVb), Pentacam random forest index (PRFI), Pentacam index of height decentration (IHD), and Corvis integrated tomographic/biomechanical index (TBI). In distinguishing subclinical KCN from normal, Sirius symmetry index of back (SIb; AUC=0.908), Pentacam inferior-superior difference (IS) value (AUC=0.862), PRFI (AUC=0.847), and Corvis TBI (AUC=0.820) performed best. There were no significant differences between the highest AUCs within keratoconic groups (DeLong, P>0.05). CONCLUSION: In clinical KCN, all topographic, tomographic, and biomechanical indices have acceptable outcomes in terms of sensitivity and specificity. However, in differentiating subclinical forms of KCN from normal corneas, curvature-based parameters (SIb and IS value) followed by integrated indices (PRFI and TBI) are the most powerful tools for early detection of KCN.

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques.

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 µm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.