Pentacam HR System Research Articles

The development of a machine learning model to train junior ophthalmologists in diagnosing the pre-clinical keratoconus.

This study aims to evaluate the diagnostic performance of a machine learning model (ML model) to train junior ophthalmologists in detecting preclinical keratoconus (PKC). A total of 1,334 corneal topography images (The Pentacam HR system) from 413 keratoconus eyes, 32 PKC eyes and 222 normal eyes were collected. Five junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the ML model. The diagnostic performance of PKC was evaluated among three groups: junior ophthalmologist group (control group), ML model group and ML model-training junior ophthalmologist group (test group). The accuracy of the ML model between the eyes of patients with KC and NEs in all three clinics (99% accuracy, area under the receiver operating characteristic (ROC) curve AUC of 1.00, 99% sensitivity, 99% specificity) was higher than that for Belin-Ambrósio enhanced ectasia display total deviation (BAD-D) (86% accuracy, AUC of 0.97, 97% sensitivity, 69% specificity). The accuracy of the ML model between eyes with PKC and NEs in all three clinics (98% accuracy, AUC of 0.96, 98% sensitivity, 98% specificity) was higher than that of BAD-D (69% accuracy, AUC of 0.73, 67% sensitivity, 69% specificity). The diagnostic accuracy of PKC was 47.5% (95%CI, 0.5-71.6%), 100% (95%CI, 100-100%) and 94.4% (95%CI, 14.7-94.7%) in the control group, ML model group and test group. With the assistance of the proposed ML model, the diagnostic accuracy of junior ophthalmologists improved with statistical significance (p < 0.05). According to the questionnaire of all the junior ophthalmologists, the average score was 4 (total 5) regarding to the comprehensiveness that the AI model has been in their keratoconus diagnosis learning; the average score was 4.4 (total 5) regarding to the convenience that the AI model has been in their keratoconus diagnosis learning. The proposed ML model provided a novel approach for the detection of PKC with high diagnostic accuracy and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses.

MCSTransWnet: A new deep learning process for postoperative corneal topography prediction based on raw multimodal data from the Pentacam HR system

This work provides a new multimodal fusion generative adversarial net (GAN) model, Multiple Conditions Transform W-net (MCSTransWnet), which primarily uses femtosecond laser arcuate keratotomy surgical parameters and preoperative corneal topography to predict postoperative corneal topography in astigmatism-corrected patients. The MCSTransWnet model comprises a generator and a discriminator, and the generator is composed of two sub-generators. The first sub-generator extracts features using the U-net model, vision transform (ViT) and a multi-parameter conditional module branch. ​The second sub-generator uses a U-net network for further image denoising. The discriminator uses the pixel discriminator in Pix2Pix. Currently, most GAN models are convolutional neural networks; however, due to their feature extraction locality, it is difficult to comprehend the relationships among global features. Thus, we added a vision Transform network as the model branch to extract the global features. It is normally difficult to train the transformer, and image noise and geometric information loss are likely. Hence, we adopted the standard U-net fusion scheme and transform network as the generator, so that global features, local features, and rich image details could be obtained simultaneously. Our experimental results clearly demonstrate that MCSTransWnet successfully predicts postoperative corneal topographies (structural similarity ​= ​0.765, peak signal-to-noise ratio ​= ​16.012, and Fréchet inception distance ​= ​9.264). Using this technique to obtain the rough shape of the postoperative corneal topography in advance gives clinicians more references and guides changes to surgical planning and improves the success rate of surgery.

Angle κ and its effect on the corneal elevation maps in refractive surgery candidates.

To assess the associations of angle κ and Pentacam decentration indices with elevation maps in normal refractive surgery candidates. Salouti Cornea Research Center, Salouti Eye Clinic, Shiraz, Iran. Retrospective observational study. In this research, the right eyes of 173 refractive surgery candidates were assessed. Data of front and back corneal elevation maps, keratometric data, decentration indices, and corneal astigmatism obtained by Pentacam HR system and angle κ obtained by Orbscan IIz were extracted. Maximum elevation (or depression) for each of the 4 quadrants was recorded. Correlations of elevation values with angle κ, Pentacam decentration indices, keratometry, and astigmatism were examined by Pearson correlation coefficient. 148 age- and sex-matched cases with keratoconus grade 1 were selected as a positive control group, and Pentacam variables were compared between the groups. Overall, data from 173 eyes of 173 normal refractive surgery candidates and 148 eyes of 148 patients with keratoconus were recorded and analyzed. In normal refractive surgery candidates, the mean of angle κ was 5.32 ± 1.36 (SD) degrees. Angle κ had a positive correlation with front and back temporal elevations based on a best-fit sphere (BFS) ( r = 0.339, P = .001; r = 0.300, P < .001, respectively). Front and back keratometric astigmatisms were positively correlated with front and back nasal and temporal elevations ( r ≥ 0.543, P < .001) and negatively correlated with superior and inferior elevations ( r ≤ -0.547, P < .001). These associations no longer existed when using best-fit toric ellipse (BFTE) for calculating elevation data. The thinnest point to vertex decentration was significantly associated with the back temporal elevation (based on the BFTE) in both normal ( r = 0.311, P < .001) and keratoconus ( r = 0.190, P = .021) eyes. This study showed that elevation maps obtained by the Pentacam system using BFS might be affected by both the large angle κ and corneal astigmatism, confounding the preoperative assessment of refractive surgery candidates. Using BFTE as a reference for calculating elevation data should eliminate most diagnostic issues and thus is encouraged in this context.

Change Patterns in Corneal Intrinsic Aberrations and Nerve Density after Cataract Surgery in Patients with Dry Eye Disease.

This study aimed to evaluate the change patterns in corneal intrinsic aberrations and nerve density after cataract surgery in dry eye disease. The preoperative, 1- and 3-month postoperative dry eye-related parameters were obtained by the Oculus keratograph and the ocular surface disease index questionnaire. The corneal intrinsic aberrations were measured using the Pentacam HR system. In vivo confocal microscopy was performed to observe the vortical and peripheral corneal nerves. An artificial intelligence technique run by the deep learning model generated the corneal nerve parameters. Corneal aberrations on the anterior and total corneal surfaces were significantly increased at 1 month compared with the baseline (p < 0.05) but gradually returned to the baseline by 3 months (p > 0.05). However, the change in posterior corneal aberration lasted up to 3 months (p < 0.05). There was a significant decrease in the corneal vortical nerve maximum length and average density after the operation (p < 0.05), and this damage lasted approximately 3 months. The corneal vortical nerve maximum length and average density were negatively correlated with the anterior corneal surface aberrations before and 1 month after the operation (correlation coefficients, CC = −0.26, −0.25, −0.28; all p < 0.05). Corneal vortex provided a unique site to observe long-term corneal nerve injury related to eye dryness. The continuous damage to the corneal vortical nerve may be due to the continuous dry eye state.

Comparison of Accuracy in Keratometries Measured by Different Camera Devices for Predicting the Intraocular Lens Power in Lens-Dislocated Patients

Introduction: This is a cross-sectional cohort study focused on assessing the influence of ocular biometric parameters of different camera devices for accurately predicting the intraocular lens (IOL) power in congenital ectopia lentis (EL) patients. Methods: This study includes a total of 91 eyes of 60 patients with congenital EL from June 2018 to April 2021. All patients underwent lens subluxation surgery with Cionni modified capsular tension rings implantation. Ocular parameters measured by partial coherence interferometry (IOLMaster 700; Carl Zeiss Meditec AG, Jena, Germany) and rotating Scheimpflug camera (Pentacam HR system; Oculus Optikgeräte GmbH, Wetzlar, Germany) were acquired from the database. The authenticity of the different keratometries (Ks) was analyzed by comparing the prediction error in spherical equivalent under controlled formula SRK/T, Haigis, and after Wang-Koch (WK) adjustment. Results: We observed significant greater K values were obtained in IOLMaster than Pentacam, resulting in more significant hyperopia error while calculating SRK/T. The IOL power calculated with the total corneal refractive power (TCRP) from Pentacam revealed the highest prediction accuracy, indicating that TCRP is the closest to the actual refractive power of the cornea. However, in an exceptional case for long eye patients, total K from IOLMaster was better recommended when using formula Haigis with WK adjustment. Conclusions: For most instances, TCRP is the best-recommended source of K value while calculating IOL power for EL patients. However, the total K from IOLMaster preferably fits for long eye patients, who require WK adjustment for Haigis formula.

Evaluation of artificial intelligence models for the detection of asymmetric keratoconus eyes using Scheimpflug tomography.

To evaluate artificial intelligence (AI) models based on objective indices and raw corneal data from the Scheimpflug Pentacam HR system (OCULUS Optikgeräte GmbH, Wetzlar, Germany) for the detection of clinically unaffected eyes in patients with asymmetric keratoconus (AKC) eyes. A total of 1108 eyes of 1108 patients were enrolled, including 430 eyes from normal control subjects, 231 clinically unaffected eyes from patients with AKC, and 447 eyes from keratoconus (KC) patients. Eyes were divided into a training set (664 eyes), a test set (222 eyes) and a validation set (222 eyes). AI models were built based on objective indices (XGBoost, LGBM, LR and RF) and entire corneal raw data (KerNet). The discriminating performances of the AI models were evaluated by accuracy and the area under the ROC curve (AUC). The KerNet model showed great overall discriminating power in the test (accuracy=94.67%, AUC=0.985) and validation (accuracy=94.12%, AUC=0.990) sets, which were higher than the index-derived AI models (accuracy=84.02%-86.98%, AUC=0.944-0.968). In the test set, the KerNet model demonstrated good diagnostic power for the AKC group (accuracy=95.24%, AUC=0.984). The validation set also proved that the KerNet model was useful for AKC group diagnosis (accuracy=94.12%, AUC=0.983). KerNet outperformed all the index-derived AI models. Based on the raw data of the entire cornea, KerNet was helpful for distinguishing clinically unaffected eyes in patients with AKC from normal eyes.

Change patterns in the corneal sub-basal nerve and corneal aberrations in patients with dry eye disease: An artificial intelligence analysis

We aimed to investigate the change patterns in corneal sub-basal nerve morphology and corneal intrinsic aberrations in dry eye disease (DED). Our study included 229 eyes of 155 patients with DED and 40 eyes of 20 healthy control. We used the Oculus keratograph and the ocular surface disease index questionnaire to assess their signs and symptoms. In vivo confocal microscopy was used to observe the corneal sub-basal nerves, corneal endothelial cells, and Langerhans cells (LCs). An artificial intelligence (AI) technique run by the deep learning model generated the sub-basal nerve fibre parameters. Furthermore, we used the Pentacam HR system to measure the corneal intrinsic aberrations and corneal surface regularity indices. DED patients more frequently had increased anterior and total corneal aberrations than controls (P < 0.05). In addition, DED had decreased average density and maximum length of corneal nerve. (Both P < 0.01) The LC number was significantly correlated with maximum length (CC = −0.19, P = 0.01) of the sub-basal nerve fibre. Furthermore, the corneal nerve average density was negatively correlated with IHD, and anterior, posterior, and total corneal aberrations (All P < 0.05) especially the higher-order aberrations. Significant correlations were seen between corneal nerve morphology changes, analysed by AI and corneal intrinsic aberrations, particularly higher-order aberrations.

KerNet: A Novel Deep Learning Approach for Keratoconus and Sub-Clinical Keratoconus Detection Based on Raw Data of the Pentacam HR System

Keratoconus is one of the most severe corneal diseases, which is difficult to detect at the early stage (i.e., sub-clinical keratoconus) and possibly results in vision loss. In this paper, we propose a novel end-to-end deep learning approach, called KerNet, which processes the raw data of the Pentacam HR system (consisting of five numerical matrices) to detect keratoconus and sub-clinical keratoconus. Specifically, we propose a novel convolutional neural network, called KerNet, containing five branches as the backbone with a multi-level fusion architecture. The five branches receive five matrices separately and capture effectively the features of different matrices by several cascaded residual blocks. The multi-level fusion architecture (i.e., low-level fusion and high-level fusion) moderately takes into account the correlation among five slices and fuses the extracted features for better prediction. Experimental results show that: (1) our novel approach outperforms state-of-the-art methods on an in-house dataset, by ~1% for keratoconus detection accuracy and ~4 for sub-clinical keratoconus detection accuracy; (2) the attention maps visualized by Grad-CAM show that our KerNet places more attention on the inferior temporal part for sub-clinical keratoconus, which has been proved as the identifying regions for ophthalmologists to detect sub-clinical keratoconus in previous clinical studies. To our best knowledge, we are the first to propose an end-to-end deep learning approach utilizing raw data obtained by the Pentacam HR system for keratoconus and subclinical keratoconus detection. Further, the prediction performance and the clinical significance of our KerNet are well evaluated and proved by two clinical experts. Our code is available at https://github.com/upzheng/Keratoconus.

Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning

BackgroundThe purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs.MethodsWe trained a DL model using a dataset of anterior segment photographs collected from Shanghai Aier Eye Hospital from June 2018 to December 2019. A Pentacam HR system was used to capture a 2D overview eye image and measure the ACD. Shallow ACD was defined as ACD less than 2.4 mm. The DL model was evaluated by a five-fold cross-validation test in a hold-out testing dataset. We also evaluated the DL model by testing it against two glaucoma specialists. The performance of the DL model was calculated by metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).ResultsA total of 3753 photographs (1720 shallow AC and 2033 deep AC images) were assigned to the training dataset, and 1302 photographs (509 shallow AC and 793 deep AC images) were held out for two internal testing datasets. In detecting shallow ACD in the internal hold-out testing dataset, the DL model achieved an AUC of 0.86 (95% CI, 0.83–0.90) with 80% sensitivity and 79% specificity. In the same testing dataset, the DL model also achieved better performance than the two glaucoma specialists (accuracy of 80% vs. accuracy of 74 and 69%).ConclusionsWe proposed a high-performing DL model to automatically detect shallow ACD from overview anterior segment photographs. Our DL model has potential applications in detecting and monitoring shallow ACD in the real world.Trial registrationhttp://clinicaltrials.gov, NCT04340635, retrospectively registered on 29 March 2020.

The effect of smoking on corneal densitometry and endothelial cell morphology

Purpose We investigated corneal endothelial morphology and corneal densitometry in smokers and compared our results with findings observed in non-smokers. Materials and Methods This cross-sectional observational study included 100 participants (50 smokers, 50 non-smokers) aged 18–80 years in whom corneal endothelial morphology was analysed using a non-contact Tomey EM-4000 specular microscope (Tomey Corporation, Japan). The Pentacam HR system was used to measure corneal densitometry spatially in three concentric zones (from the centre to the periphery) and at three different corneal depths (from the anterior to the posterior aspects). Endothelial morphology findings and corneal densitometry values were recorded in all participants, and these results were compared between smokers and non-smokers. Results Endothelial morphology and corneal densitometry analysis showed significantly lower endothelial cell counts (Num) in smokers (228 cells/mm2 vs. 246 cells/mm2, p = 0.02) in addition to increased maximum cell area (Max) values (986.5 µm2 vs. 935 µm2, p = 0.04). We observed no statistically significant intergroup difference in corneal densitometry values (p > 0.05 for each zone); however, we observed a moderately positive correlation between densitometry values in the 6–10 mm concentric zone and between the all total corneal zones and number of pack-years in smokers. Conclusions Our study highlights that among the morphometric corneal endothelial variables analysed in this study, only the Num value was significantly correlated with smoking. We observed no statistically significant intergroup difference in corneal densitometry values in this study; however, a positive correlation was observed between the number of pack-years and corneal densitometry findings. Therefore, as the pack-years increase, the increase in corneal densitometry values may indicate a decrease in corneal clarity, considering the possible contribution of secondary factors such as age.

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

To investigate topographic, tomographic, topometric, densitometric, and aberrometric parameters in subclinical keratoconus with the Pentacam HR imaging system. 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. 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 0-2mm annular area and in the anterior and central layers of the 2-6mm 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 0-2mm 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. Corneal densitometry values in the 0-2 and 2-6mm annular areas, especially in the anterior layers, are parameters that can be used to predict and distinguish subclinical keratoconus from normal eyes.

Comparative assessment of functional outcomes of different endothelial keratoplasty techniques

Comparative analysis of the higher-order aberrations (HOA) of the cornea after endothelial keratoplasty (EK) using DSAEK and DMEK techniques; evaluation of the correlation between higher-order corneal aberrations and functional outcomes of the treatment. The study included 47 patients (59 eyes). The main group consisted of 35 patients (35 eyes) with bullous keratopathy and Fuchs' endothelial dystrophy; among them, 19 patients underwent endothelial keratoplasty using the DSAEK technique and 16 - DMEK technique. The control group included 12 patients (24 eyes) of corresponding age without corneal pathology. Standard and special examination tests were performed before, as well as 6 and 12 months after the operation, including computed tomography and aberrometry of the cornea using the Pentacam HR system. Same tests were performed in the control group. The values of the main HOA of the anterior corneal surface: RMS coma (Z3±1, Z5±1), RMS spherical aberration (SA; Z40, Z60) and RMS HOA (Z3-6) 6 months after different EK techniques were statistically significantly higher than in the control group. A statistically significantly lower induction of aberrations of the posterior corneal surface after DMEK technique was revealed. Analysis of the final aberration pattern 6 months after surgery did not reveal any statistically significant differences in the values of the main (RMS coma, RMS SA, RMS HOA) total corneal aberrations after DSAEK and DMEK. A negative correlation was revealed between the best corrected visual acuity (BCVA) and HOA; however, it was statistically significant only for individual aberration values: RMS HOA (Z3-6) of the posterior corneal aberrations after DMEK (r= -0.634, p=0.049), and RMS coma (Z3±1, Z5±1) of the cornea after DSAEK (r= -0.57, p=0.042). Comparative analysis of total corneal HOA did not reveal a statistically significant difference in the main parameters: RMS coma, RMS SA, RMS HOA in patients after DSAEK and DMEK surgeries. Correlation analysis did not reveal any statistically significant effects of most aberrations on the functional outcomes of the treatment.

Clinical utility of irx3 in keratoconus

ABSTRACT Clinical relevance Diagnosis and monitoring of keratoconus is increasingly being conducted with the aid of imaging equipment such as corneal aberrometry. There is a need to also know the confidence with which ocular aberration measurements can be made. Background To assess the repeatability of lower‐ and higher‐order aberration measurements in patients with keratoconus using the irx3 wavefront aberrometer (Imagine Eyes, Orsay, France) and evaluate correlations with corneal curvature. Methods The irx3 wavefront aberrometer was used to measure bilateral lower‐ and higher‐order ocular aberrations on 33 participants with keratoconus. Three measurements were taken from each eye to determine the repeatability of lower‐order aberrations (quantified as sphere and cylinder in dioptres) and higher‐order aberration co‐efficients (up to eighth order in micrometres), coma, trefoil and total higher‐order aberration root mean square (in micrometres). Corneal curvature was measured using the Pentacam HR system (OCULUS, Wetzlar, Germany). Results Repeat measurements for lower‐order aberrations resulted in larger co‐efficients of repeatability than higher‐order aberrations. Similarly, larger co‐efficients of repeatability between repeated measures across all Zernike co‐efficients were observed in eyes with severe keratoconus (that is, corneal curvature > 52-D) compared to eyes with flatter corneas. The difference between repeated measures tended to be significant for the lower‐order aberrations regardless of corneal curvature. The highest correlations with corneal curvature for right and left eyes respectively, were identified for total higher‐order aberration root mean square (r = 0.92, p < 0.001 and r = 0.91, p < 0.001), followed closely by coma (r = −0.93, p < 0.001 and r = −0.86, p < 0.001) and the Z (3, −1) co‐efficient (r = −0.92, p < 0.001 and r = −0.86, p < 0.001 for right and left eyes, respectively). Conclusions Lower‐order aberrations tended to be less repeatable, indicating that instrument variability must be considered when monitoring progression. Total higher‐order aberration root mean square and third‐order aberrations, in particular the vertical coma Z (3, −1) co‐efficient, demonstrated a stronger correlation with corneal curvature than the lower‐order aberrations.

Screening Candidates for Refractive Surgery With Corneal Tomographic–Based Deep Learning

Evaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans. To examine the use of a deep learning model in the screening of candidates for refractive surgery. A diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, to June 28, 2019. Participants included 1385 patients; 6465 corneal tomographic images were used to generate the artificial intelligence (AI) model. The Pentacam HR system was used for data collection. The deidentified images were analyzed by ophthalmologists and the AI model. The performance of the AI classification system. A classification system centered on the AI model Pentacam InceptionResNetV2 Screening System (PIRSS) was developed for screening potential candidates for refractive surgery. The model achieved an overall detection accuracy of 94.7% (95% CI, 93.3%-95.8%) on the validation data set. Moreover, on the independent test data set, the PIRSS model achieved an overall detection accuracy of 95% (95% CI, 88.8%-97.8%), which was comparable with that of senior ophthalmologists who are refractive surgeons (92.8%; 95% CI, 91.2%-94.4%) (P = .72). In distinguishing corneas with contraindications for refractive surgery, the PIRSS model performed better than the classifiers (95% vs 81%; P < .001) in the Pentacam HR system on an Asian patient database. PIRSS appears to be useful in classifying images to provide corneal information and preliminarily identify at-risk corneas. PIRSS may provide guidance to refractive surgeons in screening candidates for refractive surgery as well as for generalized clinical application for Asian patients, but its use needs to be confirmed in other populations.

Corneal Curvature, Astigmatism, and Aberrations in Marfan Syndrome with Lens Subluxation: Evaluation by Pentacam HR System

Marfan syndrome (MFS) is associated with abnormalities of corneal biometric characteristics. We conducted a retrospective case-control study including 55 eyes of the MFS patients with lens subluxation and 53 normal eyes of the control subjects to evaluate the corneal curvature, astigmatism and aberrations using a rotating Scheimpflug camera (Pentacam HR). Compared with the control group, the anterior, posterior, and total corneal curvature were flatter in the MFS group. The anterior and total corneal astigmatism were higher in the MFS patients, whereas the posterior corneal astigmatism was not significantly different between the two groups. Regarding the total corneal aberrations, the root mean square (RMS) aberrations, RMS higher-order aberrations and RMS lower-order aberrations increased, whereas the spherical aberration decreased in the MFS patients. Corneal parameters had potential diagnostic values for MFS patients with lens subluxation and the more reasonable cutoffs were the values of corneal curvature <41.35 D, corneal astigmatism >0.85 D and spherical aberration <0.188 μm. Corneal biometric characteristics of MFS patients with lens subluxation include decreased corneal curvature, higher corneal astigmatism, larger corneal aberrations, and lower spherical aberration. Corneal curvature, corneal astigmatism, and spherical aberration are better diagnostic tools for suspicious MFS.

Characteristic of entire corneal topography and tomography for the detection of sub-clinical keratoconus with Zernike polynomials using Pentacam

The study aimed to characterize the entire corneal topography and tomography for the detection of sub-clinical keratoconus (KC) with a Zernike application method. Normal subjects (n = 147; 147 eyes), sub-clinical KC patients (n = 77; 77 eyes), and KC patients (n = 139; 139 eyes) were imaged with the Pentacam HR system. The entire corneal data of pachymetry and elevation of both the anterior and posterior surfaces were exported from the Pentacam HR software. Zernike polynomials fitting was used to quantify the 3D distribution of the corneal thickness and surface elevation. The root mean square (RMS) values for each order and the total high-order irregularity were calculated. Multimeric discriminant functions combined with individual indices were built using linear step discriminant analysis. Receiver operating characteristic curves determined the diagnostic accuracy (area under the curve, AUC). The 3rd-order RMS of the posterior surface (AUC: 0.928) obtained the highest discriminating capability in sub-clinical KC eyes. The multimeric function, which consisted of the Zernike fitting indices of corneal posterior elevation, showed the highest discriminant ability (AUC: 0.951). Indices generated from the elevation of posterior surface and thickness measurements over the entire cornea using the Zernike method based on the Pentacam HR system were able to identify very early KC.

Ocular biomechanical measurements on post-keratoplasty corneas using a Scheimpflug-based noncontact device.

To analyse ocular biomechanical properties, central corneal thickness (CCT) and intraocular pressure (IOP) in post-keratoplasty eyes, as compared to normal subjects, with a new Scheimpflug-based technology. Moreover, biomechanical data were correlated with the size and age of the donor and recipient corneas. Measurements were conducted on 46 eyes of 46 healthy patients without any corneal pathology (age: 53.83±20.8y) and 30 eyes of 28 patients after penetrating keratoplasty (age: 49.43±21.34y). Ten biomechanical parameters, the CCT and IOP were recorded by corneal visualization scheimpflug technology (CorVis ST) using high-speed Scheimpflug imaging. Keratometry values were also recorded using Pentacam HR system. Scheimpflug measurements were performed after 43.41±40.17mo (range: 11-128mo) after the keratoplasty and after 7.64±2.34mo (range: 5-14mo) of suture removal. Regarding the device-specific biomechanical parameters, the highest concavity time and radius values showed a significant decrease between these two groups (P=0.01 and P<0.001). None of other biomechanical parameters disclosed a significant difference. The CCT showed a significant difference between post-keratoplasty eyes as compared to normal subjects (P=0.003) using the CorVis ST device. The IOP was within the normal range in both groups (P=0.84). There were no significant relationships between the keratometric data, the size of the donor and recipient, age of the donor and recipient and biomechanical properties obtained by CorVis ST. The ocular biomechanics remain stable after penetrating keratoplasty according to the CorVis ST measurements. Only two from the ten device-specific parameters have importance in the follow-up period after penetrating keratoplasty.

Reliability of Pentacam HR Thickness Maps of the Entire Cornea in Normal, Post–Laser In Situ Keratomileusis, and Keratoconus Eyes

To measure the repeatability and reproducibility of Pentacam HR system thickness maps for the entire cornea in normal, post-laser insitu keratomileusis (post-LASIK), and keratoconus (KC) eyes. Reliability study. Sixty normal subjects (60 eyes), 30 post-LASIK subjects (60 eyes), and 14 KC patients (27 eyes) were imaged with the Pentacam HR system by 2 well-trained operators. For pachymetry the cornea was divided into 4 zones: a central zone (2-mm diameter) and concentric pericentral zone (2-5mm), transitional zone (5-7mm), and peripheral zone (7-10mm). The 3 concentric zones were subdivided into 8 sectors. Intraobserver repeatability and interobserver reproducibility of entire corneal thickness maps were tested by the repeatability and reproducibility coefficients, intraclass correlation coefficients, coefficient of variation, and 95% limits of agreement. From central to peripheral zones, the precision of corneal thickness measurements became gradually smaller. Central zone repeatability and reproducibility were the best in the normal, post-LASIK, and KC groups. The peripheral superior sectors showed poorer repeatability and reproducibility for all subjects. The intraobserver repeatability and interobserver reproducibility for all zones were ≤19.3μm, ≤22.1μm, and ≤20.7μm, in the normal, post-LASIK, and KC groups, respectively. The intraobserver and interobserver coefficients of variation for all zones were ≤1.3%, ≤1.6%, and ≤1.6% for all 3 groups. Pentacam HR system pachymetry of the entire cornea provided good precision in normal, post-LASIK, and KC corneas. Thickness measurements in the peripheral cornea should be interpreted with caution in abnormal corneas after surgery or with diseases.

Measurements of anterior chamber depth, white-to-white distance, anterior chamber angle, and pupil diameter using two Scheimpflug imaging devices.

To compare the ocular anterior chamber depth, white-to-white distance, anterior chamber angle, and pupil diameter, as measured with two different Scheimpflug imaging devices. This transversal study included 80 right eyes from 80 subjects aged from 20 to 40 years. Their spherical equivalents ranged from -4.25 to +1.00 diopters (D). Each eye's anterior chamber depth, white-to-white distance, anterior chamber angle, and pupil diameter, were measured for far vision using both the Galilei G4 (double Scheimpflug camera) and the Pentacam HR (single Scheimpflug camera) systems. Mean anterior chamber depths were calculated as 3.12 ± 0.23 mm and 3.19 ± 0.24 mm when measured with the Galilei G4 and the Pentacam HR, respectively. The mean white-to-white distance measured was 11.84 ± 0.31 mm and 11.90 ± 0.43 mm when measured with the Galilei G4 and the Pentacam HR, respectively. Mean pupil diameters were measured as 3.22 ± 0.58 mm and 3.22 ± 0.52 mm when measured with the Galilei G4 and the Pentacam HR, respectively. Finally, the mean anterior chamber angle was 34.30 ± 2.86 degrees when it was measured with the Galilei G4, and 39.26 ± 2.85 degrees when measured with the Pentacam HR. A comparative analysis revealed that the Galilei G4 yielded a significantly lower (P<0.05) measurement for the anterior chamber depth, anterior chamber angle, and pupil diameter, than the Pentacam HR system. Comparable values (P>0.05) for both devices were obtained for the white-to-white distance measurements. The Galilei G4 and Pentacam HR Scheimpflug systems cannot be used interchangeably because they produce significant measurement differences.

Objective evaluation of the changes in the crystalline lens during accommodation in young and presbyopic populations using Pentacam HR system.

To quantify the changes in the lens profile with accommodation in different age groups. The Pentacam HR system was used to obtain the images of the anterior eye segment from 23 young and 15 presbyopic emmetropic subjects in unaccommodated (with an accommodation stimulus of 0.0D) and accommodated (with an accommodation stimulus of 5.0D for the young group and 1.0D for the presbyopic group) states. The phakic crystalline lens shape, including curvature of crystalline lens and central lens thickness (CLT), and the measurements of anterior segment length (ASL), central anterior chamber depth (CACD) were investigated. The anterior chamber volume (ACV) was also measured. The reduction of CACD and ACV were significant in both groups after accommodation stimulus. From the profile of anterior eye segment, a significant decrease in anterior crystalline lens radii of curvature (-2.52mm) and a mean increase in CLT (0.222mm) and ASL (0.108mm) were found in the young group with an accommodation stimulus of 5.0D. However, no statistically significant changes of CLT, ASL, or crystalline lens radii of curvature were found in the presbyopic group. Our data showed that the shallowing of anterior chamber during accommodation was caused by the forward bulging of the anterior lens surface, rather than by anterior shifting of lens position in either young or presbyopic subjects.