Topographic Keratoconus Classification Research Articles

Diagnostic ability of the corneal anterior and posterior surface area calculated by corneal modelling approach in early stage keratoconus patients.

To investigate the discrimination ability of the corneal anterior and posterior surface area between patients with keratoconus stage 1 and normal individuals. In this retrospective study, 116 eyes of 116 normal individuals and 366 eyes of 366 keratoconus patients were included. Keratoconus patients were divided into six groups according to the topographic keratoconus classification of Pentacam. Anterior and posterior surface data of sagittal (SM) and elevation maps (EM) were exported from Pentacam, and human corneal models were created employing the software utilizing the PyVista module of Python programming language. The anterior and posterior corneal surface area (a-CSA and p-CSA) of SM and EM were calculated by the software ranging from central 3 to 8mm diameter. Anterior and posterior CSA values were higher in KC patients compared to normal individuals (p < 0.001). The p-CSA for SM and EM measured at the central 3mm was higher in patients with KC-1 compared to normal eyes (p = 0.002, p = 0.005, respectively), For both maps, a-CSA and p-CSA measured at the central 4 and 5mm were higher in KC-1 compared to normal individuals (p < 0.05). The highest area under the curve (AUC) values in the diagnosis patients with KC-1 were obtained from 3mm p-CSA for SM (AUC: 0.8338), 3mm p-CSA for EM (AUC: 0.7999), 4mm p-CSA for SM (AUC: 0.8531), 4mm p-CSA for EM (AUC:0.7948), 5mm p-CSA for SM (AUC: 0.8455), 5mm p-CSA for EM (AUC:0.7614). The corneal surface area as a parameter, especially for central 3, 4, and 5mm, has a discrimination ability in diagnosing keratoconus disease and distinguishes normal eyes from KC-1 eyes.

Is it really keratoconus ‐ topographic mimicry

Aims/Purpose: The aim of this case series is to present eyes with topographic keratoconus configuration, but in which objectifiable no keratoconus exists and thus a so‐called pseudo‐keratoconus is present.Methods: Inclusion criteria were a topographic classification of the cornea as keratoconus by Scheimpflug imaging using the topographic keratoconus classification TKC and a typical elevation data profile with irregular astigmatism and slope (= Ansteilung) of the cornea. Clinical image by slit lamp examination, best‐corrected visual acuity and Scheimpflug tomography of the anterior segment of the eye (Pentacam®, OCULUS, Wetzlar, Germany) were analysed.Results: In our case series, 33 eyes of 23 patients were included. The mean age was 51 (±16) years and the ratio of females to males was balanced. The mean Kmax was 51.1 (±7.1) dioptres. The mean corneal thickness at the thinnest point was 517 (±80) μm. The best‐corrected decimal visual acuity was 0.47 (±0.27). The study eyes were divided into 3 groups according to the underlying cause of the topographic changes. One group included corneal diseases with inferior slope in topography (corneal scar n = 9, Map‐Dot‐Fingerprint dystrophy n = 6, Fuchs endothelial dystrophy n = 5, vortex keratopathy n = 2). The second group had a superior corneal slope that could be interpreted as a superior keratoconus configuration (Map‐Dot‐Fingerprint dystrophy n = 5, irregular astigmatism without other corneal pathology n = 2, Salzmann corneal degeneration n = 1, corneal scar n = 1). The third group included eyes with postoperative newly occurring keratoconus configuration after phototherapeutic keratectomy (n = 2).Conclusions: This case series shows that different corneal pathologies can imitate a topographic keratoconus configuration. The identification – not least at the slit lamp – and differentiation of these pathologies from keratoconus are important in daily practice with regard to counselling of the patients and further treatment.

A novel tool for quantitative measurement of distortion in keratoconus.

BackgroundKeratoconus is associated with thinning and anterior protrusion of the cornea resulting in the symptoms of blurry and distorted vision. The commonly used clinical vision tests such as visual acuity and contrast sensitivity may not reflect the symptoms experienced in keratoconus and there are no quantitative tools to measure visual distortion. In this study, we used a quantitative test based on vernier alignment and field matching techniques to quantify visual distortion in keratoconus and assess its relation to corneal structural changes.MethodsA total of 50 participants (25 keratoconus and 25 visually normal) completed the experiment where they aligned supra-threshold white target circles in opposite field in reference to guidelines and circles to complete a square structure monocularly. The task was repeated five times and the global distortion index (GDI) and global uncertainty index (GUI) were calculated as the mean and standard deviation respectively of local perceived misalignment of target circles over five trials.ResultsBoth GDI and GUI were higher in participants with keratoconus compared to controls (p < 0.01). Both parameters correlated with the best corrected visual acuity, maximum corneal curvature (Kmax), topographical keratoconus classification (TKC) and central corneal thickness (CCT).ConclusionOur findings show that the quantitative measure of distortion could be a useful tool for behavioural assessment of progressive keratoconus.

Distribution of pediatric keratoconus by different age and gender groups.

PurposeTo evaluate the distribution of pediatric keratoconus patients and the disease severity based on different age and gender groups in China.Materials and methodsA total of 446 keratoconus eyes in 266 pediatric patients from January 2019 to January 2022 were included in the cross-sectional study. The clinical findings and severity of keratoconus were recorded and Kruskal–Wallis test, chi-squared test, and Cochran-Mantel-Haenszel (CMH) test were used to compare the parameters between different gender and age groups.ResultsThe male/female ratio was 353/93 (3.8:1), and the median age was 16 years (range: 6–17 years). Male patients were statistically younger than female patients (P = 0.041). The male/female ratio decreased with age (P for trend = 0.011). The distribution of the topographic keratoconus classification (TKC) stage was significantly different between gender and age groups (all P < 0.05). Male patients had a higher ratio of advanced keratoconus eyes (TKC ≥ 3) than female patients (P < 0.001), and CMH analysis indicated that being a male was a risk factor for advanced keratoconus after controlling for age (odds ratio: 2.581, P < 0.001).ConclusionMale keratoconus patients were younger, with a higher ratio of advanced keratoconus than female patients in the Chinese pediatric patients evaluated. Multicenter studies with larger sample sizes are necessary in the future.

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

To compare the agreement between artificial intelligence (AI)-based classifiers and clinical experts in categorizing normal cornea from ectatic conditions. 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). 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. 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.

Distribution of Corneal Geometric Landmarks and Relationship Between Their Distances and Biomechanical Parameters in the Development of Keratoconus.

Purpose: To analyze the changes in coordinates and distances among three typical geometric landmarks of the cornea, namely, the thinnest point (TP), maximum curvature (Kmax), and corneal apex (AP) during the development of keratoconus, and explore the potential relationship between these changes and the abnormalities of corneal biomechanics. Methods: Normal eyes (n = 127), clinical keratoconic eyes (CKC, n = 290), and the eyes of forme fruste keratoconus (FFKC, n = 85) were included; among them, the CKC group was classified into four grades based on the Topographic Keratoconus Classification (TKC) provided by Pentacam. A total of 38 Corvis ST output parameters and three distance parameters of three typical landmarks (DKmax-AP, DTP-AP, and DKmax-TP) based on Pentacam were included. The differences of parameters among the abovementioned six groups (Normal, FFKC, and CKC stage I to CKC stage IV) were analyzed. Spearman’s rank correlation test was performed to choose several dynamic corneal response (DCR) parameters that could best reflect the changes of corneal biomechanical characteristics during the progression of the disease, and the Pearson’s or Spearman’s correlation test was conducted to determine the association between the three distances and the selected DCR parameters in each grade. In addition, by flipping the X coordinate of the left eye on the vertical axis to reflect the direction of the right eye, the coordinates of TP and Kmax in different developmental grades were highlighted. Results: From CKC stage II, the three geometric landmark distances commenced to correlate with the corneal DCR parameters (CBI, SPA1, IR, DA Ratio 2, ARTh, MIR, Radius, Pachy, and DA Ratio 1), which could better represent the changes of biomechanical properties from normal cornea to keratoconus. From normal cornea to CKC stage IV, the coordinates of Kmax were gradually tended to the inferior temporal region from dispersion, while TP was always concentrated in the inferior temporal region. Although DKmax-AP, DKmax-TP, and DTP-AP all showed a gradual decreasing trend with the progress of the disease, the first two did not change significantly, and only DTP-AP significantly approached AP in the later stage of disease development. In addition, from the FFKC group, the corresponding values of DKmax-TP in each disease development group were smaller than DKmax-AP. Conclusions: In the later stage of keratoconus, the relationship between the three typical landmark distance parameters and DCR parameters is stronger, and even the weakening of corneal biomechanical properties may be accompanied by the merger of typical landmark positions.

Contact Lens Fitting in Patients with Keratoconus - A Retrospective Assessment of 200 Patients.

Nowadays, keratoconus (KC) is very well treatable in a stage-oriented manner. A wide range of designs and materials of contact lenses (CL) are available for the treatment of KC. The aim of this study was to evaluate the possibilities, the possible challenges and the visual outcome of lens fitting in KC eyes. This retrospective study includes data from 200 patients who received a lens fitting trial in our contact lens service between 2006 and 2016. We documented ophthalmological parameters, the type of prescribed CL, the number of required trial lenses and possible causes of the failure of the lens fitting. The mean age at initial lens fitting was 33.9 ± 12.5 years. In 98.8% of the cases, the fitting was performed with rigid gas permeable lenses, in 90.1% with four-curve lenses. Of the total number of aspheric lenses prescribed, 87.5% were fitted in keratoconus stages "1" to "2" (topographic keratoconus classification; Oculus Keratograph). Back surface toric lenses or bitoric lenses were fitted to 61.7% in keratoconus stages "2 - 3" to "4". Before patients received their final CL, a median of 2 trial lenses were required (max. 16). Mean visual acuity with lens correction was 0.8 ± 0.2 at the initial fitting, mean visual acuity with glasses correction was 0.5 ± 0.3. In 7.7% of the eyes, the KC lens fitting was discontinued due to the advanced stage of keratoconus, requiring a corneal transplant. Reasons for discontinuing contact lens fitting included lens intolerance (2.3%), application problems (0.3%) or acute corneal hydrops (0.3%). Discontinuation of lens wearing due to incompatibilities or application problems occurred in only four cases (1.1%) in the further course after lens fitting. The use of contact lenses is an integral part of the stage-appropriate therapy of keratoconus. Good visual acuity can be achieved in all stages of keratoconus with a low drop-out rate. In most cases, the adjustment is carried out with rigid gas permeable lenses with a four-curve geometry. In initial stages, aspherical lenses may be sufficient. Toric lenses can be fitted in advanced stages when rotationally symmetrical lenses cannot achieve a satisfactory fit. If contact lenses have been successfully fitted, there are only a few cases in which patients abandon their contact lenses because of intolerance.

Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity

BackgroundTo investigate machine-learning (ML) algorithms to differentiate corneal biomechanical properties between different topographical stages of keratoconus (KC) by dynamic Scheimpflug tonometry (CST, Corvis ST, Oculus, Wetzlar, Germany). In the following, ML models were used to predict the severity in a training and validation dataset.MethodsThree hundred and eighteen keratoconic and one hundred sixteen healthy eyes were included in this monocentric and cross-sectional pilot study. Dynamic corneal response (DCR) and corneal thickness related (pachymetric) parameters from CST were chosen by appropriated selection techniques to develop a ML algorithm. The stage of KC was determined by the topographical keratoconus classification system (TKC, Pentacam, Oculus). Patients who were classified as TKC 1, TKC 2 and TKC 3 were assigned to subgroup mild, moderate, and advanced KC. If patients were classified as TKC 1–2, TKC 2–3 or TKC 3–4, they were assigned to subgroups according to the normative range of further corneal indices (index of surface variance, keratoconus index and minimum radius). Patients classified as TKC 4 were not included in this study due to the limited amount of cases. Linear discriminant analysis (LDA) and random forest (RF) algorithms were used to develop the classification models. Data were divided into training (70% of cases) and validation (30% of cases) datasets.ResultsLDA model predicted healthy, mild, moderate, and advanced KC eyes with a sensitivity (Sn)/specificity (Sp) of 82%/97%, 73%/81%, 62%/83% and 68%/95% from a validation dataset, respectively. For the RF model, a Sn/Sp of 91%/94%, 80%/90%, 63%/87%, 72%/95% could be reached for predicting healthy, mild, moderate, and advanced KC eyes, respectively. The overall accuracy of LDA and RF was 71% and 78%, respectively. The accuracy for KC detection including all subgroups of KC severity was 93% in both models.ConclusionThe RF model showed good accuracy in predicting healthy eyes and various stages of KC. The accuracy was superior with respect to the LDA model. The clinical importance of the models is that the standalone dynamic Scheimpflug tonometry is able to predict the severity of KC without having the keratometric data.Trial registrationNCT04251143 at Clinicaltrials.gov, registered at 12 March 2018 (Retrospectively registered).

Corneal densitometry in different stages of keratoconus

Purpose: The aim of this study was to evaluate corneal clarity in keratoconic patients using corneal densitometry. Methods: comparing corneal densitometry results among keratoconic patients and normal individual was measured over a 12- mm diameter area, divided by annular concentric zones and depths. Keratoconus was classified according to the topographic keratoconus classification, from March 2019 to March 2020 in Ophthalmic center Mansoura University, Egypt. Results: Group 1: 20 eyes diagnosed as manifest grade 1 keratoconus; Group 2: 20 eyes diagnosed as manifest grade 2 keratoconus; Group 3: 20 eyes diagnosed as manifest grade 3 keratoconus; Group 4: 20 eyes diagnosed as manifest grade 4 keratoconus; with 40 eyes normal topographic parameter a control group. the comparison between the studied groups regarding anterior (120 mm), central (from 120 mm to the last 60 mm) and posterior (last 60 mm) layers measurements showed increase in corneal densitometry reading in 0-2 mm, 2-6 mm, 6-10 mm, 10-12 mm zones in keratoconous patient and compared to control group, there was significantly increased in densitometry readings in keratoconous eyes and was related to keratoconous severity. Conclusions: The densitometry readings increase in keratoconic eyes with higher values with increasing severity of keratoconus. Whitch may add valuable data for early diagnosis of keratoconus.

Tomographic and aberrometric assessment of first-time diagnosed paediatric keratoconus based on age ranges: a multicentre study.

To describe paediatric keratoconus (KC) patients by tomographic and aberrometric characteristics at first diagnosis, in a multicentre study. We included 278 eyes from 139 paediatric patients, with a first tomographic diagnosis (Pentacam® ) of KC prior to 18years old. KC classification was based on the KC Index (≥ 1.07) and Topographic Keratoconus Classification (TKC≥1). Patients were divided based on age ranges (14 and under and over 14years) and gender. Statistical analysis was performed with SPSS statistics 25.0. ANOVA factor was carried out comparing to compare groups. 278 eyes were screened, and 230 eyes were diagnosed with paediatric KC. Mean age was 15.48±2.33 (6 to 18) years. We found differences in terms of TKC (2.08±0.89 and 2.38±0.82, p<0.05) and spherical aberration (-0.71±0.97 and -1.07±1.36, p<0.05) among the 14years old or under and above 14years old groups, respectively. Overall, female paediatric KC patients presented a more severe TKC, Belin Ambrosio Display, maximum keratometry, asphericity and primary and secondary coma aberrations compared to male KC patients. We observed a correlation between CDVA and asphericity (r=0.71, p<0.01), as well as between CDVA and spherical aberration (r=0.69, p<0.01). Our findings revealed that the debut of KC is usually in a moderate to advanced stage in the paediatric population at first diagnosis, particularly in female patients. Corneal tomography should be systematically performed in children with recent onset of corneal astigmatism.

Keratoconus detection and classification from parameters of the Corvis®ST : Astudy based on algorithms of machine learning

Hintergrund und ZielsetzungIn den vergangenen Jahren wurden zunehmend Systeme der künstlichen Intelligenz in der Medizin etabliert, die Pathologien oder Erkrankungen erkennen oder von komplementären Erkrankungen abgrenzen. Bisher liefert das Corvis®ST (Corneal Visualization Scheimpflug Technology, Oculus, Wetzlar, Deutschland) einen Index-CBI, der quasi binär Keratokonus klassifiziert, aber kein Staging zulässt. Ziel der Studie ist es, anhand von Messgrößen des Corvis®ST ein Vorhersagemodell zu entwerfen, das den Topographic Keratoconus Classification Index (TKC) der Pentacam high resolution (HR, Oculus) nachbildet.Patienten und MethodenEs wurden 60 Messungen an Normalprobanden (TKC 0) und 379 Augen mit Keratokonus (TKC 1 bis 4) in die Studie mit einbezogen. Nach der Messung mit der Pentacam HR (Zielgröße TKC) wurde eine Untersuchung mit dem Corvis®ST durchgeführt, aus der 6 Messparameter extrahiert wurden, die in den Corvis Biomechanical Index CBI eingehen (ARTh, SP-A1, DA-Ratio 1 mm, DA-Ratio 2 mm, A1 velocity, max. Deformation Amplitude). Neben dem TKC als Zielgröße wurde der binarisierte TKC (1: TKC 1 bis 4, 0: TKC 0) modelliert. Als Gütemaß wurde die Genauigkeit des Modells als Anteil der korrekten Klassifizierungen herangezogen. Fehlklassifizierungen wurden in der Modellierung so bestraft, dass die Abweichung des modellierten TKC-Wertes vom gemessenen Wert bewertet wurde.ErgebnisseEs wurden 24 verschiedene Modelle des überwachten maschinellen Lernens aus 6 Familien getestet. Für die Modellierung des TKC in Stufen von 0–4 zeigte das Modell, basierend auf einer Support Vector Machine (SVM) mit linearem Kernel, die beste Performance mit einem Anteil an richtigen Klassifizierungen von 65,1 %. Für den binarisierten Wert des TKC zeigte ein Decision Tree mit grober Auflösung die beste Performance mit einem Anteil an richtigen Klassifizierungen von 95,2 %, direkt gefolgt von der SVM mit linearem oder quadratischem Kernel und dem Nearest Neighborhood Classifier mit kubischem Kernel (jeweils 94,5 %).SchlussfolgerungenIn der Arbeit soll das Prinzip des überwachten Maschinenlernens in der Anwendung auf die modellierte Klassifizierung von Messbefunden gezeigt werden. So wurden Messdaten des Corvis®ST dazu verwendet, die Einteilung in den Schweregrad eines Keratokonus mittels Pentacam (TKC) mit einer ganzen Reihe von Algorithmen des maschinellen Lernens nachzubilden.

Logistic index for keratoconus detection and severity scoring (Logik)

PurposeTo develop an objective severity scoring system for keratoconus for the use in clinical practice. MethodsCorneal elevation and minimum thickness data of 812 subjects were retrospectively collected and divided into two groups: one control group with normal topography in both eyes (304 eyes), and one keratoconus group (508 eyes). Keratoconus cases ranged from suspect to moderate and had at least 1 examination in 1 of 2 recruiting centres. The elevation data were fitted to Zernike polynomial functions up to 8th order. An adapted machine learning algorithm was then applied to derive a platform-independent severity scoring and identification system for keratoconus. ResultsThe resulting logistic index for keratoconus (Logik) provided consistent and progressing scoring that reflected keratoconus severity. Moreover, the system provided an accurate classification of suspect keratoconus versus normal (sensitivity of 85.2%, specificity of 70.0%) when compared with Belin/Ambrosio Display Deviation (BAD_D) (sensitivity of 75.0%, specificity of 74.4%) and the Pentacam Topographical Keratoconus Classification (TKC) (sensitivity of 9.3%, specificity of 97.0%). Logik also showed better accuracy for grading keratoconus stages with an average accuracy of 99.9% versus (98.2%, 94.7%) with BAD_D and TKC respectively. ConclusionLogik is a reliable index to identify suspect keratoconus and to score the severity of the disease. It shows an agreement with existing approaches while achieving better performance.

The impact of filaggrin genotype and atopic dermatitis on risk and severity of keratoconus

Abstract Background Filaggrin is a major structural protein in the skin barrier and null mutations in the filaggrin gene (FLG) increase the risk of developing atopic dermatitis (AD). Previous studies report an association between keratoconus (KC) and the presence of AD, and FLG mutated patients with concomitant AD may have a more severe KC. We investigated if FLG genotype and AD correlate to the frequency and severity of KC. Methods One hundred and eight patients were recruited and distributed in four groups based on KC and AD status. Patients with high myopia comprised the reference group. We used the topographical keratoconus classification (TKC), Kmax and central corneal thickness (CCT) to compare the groups and analyzed FLG mutation status by DNA sequencing. Results In total, 15 patients (14%) in our cohort harbored mutation in FLG. Nine of 49 patients (18%) with AD were FLG mutated whereas 6/59 (10%) without AD were carriers of FLG mutation (P = 0.22). There was no difference in Kmax between KC patients with and without AD (median Kmax 56.9 and 57.6, respectively – P = 0.81). Patients with FLG mutation had a median Kmax of 62.5 whereas FLG wild-type patients had a median Kmax of 56.9, P = 0.29. There was no difference in CCT between FLG mutated KC patients (median CCT 420 µm) and KC patients with FLG wild-type (median CCT 448 µm), P = 0.21. Conclusion In our cohort, the frequency of FLG mutations was low and genotype equally distributed between patients with and without AD and KC. There was no aggravation in KC severity parameters in FLG mutated patients compared to patients with FLG wild-type. Future studies powered to assess the synergistic influence of AD and FLG genotype may identify patients at risk of severe KC.

Ocular Surface Disease Index and Ocular Thermography in Keratoconus Patients.

Purpose Keratoconus (KC) has been defined as a “noninflammatory” corneal disease, but recent studies have noted a potential inflammatory origin. We analysed the Ocular Surface Disease Index (OSDI) and ocular surface temperature (OST) in KC patients compared to controls. Patients and Methods. A total of 179 eyes in 90 patients with KC (topographic keratoconus classification 0-1 to 4, age 36.1 ± 12.5 years, 65.9% males) and 82 eyes in 41 controls (age 36.4 ± 12.8 years, 47.6% males) were examined. The participants completed the OSDI questionnaire and underwent corneal topography, tomography, and thermography. Additional outcome measures were vision- and discomfort-related OSDI subscores and mean OST at the corneal centre during 10 seconds of sustained eye opening after blinking. Results The OSDI score (31.4 ± 22.4 vs. 17.5 ± 17.9) and vision- (17.7 ± 14.6 vs. 10.5 ± 13.2) and discomfort-related (14.3 ± 10.7 vs. 9.4 ± 10.5) OSDI subscores were significantly higher in KC patients than in controls (p < 0.001). We found no significant difference in the central corneal OST (34.2 ± 0.6°C vs. 34.2 ± 0.7°C; p < 0.001). We found no significant difference in the central corneal OST (34.2 ± 0.6°C vs. 34.2 ± 0.7°C; p < 0.001). We found no significant difference in the central corneal OST (34.2 ± 0.6°C vs. 34.2 ± 0.7°C; r > 0.174, p < 0.001). We found no significant difference in the central corneal OST (34.2 ± 0.6°C vs. 34.2 ± 0.7°C; r > 0.174, r > 0.174, Conclusion KC patients had increased OSDI scores and vision- and discomfort-related OSDI subscores without an increase in the OST compared to a normal population. OSDI score/subscores weakly correlate with SAI and SRI but do not correlate with OST in KC patients or controls. Vision- and discomfort-related symptoms of KC have to be managed in parallel in ophthalmological practice, but the necessity of anti-inflammatory treatment cannot be verified through ocular thermography.

Hypothyroidism is Not Associated with Keratoconus Disease: Analysis of 626 Subjects.

Purpose To analyze the association between hypothyroidism and keratoconus, we examined blood thyroid hormone levels and corneal tomographic parameters in healthy subjects and patients with keratoconus. Methods We included 626 subjects (304 left eyes, 49%; 431 males, 69%; age 38.4 ± 14.3 y). Patients with keratoconus were from our Homburg Keratoconus Center (HKC) (n = 463); patients with hypothyroidism were from the Department of Internal Medicine of Saarland Medical University, Homburg/Saar, Germany (n = 75); and healthy subjects were from the Department of Ophthalmology of Saarland University Medical Center (n = 88). We included only one randomly selected eye of each subject and the first examination data. Exclusion criteria Previous thyroid medication, previous ocular surgery, and patients with suspected keratoconus (topographic keratoconus classification, [TKC]: 0 < 1). Patient eyes were classified (TKC) with dedicated, instrument-based, keratoconus detection software provided with the Pentacam. TKC = 0 was considered “normal,” and TKCs ≥ 1 were considered keratoconus. Subjects were also classified as euthyroid or hypothyroid, based on blood thyroid hormone status (i.e., TSH, FT3, and FT4). A multiple logistic linear regression model was constructed to determine the effects of age (covariate), gender, and hypothyroidism (effect sizes) on “TKC-positive” disease. Results The significance levels for a constant parameter, sex, thyroid condition, and age were p < 0.0001, p < 0.0001, p < 0.0001, and p=0.003, respectively. The odds ratios for age, sex, and hypothyroidism were 0.98, 3.05, and 3.34, respectively. Male sex and a euthyroid condition had significantly positive, clinically relevant effects, and age had a significantly negative, but clinically irrelevant effect on the estimated TKC index. Conclusions Keratoconus appeared to occur more often in patients classified as euthyroid than in patients with hypothyroidism. Thus, hypothyroidism alone could not support the development of keratoconus. Based on these results, it should not be mandatory to screen patients with hypothyroidism for keratoconus or patients with keratoconus for hypothyroidism.

Computer aided diagnosis for suspect keratoconus detection

PurposeTo develop a stable and low-cost computer aided diagnosis (CAD) system for early keratoconus detection for clinical use. MethodsThe CAD combines a custom-made mathematical model, a feedforward neural network (FFN) and a Grossberg-Runge Kutta architecture to detect clinical and suspect keratoconus. It was applied to retrospective data of 851 subjects for whom corneal elevation and thickness data was available. These data were divided into four groups: a control group (312 eyes) with bilateral normal tomography, keratoconus suspect (77 eyes) with a clinically diagnosed keratoconus in one eye and a normal fellow eye, mild keratoconus (220 eyes), and moderate keratoconus (229 eyes). The proposed framework is validated using 10-cross-validation, holdout validation and ROC curves. ResultsThe CAD detects suspect keratoconus with an accuracy of 96.56% (sensitivity 97.78%, specificity 95.56%) versus an accuracy of 89.00% (sensitivity 83.00%, specificity 95.00%) for Belin/Ambrosio Deviation (BADD), and an accuracy of 79.00% (sensitivity 58.00%, specificity 99.70%) for Topographical Keratoconus Classification (TKC). For the detection of mild to moderate keratoconus CAD shows nearly similar accuracies as previously described methods, with an average accuracy of 99.50% for CAD, versus 99.46% for BADD and 96.50% for TKC. The proposed algorithm also provides a 70% reduction in computation time, while increasing stability and convergence with respect to traditional machine learning techniques. ConclusionThe proposed algorithm is highly accurate and provides a stable screening platform to assist ophthalmologists with the early detection of keratoconus. This framework could potentially be set up for any Scheimpflug tomography system.

Effects of Preoperative Topometric Indices on Visual Gain After Intracorneal Ring Segment Implantation For Keratoconus.

To assess the corneal topometric parameters that can be predictive for better visual gain after intracorneal ring segment (ICRS) implantation in eyes with keratoconus. A total of 42 eyes of 32 patients who underwent ICRS implantation at Dokuz Eylul University, Deparment of Ophthalmology, Cornea Divison were included. Changes in uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), refractive errors, and corneal topometric indices measured using Scheimpflug topography (Pentacam, Oculus) were evaluated retrospectively. After creation of intrastromal tunnels of 5.01±0.03 mm inner diameter, 5.71±0.03 mm outer diameter and at 384.21±34.12 μm depth, 1 or 2 ICRS of 150 to 350 µm thickness and 90 to 210° arc length were implanted. Preoperative UDVA of 0.09±0.10 and CDVA of 0.29±0.14 Snellen lines improved postoperatively to 0.42±0.26 and 0.62±0.24, respectively (P<0.001 for both). Preoperative spherical equivalent of -6.35±4.58D and refractive astigmatism of -5.89±2.40D decreased to -3.59±3.86 and -2.25±1.66D, retrospectively (P<0.001 for both). Strong negative correlations were detected between preoperative mean simulated keratometry (SimKavg)/index of surface variance (ISV) and changes in UDVA/CDVA (P<0.01, for all). Postoperative change in ISV was positively correlated with thicknesses of implanted rings. Change in topographical keratoconus classification was positively and change in index of vertical asymmetry was negatively correlated with number of implanted rings (P<0.05 for all). Preoperative ISV value seems to be beneficial in predicting visual gain after ICRS implantation, in addition to SimKavg. Future work on new nomograms for ICRS selection that include ISV, besides refractive, topographic, and cone location data, is warranted.

Macular Choroidal Thickening in Keratoconus Patients: Swept-Source Optical Coherence Tomography Study

PurposeTo determine the choroidal thickness (CT) profile in keratoconus (KC) patients using swept-source optical coherence tomography (SS-OCT).MethodsThis was a prospective, cross-sectional study. One hundred two eyes of 52 KC patients were studied using Pentacam and SS-OCT. The macular CT profile was created by manually measuring the distance between the retinal pigment epithelium and the choroid-sclera junction on horizontal b-scans at nine different macular locations. The results were compared to 93 eyes of 93 healthy controls.ResultsMean age of the KC group was 34.9 ± 13.5 years and mean axial length (AL) was 24.1 ± 1.3 mm. Mean topographic KC classification (TKC) was 2.0; 39 eyes were classified as early KC (TKC <1–2), 34 eyes as moderate (TKC 2, 2–3), and 29 as advanced (TKC 3+). Mean subfoveal CT was 383.2 μm in KC patients and 280.5 μm in control group (P < 0.001). CT in KC patients was statistically thicker in all measure locations (P < 0.001). CT in KC eyes decreased with age, approaching control group at >45 years old, losing statistical significance (P = 0.37).ConclusionsCT in KC patients is statistically thicker than in healthy population. After age 45, CT decreases approaching control group values.Translational RelevanceThis study describes changes in the CT profile of KC patients, a disease that was considered purely corneal. These choroidal changes argue that KC is a disease that likely involves several ocular structures other than the cornea, and could open new research lines related to the pathophysiology of KC.

Asymmetry between Left and Right Eyes in Keratoconus Patients Increases with the Severity of the Worse Eye

ABSTRACTPurpose: To evaluate whether the inter-eye asymmetry of keratoconus (KC) patients is different from a healthy control group and to investigate how asymmetry changes with increasing severity of the disease.Methods: In this retrospective study, we included both eyes of 350 patients with KC (age 35 ± 13 years) and 68 candidates planned for refractive surgery (control group, age 37 ± 11 years). Inclusion criteria for the KC group were keratoconus in at least one eye with Pentacam Topographical Keratoconus Classification (TKC) of at least 0.5. Patients eligible for refractive surgery in both eyes were included in the control group.Corneal tomography as well as Ocular Response Analyzer measurements were compared between both groups. Subgroup analysis was performed with respect to the TKC staging. Asymmetry was provided as worse eye (defined by higher TKC) minus fellow eye.Results: In the KC group, both eyes showed the same TKC staging in 30.6%, a difference of one stage in 34.0% and of two stages in 24.6% of the patients. The inter-eye asymmetry in the keratoconus group was significantly larger than that in the control group. Corneal power showed an asymmetry of 3.8 ± 4.0 D in keratoconus eyes versus 0.22 ± 0.17 D in the control group. Central corneal thickness (CCT) asymmetry was 34 ± 30 µm versus 6 ± 5 µm, respectively. The Keratoconus Match Index showed an asymmetry of 0.40 ± 0.35 versus 0.15 ± 0.14. The difference between both eyes increased with increasing TKC of the worse eye.Conclusions: Inter-eye asymmetry is larger in keratoconus than in normal eyes, and it increases with keratoconus severity in the worse eye.

Repeatability and reliability of ectasia display and topometric indices with the Scheimpflug system in normal and keratoconic eyes

To evaluate the repeatability and reliability of Belin-Ambrósio Enhanced Ectasia Display-III indices and topometric indices with a Scheimpflug system (Pentacam HR) in normal eyes and keratoconic eyes. Ulucanlar Eye Research and Training Hospital, Ankara, Turkey. Prospective case series. The same operator performed 3 consecutive successive measurements with the Scheimpflug device in the eyes of patients with keratoconus (keratoconus group) and healthy volunteers (normal group). The keratoconus group was further classified into the following 3 subgroups according to the topographic keratoconus classification: early, moderate, and severe. One eye of each volunteer was assigned randomly to a repeatability test. Themaximum keratometry (K), 6-parameter topometric indices, and 9parameter ectasia display indices were recorded. The within-subject standard deviation, precision, repeatability indices, coefficient of variation, and intraclass correlation coefficient (ICC) values were calculated for repeatability and reliability assessments. There were 100 patients in the keratoconus group and 100 healthy volunteers in the normal group. The maximum K, topometric indices except for index of height asymmetry, ectasia display indices except for deviation of normality of the front elevation, and deviation of normality of the back elevation were repeatable in normal eyes and keratoconic eyes. The repeatability indices of all parameters increased as the severity of keratoconus increased. The maximum K value and all indices showed good to excellent reliability in both groups (ICC values between 0.750 and 1.0 for all values). The maximum K value, most topometric indices, and ectasia display indices were repeatable and reliable in normal eyes and keratoconic eyes.