Structural changes in the corneal subbasal nerve plexus in keratoconus

Background Corneal confocal microscopy (CCM) allows visualizing slightest alterations within the corneal subbasal nerve plexus (SNP). Recent CCM studies based on the analysis of three–five CCM images per eye assumed a reduced corneal nerve fibre length (CNFL) in keratoconus (KC). Methods The SNP of KC patients (n = 23, 13 contact lens wearing, 10 noncontact lens wearing) and patients without KC (n = 16) was examined by 10 CCM images of one eye per patient. The CNFL per frame area was calculated, and the SNP tortuosity was quantified by measuring (a) the amplitude of the curves and (b) the area under the curve (AUC) formed by the SNP. Results Analysing 390 non‐overlapping confocal images revealed the CNFL (mm/mm2) to be significantly lower in KC (16.4 ± 1.9 mm/mm2) than in healthy corneae (23.8 ± 3.3 mm/mm2, p < 0.0001; mean ± SD; p‐value calculated using the Mann–Whitney U‐test), without a difference between contact lens wearing and noncontact lens wearing KC patients (p = 0.4). Amplitudes and AUCs analysed as median with 25th and 75th percentile were significantly increased in KC (amplitude 33/23/41 µm and AUC 2839/1545/3444 µm2) compared to healthy corneae (amplitude 24/18/28 µm and AUC 1870/1193/2327 µm2, p < 0.0001). Conclusions Corneal confocal microscopy (CCM) visualizes slightest alterations within the SNP in KC including (a) a significantly lower CNFL and (b) an enhanced winding course of the SNP. The significantly lower CNFL observed in KC may support the hypothesis of a neurodegenerative aspect of the disease and might be a measure to be correlated with the severity and progression of the disease

Combined biomechanical and tomographic keratoconus staging: Adding a biomechanical parameter to the ABCD keratoconus staging system

Purpose This retrospective cross-sectional study evaluated the potential of an additional biomechanical parameter ‘E’ as an addition to the tomographic ABCD ectasia/keratoconus (KC) staging. Methods The Corvis Biomechanical Factor (CBiF) represents the modified linear term of the Corvis Biomechanical Index (CBI) developed based on 448 KC corneas from the Homburg Keratoconus Center (HKC). The CBiF range was divided into five stages (E0 to E4) to create a grading system according to the ABCD stages. Stage E0 was characterized by values smaller than the 2.5 percentile. The thresholds were created by dividing the CBiF range between the 2.5 and 97.5 percentiles into four groups of equal values (E1–E4). The frequency distribution of ‘E’ was analysed and independently validated based on another 860 KC corneas dataset from Milano and Rio de Janeiro (MR). The relationship between ‘E’ and the ABCD staging was analysed by cross-tabulation. The specificity of ‘E’ was assessed based on healthy controls (112|851) from both datasets (HKC|MR). Results ‘E’ was normally distributed with E0 = 37|30, E1 = 86|200, E2 = 155|354, E3 = 101|206, E4 = 69|70 in the KC group and 96.4%|90.5% of the controls classified E0 in the HKC|MR dataset, respectively. Cross-tabulation revealed that ‘E’ was most comparable to posterior corneal curvature (‘B’) in both datasets, while showing a trend towards more advanced stages in comparison to anterior corneal curvature (‘A’) and thinnest corneal thickness (‘C’). Conclusion The novel Corvis-derived parameter ‘E’ provides a biomechanical staging for ectasia/KC potentially enhancing the ABCD staging and may detect abnormalities before tomographic changes, which requires further studies

Reliability analysis of successive Corneal Visualization Scheimpflug Technology measurements in different keratoconus stages

ABSTRACT. Background: This study assesses the reliability of successive corneal biomechanical response measurements by the Corneal Visualization Scheimpflug Technology (CST, Corvis ST , Oculus Optikger€ate, Wetzlar, Germany) in different keratoconus (KC) stages. Methods: A total of 173 eyes (15 controls: 15 eyes, and 112 KC patients: stages 1|1–2|2|2–3|3|3–4|4, n = 26|16|36|18|31|26|5 according to Topographical KC Classification, TKC) were repeatedly examined five times with the CST, each after repositioning the patient’s head and re-adjusting the device. Tomographical analysis (Pentacam HR ; Oculus, Wetzlar, Germany) was performed once before and once after CST measurements. Outcome measures included (1) A1 velocity, (2) deformation amplitude (DA) ratio 2 mm, (3) integrated radius, (4) stiffness parameter A1 and (5) Ambrosio relational thickness to the horizontal profile (ARTh). The Corvis Biomechanical Index (CBI) is reported to be extracted out of these parameters. Mean values of the five measurements and Cronbach’s a were calculated as a measure for reliability. Results: Ambrosio relational thickness to the horizontal profile and SPA1 were significantly higher in controls (534|123) compared to TKC1 (384|88), TKC2 (232|66), TKC3 (152|55) and TKC4 (71|27; p < 0.0001). The other parameters were similar in controls and TKC1 (A1 velocity: 0.148|0.151 m/s; integrated radius: 8.2|8.6 mm 1 ), but significantly higher in TKC stages 2 to 4 (DA ratio 2 mm: 5.5|6.3|8.0; A1 velocity: 0.173|0.174|0.186 m/second; integrated radius: 10.9|12.8|19.0 mm 1 ; p < 0.0001). All parameters proved to be highly reliable (Cronbach’s a ≥ 0.834) and the corneal tomography remained unaffected. Conclusions: The individual parameters included in the CBI (consisting of ARTh, SPA1, DA ratio 2 mm, A1 velocity and integrated radius) are highly reliable but differ KC stage-dependently

Approval rates for corneal donation and the origin of donor tissue for transplantation at a university-based tertiary referral center with corneal subspecialization hosting a LIONS Eye Bank

Background: With the increasing demand for corneas, eye banks must optimize the tissue donation, collection, and selection process. This retrospective monocentric study analyzed the approval rates for corneal donation and the origin of and reasons for discarding donor corneas from 2010 to 2019. Methods: Data included the number of deceased, approval or rejection by the family for corneal donation and contraindications. Corneal grafts were included from all deceased persons who were full-body and multi-organ donors at the Saarland University Medical Center (UKS) and from external institutions. Additional analyzed parameters included endothelial cell count (ECC), blood sample serology for infections, and conjunctival swab testing . Results: A total of 1748 corneoscleral buttons were harvested from 10,265 deceased persons (17% with no contraindication) at the UKS between 2010 and 2019, with a consent rate of 23.3%. The number of explants increased from 136 in 2010 (15% of the deceased, total = 925) to 251 in 2019 (21%, total = 1214). Both the general and departmentspecifc data showed similar percentages for corneal donation over the years, with intensive care and palliative units recently providing the most corneas. The increase in the number of corneas processed by the cornea bank over the years (368 in 2010 compared with 857 in 2019) was linked both to a better internal supply in 2010 (262, 71.2% of the total) compared with 2019 (519, 60.6%) and to an external supply by reinforcement of cooperation with external hospitals, including Luxembourg in 2010 (106, 28.8% of the total) compared with 2019 (338, 39.4%). A total of 195 of 377 corneas (52%) were discarded in 2009 compared with 260 out of 715 (36%) in 2019. The main reasons for discarding were low ECC (36% of discarded corneas in 2009; 11% in 2019), positive conjunctival swab (11% in 2009; 13% in 2019), and blood sample serology (6% in 2009 and in 2019). Conclusion: Despite an increasing number of donors, the demand for corneas is still rising. Improved cooperation with internal departments and with external clinics has led to an increasing number of explanted corneas. The main reason for discarding corneas was low ECC, followed by a positive conjunctival swab for fungal or bacterial contamination and serology. Increased donation rates and continued improvements in collection and selection processes are necessary to cover the high demand for cornea

Descemet's membrane endothelial keratoplasty is the predominant keratoplasty procedure in Germany since 2016: a report of the DOG-section cornea and its keratoplasty registry

Background/aims This retrospective multicentric panel study provides absolute numbers, types of and indications for corneal transplantation in Germany from 2011 to 2021 and sets them into the international context. Methods A questionnaire was sent to the 104 German ophthalmologic surgery departments and 93 (89%) provided their data. Results The number of reported keratoplasties more than doubled from 2011 (n=4474) to 2021 (n=8998). Lamellar keratoplasties (49% posterior (n=2883), 4% anterior (n=231)) surpassed penetrating keratoplasty (PKP, 47%, n=2721) for the first time in 2014. Since 2016, Descemet’s membrane endothelial keratoplasty (DMEK) has become the predominant keratoplasty procedure in Germany. Its number increased by 1.5-fold from 3850 (2016) to 5812 (2021). Main indications in 2021 were Fuchs’ endothelial corneal dystrophy (FECD, 43%), pseudophakic corneal decompensation (12%), repeated keratoplasty (11%), infections (7%), keratoconus (6%) and corneal scarring (4%, others: 9%). The PKP percentage decreased from 70.2% in 2011 (n=3141) to 31.7% in 2021 (n=2853). Descemet’s stripping (automated) endothelial keratoplasties (DSAEKs) decreased to 1% in 2021 (n=74). 98.6% of all posterior lamellar keratoplasties were DMEKs in Germany in 2021. The number of deep anterior lamellar keratoplasties (DALKs) remained comparable from 2011 (n=269) to 2021 (n=251, 2.8%). Conclusion Main indications for corneal transplantation in Germany (2021) were FECD and pseudophakic corneal decompensation. DMEK is by far the predominant corneal transplantation procedure since 2016 followed by PKP, whose absolute number decreased only slightly during the decade from 2011 to 2021. DALK proportions remain low, but stable, whereas DSAEK decreased annually and plays a minor role in Germany. Trial registration number NCT03381794

Intraocular juvenile xanthogranuloma of the iris in an adult patient

Purpose: Juvenile xanthogranuloma (JXG) is a rare histiocytic skin disease primarily of young children, which may also affect ocular structures and in particular the iris. Observations: This is a case report of a fifty-year-old patient without skin lesions showing a progressive decrease of visual acuity, iris vascularization and a yellowish iris tumor in the iridocorneal angle of his right eye. Treatment with topical and systemic prednisolone led to full recovery of visual acuity, tumor regression and restitutio ad integrum. Conclusions and importance: Although the juvenile xanthogranuloma is a very rare skin disease of young children, it may also affect the eye and in particular the iris in adult patients. There exists no standard treatment, the first-line therapy in most cases, however, is topical and systemic prednisolone application

Reliability analysis of successive Corvis ST® measurements in keratoconus 2 years after accelerated corneal crosslinking compared to untreated keratoconus corneas

Purpose To assess the reliability of successive Corvis ST® measurements (CST, Oculus, Wetzlar, Germany) in keratoconus (KC)≥2 years after accelerated corneal crosslinking (9 mW/cm2 , 10 min, 5.4 J/cm2 ) compared to untreated KC corneas. Methods Three successive CST measurements per eye were performed in≥2 years after CXL (CXLG, n=20 corneas of 16 patients) and a control group consisting of non-operated, ABC-stage-matched KC corneas according to Belin’s ABCD KC grading (controls, n=20 corneas, 20 patients). Main outcome measures included maximal keratometry (Kmax), the Belin/ Ambrósio-Enhanced-Ectasia-Deviation-Index BAD-D; the biomechanical parameters A1 velocity, deformation amplitude (DA) ratio 2 mm, Ambrósio relational thickness to the horizontal profle (ARTh), integrated radius, stifness parameter A1 (SP-A1), and the Corvis Biomechanical Factor (CBiF, the linearized term of the Corvis Biomechanical Index). Mean values, standard deviations, and Cronbach’s alpha (CA) were calculated. Results Both groups were tomographically comparable (BAD: 11.5±4.7|11.2±3.6, p=0.682, Kmax: 60.5±7.2|60.7±7.7, p=0.868 for controls|CXLG, paired t-test). A1 velocity (mean±SD: 0.176±0.02|0.183±0.02, p=0.090, CA: 0.960|0.960), DA ratio 2 mm (6.04±1.13|6.14±1.03, p=0.490, CA: 0.967|0.967), integrated radius (12.08±2.5|12.42±1.9, p=0.450, CA: 0.976|0.976), and CBiF (4.62±0.6|4.62±0.4, p=0.830, CA: 0.965|0.965) were also comparable (controls|CXLG). ARTh was signifcantly higher in controls (177.1±59, CA: 0.993) than after CXL (155.21±65, p=0.0062, CA: 0.993) and SP-A1 was signifcantly higher after CXL (59.2±13, CA: 0.912) than in controls (52.2±16, p=0.0018, CA: 0.912). Conclusion ARTh and SP-A1 difered signifcantly between controls and CXLG. Biomechanical measurements were generally of excellent reliability in both groups. CXL seems to afect biomechanical measurements of human corneas over more than 2 years

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

Hintergrund und Zielsetzung In 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 Methoden Es 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. Ergebnisse Es 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 %). Schlussfolgerungen In 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.Background and objective In the last decades increasingly more systems of artificial intelligence have been established in medicine, which identify diseases or pathologies or discriminate them from complimentary diseases. Up to now the Corvis®ST (Corneal Visualization Scheimpflug Technology, Corvis®ST, Oculus, Wetzlar, Germany) yielded a binary index for classifying keratoconus but did not enable staging. The purpose of this study was to develop a prediction model, which mimics the topographic keratoconus classification index (TKC) of the Pentacam high resolution (HR, Oculus) with measurement parameters extracted from the Corvis®ST. Patients and methods In this study 60 measurements from normal subjects (TKC 0) and 379 eyes with keratoconus (TKC 1–4) were recruited. After measurement with the Pentacam HR (target parameter TKC) a measurement with the Corvis®ST device was performed. From this device 6 dynamic response parameters were extracted, which were included in the Corvis biomechanical index (CBI) provided by the Corvis®ST (ARTh, SP-A1, DA ratio 1 mm, DA ratio 2 mm, A1 velocity, max. deformation amplitude). In addition to the TKC as the target, the binarized TKC (1: TKC 1–4, 0: TKC 0) was modelled. The performance of the model was validated with accuracy as an indicator for correct classification made by the algorithm. Misclassifications in the modelling were penalized by the number of stages of deviation between the modelled and measured TKC values. Results A total of 24 different models of supervised machine learning from 6 different families were tested. For modelling of the TKC stages 0–4, the algorithm based on a support vector machine (SVM) with linear kernel showed the best performance with an accuracy of 65.1% correct classifications. For modelling of binarized TKC, a decision tree with a coarse resolution showed a superior performance with an accuracy of 95.2% correct classifications followed by the SVM with linear or quadratic kernel and a nearest neighborhood classifier with cubic kernel (94.5% each). Conclusion This study aimed to show the principle of supervised machine learning applied to a set-up for the modelled classification of keratoconus staging. Preprocessed measurement data extracted from the Corvis®ST device were used to mimic the TKC provided by the Pentacam device with a series of different algorithms of machine learning