AS-OCT Images Research Articles

Anterior Chamber Angle and Intraocular Pressure Control After Phacoemulsification in Primary Angle Closure With Different Mechanisms.

Different mechanisms of angle closure represented distinct aspects of intraocular pressure (IOP) control after phacoemulsification. Classification of angle closure mechanisms is necessary for postoperative IOP management and glaucoma progression in primary angle closure eyes. To investigate the relationship between the anterior chamber angle (ACA) characteristics, measured by swept-source anterior segment optical coherence tomography (SS AS-OCT), and intraocular pressure (IOP) control after phacoemulsification in eyes with primary angle closure disease (PACD) with different angle closure mechanisms. PACD eyes were classified into 3 groups according to angle closure mechanisms using preoperative SS AS-OCT images; pupillary block (PB), plateau iris configuration (PIC), exaggerated lens vault (ELV). This retrospective, clinical cohort study included eighty-five eyes of 85 PACD patients: 34 with PB, 23 with PIC, and 28 with ELV. ACA parameters were measured preoperatively and 1 month postoperatively using SS AS-OCT. IOP measurements were performed preoperatively and during 6 months postoperatively. Postoperative IOP reduction and fluctuation were calculated, and their correlations with SS AS-OCT parameters were analyzed. PIC group showed the lowest postoperative IOP reduction compared with the other groups ( P =0.023). Preoperative ACA measurements were significantly associated with postoperative IOP reduction in ELV and PB groups, while postoperative measurements were in PIC group. Preoperative and postoperative change of iridotrabecular contact (ITC) index and area were correlated with postoperative IOP reduction in PB and ELV groups but not in PIC group. Postoperative ITC index ( P =0.031) and area ( P =0.003) showed significant correlations with postoperative IOP fluctuation only in PIC group. SS AS-OCT parameters including ITC index and area showed different associations with postoperative IOP control, which should be considered in determination of lens extraction and treatment of PACD eyes.

Cascade spatial and channel-wise multifusion network with criss cross augmentation for corneal segmentation and reconstruction

High-quality 3D corneal reconstruction from AS-OCT images has demonstrated significant potential in computer-aided diagnosis, enabling comprehensive observation of corneal thickness, precise assessment of morphological characteristics, as well as location and quantification of keratitis-affected regions. However, it faces two main challenges: (1) prevalent medical image segmentation networks often struggle to accurately process low-contrast corneal regions, which is a vital pre-processing step for 3D corneal reconstruction, and (2) there are no reconstruction methods that can be directly applied to AS-OCT sequences with 180-degree scanning. To combat these, we propose CSCM-CCA-Net, a simple yet efficient network for accurate corneal segmentation. This network incorporates two key techniques: cascade spatial and channel-wise multifusion (CSCM), which captures intricate contextual interdependencies and effectively extracts low-contrast and obscure corneal features; and criss cross augmentation (CCA), which enhances shape-preserved feature representation to improve segmentation accuracy. Based on the obtained corneal segmentation results, we reconstruct the 3D volume data and generate a topographic map of corneal thickness through corneal image alignment. Additionally, we design a transfer function based on the analysis of intensity histogram and gradient histogram to explore more internal cues for better visualization results. Experimental results on CORNEA benchmark demonstrate the impressive performance of our proposed method in terms of both corneal segmentation and 3D reconstruction. Furthermore, we compare CSCM-CCA-Net with state-of-the-art medical image segmentation approaches using three challenging medical fundus segmentation datasets (DRIVE, CHASEDB1, FIVES), highlighting its superiority in terms of segmentation accuracy. The code and models will be made available at https://github.com/qianguiping/CSCM-CCA-Net.

SeqCorr-EUNet: A sequence correction dual-flow network for segmentation and quantification of anterior segment OCT image

The accurate segmentation of AS-OCT images is a prerequisite for the morphological details analysis of anterior segment structure and the extraction of clinical biological parameters, which play an essential role in the diagnosis, evaluation, and preoperative prognosis management of many ophthalmic diseases. Manually marking the boundaries of the anterior segment tissue is time-consuming and error-prone, with inherent speckle noise, various artifacts, and some low-quality scanned images further increasing the difficulty of the segmentation task. In this work, we propose a novel model called SeqCorr-EUNet with a dual-flow architecture based on convolutional gated recursive sequence correction for semantic segmentation and quantification of AS-OCT images. An EfficientNet encoder is employed to enhance the intra-slice features extraction ability of semantic segmentation flow. The sequence correction flow based on ConvGRU is introduced to extract inter-slice features from consecutive adjacent slices. Spatio-temporal information is fused to correct the morphological details of pre-segmentation results. And the channel attention gate is inserted into the skip-connection between encoder and decoder to enrich the contextual information and suppress the noise of irrelevant regions. Based on the segmentation results of the anterior segment structures, we achieved automatic extraction of essential clinical parameters, 3D reconstruction of the anterior chamber structure, and measurement of anterior chamber volume. The proposed SeqCorr-EUNet has been evaluated on the public AS-OCT dataset. The experimental results show that our method is competitive compared with the existing methods and significantly improves the segmentation and quantification performance of low-quality imaging structures in AS-OCT images.

Peripheral hypertrophic subepithelial degeneration – case report

Purpose: The aim of the paper is to present the case of a 53-year-old female patient who, based on the clinical picture and optical coherent tomography of the anterior segment (AS-OCT), was diagnosed with peripheral hypertrophic subepithelial degeneration (PHSD) and underwent surgical treatment in the form of superficial keratectomy with mechanical removal of degenerative plaques.
 Methods: Analysis of AS-OCT images and corneal tomography based on Scheimpflug images and surgical treatment – superficial keratectomy with mechanical removal of degenerative plaques.
 Results: Before treatment, the best corrected visual acuity (BCVA) for distance, as assessed by the Snellen chart, was 3/50 c.cor +10.50/-6.50 ax 18° in the right eye (RE) and in the left eye (LE) 5/10 c.cor +10.75/-3.00 ax 48°. Preoperative keratometry performed with the Pentacam device was K1 32.7 D and K2 46.0 D in the OP and K1 42.5 D and K2 44.7 D in the LE. Postoperative BCVA in RE was 5/8 c.cor +3.50/-1.00 ax 120° at 5 months of follow-up and in LE 5/6 c.cor +4.75/-1.00 ax 64° at 3rd month of observation. Postoperative keratometry (Pentacam) in RE K1 was 44.6 D, K2 46.9 D in the 5th month of observation, and in LE K1 45.6 D, K2 46.5 D in the 3rd month of observation. As a result of the treatment, BCVA improved, astigmatism decreased and corneal translucency improved.
 Conclusions: AS-OCT and Scheimpflug-based corneal tomography are helpful in the diagnosis of PHSD. Superficial keratectomy with mechanical removal of degenerative plaques seems to be an effective treatment for PHSD

Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches

Recent clinical studies have emphasized the importance of understanding the morphology and mechanics of the ciliary muscle. The ciliary muscle plays a vital role in various functions related to the anterior segment of the eye, including the regulation of intraocular pressure and the maintenance of the shape of the crystalline lens. To advance research in this area, we propose a fully automated methodology for the segmentation and biomarker measurement of the ciliary muscle in two different scan depths (6mm and 16mm), which are commonly used by clinicians to analyze biomarkers. Our methodology aims to provide repeatable, and immediate results through an exhaustive analysis of different network architectures, encoders, and transfer learning strategies. We also extracted a comprehensive set of relevant biomarkers, including parameters that provide essential information about its behavior during the accommodation process, overall dimensions, and biomechanical properties. These biomarkers can help clinicians and researchers in the diagnoses and monitor of different ocular diseases such as glaucoma, myopia, and presbyopia and develop new therapeutic strategies, potentially leading to more effective treatments and improved patient outcomes. Our methodology achieved accurate qualitative and quantitative results, with high accuracy values of 0.9665±0.1280 and 0.9772±0.0873 for the best combinations for 6mm and 16mm, respectively. Our proposed system provides a valuable and automatic tool for clinicians and researchers in the segmentation and analysis of the ciliary muscle in AS-OCT images.

Deep learning model to identify angle dysgenesis on ASOCT images

AbstractAnterior segment spectral domain–optical coherence tomography (ASOCT) is increasingly used in glaucoma patients, primarily to investigate the anterior chamber angle and visualize the trabecular meshwork (TM) and Schlemm's Canal (SC) in‐vivo. The anatomical variability of the drainage angle, TM and SC morphology makes it difficult to distinguish normal from abnormal. Therefore, the interpretation of ASOCT images requires expertise and a deep understanding of the complexity involved in the developmental anomalies of the outflow pathways. While the ASOCT scans can successfully capture the gross changes, they may fail to identify angle dysgenesis in cases where altered extracellular matrix anomalies are subtle enough to get unnoticed by the human eyes of an expert. Deep learning (DL) has demonstrated convincing results and potential in classifying ophthalmic diseases. The integration of high‐resolution ASOCT images trained DL algorithms can further enhance the translational potential of DL‐based classification for angle dysgenesis. Apart from the gross changes in the iridocorneal angle region, the perturbations in TM layers due to associated gene mutations are also captured on ASOCT scans and may be identified by DL algorithms. We have developed DL models, which can identify obscure features of angle dysgenesis in ASOCT scans (ADoA) and validated its performance in eyes with primary open angle glaucoma. Eight hundred ASOCT B‐scans were included, and each image was cropped to extract the iridio‐corneal angle (ICA) area and a TM area to form two datasets. Finally, the total number of images after augmentations in each dataset increased to 8160, divided into training and testing sets for model building. To avoid overfitting, incorporate variability and increase the size of the datasets, 23 different types of augmentation techniques were performed on each image using the CloDSA tool, a freely available package.Three deep learning models were developed for a consensus‐based outcome to identify ADoA objectively. The models performed well on the internal as well as external validation datasets. The model performance on the multiple independent datasets highlights the translational relevance of image‐based diagnostic alternatives in clinical settings, as it has the potential to be deployed for screening patients and their family members for detection of angle dysgenesis in vivo.

Automated Quantitative Analysis of Anterior Segment Inflammation Using Swept-Source Anterior Segment Optical Coherence Tomography: A Pilot Study.

The aim of this study is to develop an automated evaluation of anterior chamber (AC) cells in uveitis using anterior segment (AS) optical coherence tomography (OCT) images. We analyzed AS swept-source (SS)-OCT (CASIA 2) images of 31 patients (51 eyes) with uveitis using image analysis software (Python). An automated algorithm was developed to detect cellular spots corresponding to hyper-reflective spots in the AC, and the correlation with Standardization of Uveitis Nomenclature (SUN) grading AC cells score was evaluated. The approximated AC grading value was calculated based on the logarithmic approximation curve between the number of cellular spots and the SUN grading score. Among 51 eyes, cellular spots were automatically segmented in 48 eyes, whereas three eyes (all SUN grading AC cells score: 4+) with severe fibrin formation in the AC were removed by the automated algorithm. The AC cellular spots increased with an increasing SUN grading score (p < 0.001). The 48 eyes were split into training data (26 eyes) and test data (22 eyes). There was a significant correlation between the SUN grading score and the number of cellular spots in 26 eyes (rho: 0.843, p < 0.001). There was a significant correlation between the SUN grading score and the approximated grading value of 22 eyes based on the logarithmic approximation curve (rho: 0.774, p < 0.001). Leave-one-out cross-validation analysis demonstrated a significant correlation between the SUN grading score and the approximated grading value of 48 eyes (rho: 0.748, p < 0.001). This automated anterior AC cell analysis using AS SS-OCT showed a significant correlation with clinical SUN grading scores and provided SUN AC grading values as a continuous variable. Our findings suggest that automated grading of AC cells could improve the accuracy of a quantitative assessment of AC inflammation using AS-OCT images and allow the objective and rapid evaluation of anterior segment inflammation in uveitis. Further investigations on a large scale are required to validate this quantitative measurement of anterior segment inflammation in uveitic eyes.

Predicting demographic characteristics from anterior segment OCT images with deep learning: A study protocol.

IntroductionAnterior segment optical coherence tomography (AS-OCT) is a non-contact, rapid, and high-resolution in vivo modality for imaging of the eyeball’s anterior segment structures. Because progressive anterior segment deformation is a hallmark of certain eye diseases such as angle-closure glaucoma, identification of AS-OCT structural changes over time is fundamental to their diagnosis and monitoring. Detection of pathologic damage, however, relies on the ability to differentiate it from normal, age-related structural changes.Methods and analysisThis proposed large-scale, retrospective cross-sectional study will determine whether demographic characteristics including age can be predicted from deep learning analysis of AS-OCT images; it will also assess the importance of specific anterior segment areas of the eyeball to the prediction. We plan to extract, from SUPREME®, a clinical data warehouse (CDW) of Seoul National University Hospital (SNUH; Seoul, South Korea), a list of patients (at least 2,000) who underwent AS-OCT imaging between 2008 and 2020. AS-OCT images as well as demographic characteristics including age, gender, height, weight and body mass index (BMI) will be collected from electronic medical records (EMRs). The dataset of horizontal AS-OCT images will be split into training (80%), validation (10%), and test (10%) datasets, and a Vision Transformer (ViT) model will be built to predict demographics. Gradient-weighted Class Activation Mapping (Grad-CAM) will be used to visualize the regions of AS-OCT images that contributed to the model’s decisions. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) will be applied to evaluate the model performance.ConclusionThis paper presents a study protocol for prediction of demographic characteristics from AS-OCT images of the eyeball using a deep learning model. The results of this study will aid clinicians in understanding and identifying age-related structural changes and other demographics-based structural differences.Trial registrationRegistration ID with open science framework: 10.17605/OSF.IO/FQ46X.

Changes in Iris Stiffness and Permeability in Primary Angle Closure Glaucoma.

PurposeTo evaluate the biomechanical properties of the iris by evaluating iris movement during pupil constriction and to compare such properties between healthy and primary angle-closure glaucoma (PACG) subjects.MethodsA total of 140 subjects were recruited for this study. In a dark room, the anterior segments of one eye per subject were scanned using anterior segment optical coherence tomography imaging during induced pupil constriction with an external white light source of 1700 lux. Using a custom segmentation code, we automatically isolated the iris segments from the AS-OCT images, which were then discretized and transformed into a three-dimensional point cloud. For each iris, a finite element (FE) mesh was constructed from the point cloud, and an inverse FE simulation was performed to match the clinically observed iris constriction in the AS-OCT images. Through this optimization process, we were able to identify the elastic modulus and permeability of each iris.ResultsFor all 140 subjects (95 healthy and 45 PACG of Indian/Chinese ethnicity; age 60.2 ± 8.7 for PACG subjects and 57.7 ± 10.1 for healthy subjects), the simulated deformation pattern of the iris during pupil constriction matched well with OCT images. We found that the iris stiffness was higher in PACG than in healthy controls (24.5 ± 8.4 kPa vs. 17.1 ± 6.6 kPa with 40 kPa of active stress specified in the sphincter region; P < 0.001), whereas iris permeability was lower (0.41 ± 0.2 mm2/kPa s vs. 0.55 ± 0.2 mm2/kPa s; p = 0.142).ConclusionsThis study suggests that the biomechanical properties of the iris in PACG are different from those in healthy controls. An improved understanding of the biomechanical behavior of the iris may have implications for the understanding and management of angle-closure glaucoma.

Anterior chamber angle classification in anterior segment optical coherence tomography images using hybrid attention based pyramidal convolutional network

As the main cause of irreversible visual impairment, angle-closure glaucoma (ACG) can be detected with anterior segment optical coherence tomography (AS–OCT). The automatic classification of anterior chamber angles (ACA) into closed angle, narrowed angle and open angle in the AS–OCT images is highly significant for understanding glaucoma progression. The traditional techniques for image classification usually rely on the extraction of handcrafted features from the images. Despite the popularity of deep learning methods in image classification, very few researches have been done to utilize them for the multi-class classification of AS–OCT images. In this work, a deep learning based algorithm is proposed for accurate ACA classification in AS–OCT images. The proposed method learns the distinguishable representations from numerous training AS–OCT images using the pyramidal convolution which contains multi-scale of kernels, where each scale includes several kinds of filters with changing depth and size. In this way, it can facilitate capturing the different levels of subtle visual cues that cannot be modeled by the handcrafted features. In addition, the skip connection has been adopted to concatenate the feature maps output at different levels to explore the correlation among them better. Moreover, the hybrid attention module including spatial attention and channel attention is introduced to emphasize important spatial and channel-wise information and reduce redundant information to increase the classification accuracy. We have evaluated classification performance of the proposed algorithm on 2636 ACA images from the Zhongshan Ophthalmic Center, Sun Yat-sen University in China, which is the only public ACG dataset in the world. The experimental results show that our algorithm can classify ACA as closed angle, narrowed angle and open angle effectively and it outperforms existing popular deep learning methods by providing higher specificity, sensitivity, accuracy and balanced accuracy.

Состояние витреолентикулярного интерфейса после выполнения факоэмульсификации

Состояние витреолентикулярного интерфейса после выполнения факоэмульсификации

Differences in Ocular Biometric Measurements among Subtypes of Primary Angle Closure Disease: The Chinese American Eye Study

To assess differences in ocular biometric measurements between primary angle closure suspect (PACS) eyes and primary angle closure (PAC) and primary angle closure glaucoma (PACG) eyes. Cross-sectional study. Patients with primary angle closure disease (PACD) were identified from the Chinese American Eye Study, a population-based study in Los Angeles, California. Patients previously underwent complete ocular examinations including gonioscopy and anterior segment (AS)-OCT imaging with the Tomey CASIA SS-1000 (Tomey Corporation). Four AS-OCT images were analyzed per eye. Averaged and sectoral measurements of biometric parameters, including angle recess area (ARA), trabecular iris space area (TISA), iris area, iris curvature, lens vault, anterior chamber depth, and anterior chamber area, were compared between early PACD (PACS) and late PACD (PAC and PACG) groups. Machine learning classifiers that attempt to differentiate between early and late PACD eyes were developed by applying different regression algorithms to a training dataset of sectoral parameter measurements. Classifier performance was assessed using an independent test dataset. Averaged and sectoral measurements of biometric parameters. Two hundred ninety-eight eyes (231 PACS, 67 PAC or PACG) of 298 patients were analyzed. No difference was found in averaged biometric measurements between the 2 groups before (P > 0.09) or after (P > 0.14) adjusting for age and gender. Differences (P < 0.04) between the 2 groups were found for 11 sectoral parameter measurements, including ARA and TISA. The performance of machine learning classifiers developed using sectoral parameter measurements was poor on the independent test dataset for all regression algorithms (area under the receiver operating characteristic curve, 0.529-0.628). Differences in biometric measurements between subtypes of PACD eyes were small in a population-based cohort of Chinese Americans. The poor performance of classifiers based on these measurements highlights potential challenges of developing quantitative methods to detect late PACD.

Measurement of bleb height area and extent using anterior segment optical coherence tomography

To image trabecolectomy blebs using anterior segment optical coherence tomography AS-OCT and to measure the blebs morphological features such as bleb height area and extend. In this paper fuzzy local information C-means clustering is used to segment the bleb boundary. A batch of 25 AS-OCT images are used to assess the performance of the determined parameters to the clinical parameters, and 91.43% accuracy is obtained in the determined parameters result. The mean value of bleb height, area and extend are 0.2 mm, 1.618 mm2, 0.343 mm respectively. The result shows the potential applicability of the method for automated and objective mass screening for detection of bleb boundary.

Measurement of bleb height area and extent using anterior segment optical coherence tomography

To image trabecolectomy blebs using anterior segment optical coherence tomography AS-OCT and to measure the blebs morphological features such as bleb height area and extend. In this paper fuzzy local information C-means clustering is used to segment the bleb boundary. A batch of 25 AS-OCT images are used to assess the performance of the determined parameters to the clinical parameters, and 91.43% accuracy is obtained in the determined parameters result. The mean value of bleb height, area and extend are 0.2 mm, 1.618 mm2, 0.343 mm respectively. The result shows the potential applicability of the method for automated and objective mass screening for detection of bleb boundary.

Automatic Identification and Representation of the Cornea-Contact Lens Relationship Using AS-OCT Images.

The clinical study of the cornea–contact lens relationship is widely used in the process of adaptation of the scleral contact lens (SCL) to the ocular morphology of patients. In that sense, the measurement of the adjustment between the SCL and the cornea can be used to study the comfort or potential damage that the lens may produce in the eye. The current analysis procedure implies the manual inspection of optical coherence tomography of the anterior segment images (AS-OCT) by the clinical experts. This process presents several limitations such as the inability to obtain complex metrics, the inaccuracies of the manual measurements or the requirement of a time-consuming process by the expert in a tedious process, among others. This work proposes a fully-automatic methodology for the extraction of the areas of interest in the study of the cornea–contact lens relationship and the measurement of representative metrics that allow the clinicians to measure quantitatively the adjustment between the lens and the eye. In particular, three distance metrics are herein proposed: Vertical, normal to the tangent of the region of interest and by the nearest point. Moreover, the images are classified to characterize the analysis as belonging to the central cornea, peripheral cornea, limbus or sclera (regions where the inner layer of the lens has already joined the cornea). Finally, the methodology graphically presents the results of the identified segmentations using an intuitive visualization that facilitates the analysis and diagnosis of the patients by the clinical experts.

Appearance of herpes simplex keratitis on anterior segment optical coherence tomography.

To describe the anterior segment AS-OCT findings of herpetic simplex keratitis. Forty-two eyes of 42 patients with proven herpetic keratitis of varying severity were included in this prospective, observational, non-comparative case series study. All subjects underwent ophthalmologic examination and AS-OCT imaging. Twenty-five of the 42 eyes (59.5%) had sub-epithelial infiltrates. In 11 of these cases, the overlying epithelium appeared intact, but in 14 cases, hydropic changes, heaping, and defects were observed. Seventeen eyes (40.5%) showed stromal involvement: six cases with scar at presentation and one case associated with uveitis. Stromal infiltrates were also seen, in ten cases, as a diffuse or local lentiform or spindle-shaped hyper-reflective area in the stroma. Herpetic keratouveitis has characteristic features on AS-OCT images. This study demonstrates that herpetic keratitis also has characteristic AS-OCT features, including sub-epithelial infiltration and specific stromal hyper-reflective patterns. These features are not unique to herpetic keratitis, but AS-OCT imaging may provide useful supplementary information for diagnosing and monitoring herpetic keratitis.

Corneal dystrophies in optical coherence tomography

Corneal optical coherence tomography (anterior segment OCT, AS-OCT) is described in the current IC3D classification of corneal dystrophies to be amethod for improvement of clinical diagnostics and treatment. In this case series AS-OCT images of corneal dystrophies were analyzed with respect to morphological changes. This was aretrospective imaging and morphological case series with 38 eyes. For image acquisition the corneal module of the high-resolution spectral-domain OCT Zeiss Cirrus HD-5000 platform (Oberkochen, Germany) was employed. The following corneal dystrophies were analyzed: epithelial basement membrane dystrophy, Meesmann corneal dystrophy, Reis-Bücklers corneal dystrophy, granular corneal dystrophy type1, granular corneal dystrophy type2 and macular corneal dystrophy. The AS-OCT images showed the typical changes of the dystrophies through hyperreflectivity and hyporeflectivity in the individual corneal layers. The findings in the AS-OCT images correlated well with the histological descriptions in the literature and provided additional information to the slit lamp examination, especially with respect to the exact location of the alterations. Corneal AS-OCT imaging seems to be ahelpful tool for determination of morphological changes in patients with corneal dystrophies and can facilitate both the diagnostics and surgical treatment decisions.

Comparison of the MyoRing implantation depth by mechanical dissection using PocketMaker microkeratome versus Melles\xa0hook via AS-OCT

BackgroundThis paper seeks to evaluate the depth and outcomes of MyoRing implantation using two mechanical dissection procedures including: PocketMaker microkeratome in opposition to the Melles hook method.MethodsThis retrospective study was carried out on 39 eyes of 38 keratoconus patients (28 male and 10 female) with the mean age of 28.97frac{+}{.}10.37 years and had undergone MyoRing implantation by the two mentioned methods. The MyoRing was inserted into the corneal pocket which was made manually in 18 eyes (Melles hook group) or with PocketMaker microkeratome in 21 eyes (PocketMaker group). The mean follow up time was 9.81frac{+}{.}3.7 months with pre-operative and post-operative ophthalmic examination including uncorrected visual acuity (UCVA), best-corrected visual acuity (BCVA), keratometry readings and central corneal thickness measurement. AS-OCT (Casia, SS-1000, Tomey, Nagoya, Japan) imaging was used to measure MyoRing insertion depth, exactly.ResultsPre-operative and post-operative UCVA (LogMAR) mean change for the PocketMaker and Melles hook groups were recorded at 0.75 ± 0.32 and 0.78 ± 0.33, respectively. Similarly, BCVA (LogMAR) mean change were 0.27 ± 0.22 and 0.23 ± 0.22. Mean keratometry (Kmean) change were 6.06 ± 4.18 and 6.56 ± 3.55 respectively. UCVA change (P = 0.767), BCVA change (P = 0.77) and Kmean change (P = 0.693) showed that there was no statistically significant difference between both groups for any parameter. Depth measurements achieved from AS-OCT images showed that there was no statistically significant difference in pocket depth between two methods of MyoRing implantation (P = 0.413).ConclusionsThe results of Myoring implantation outcomes using mechanical dissection via PocketMaker microkeratome as against Melles hook are comparable.

Imaging of proximal lacrimal system with time domain anterior segment optical coherence tomography in Asian Indian population

ABSTRACTThis article reports proximal lacrimal system parameters using Anterior Segment Optical Coherence Tomography (ASOCT-Visante) in an apparently normal Asian Indian population with assessment of any correlation between tear meniscus height and punctal diameter. This was a prospectively designed observational study that included healthy adults, who volunteered for optical coherence tomography imaging (OCT) of their proximal lacrimal system. Time domain ASOCT (Visante) images of the punctum and proximal canaliculus were captured using the high resolution corneal scan protocol. External lacrimal punctal diameter (ELP), internal lacrimal punctal diameter (ILP), vertical canalicular length (VCL) and tear meniscus height (TMH) were calculated. Statistical analysis was performed using Pearson’s correlation test. Our study had 100 normal subjects with mean age 24.14 years, range 15–38 years. ASOCT (Visante) with advantage of deeper penetration was able to image canaliculus in all cases. The mean TMH, ELP, ILP and VCL was 252.7 ± 67.98 μm, 382.2 ± 103.14μm, 140.7 ± 67.29 μm and 811.8 ± 253.7 μm, respectively, in 100 normal subjects. No correlation was found between ELP, ILP and TMH (Pearson’s Correlation; r = -0.355, p > 0.05 for all). ASOCT (VisanteTM) is a non-invasive and easy to use objective tool for imaging proximal lacrimal system. Further studies are required to validate normative data and correlate with microscopic findings of lacrimal system.

Evaluation of early anatomical changes following canaloplasty with anterior segment spectral-domain optical coherence tomography and ultrasound biomicroscopy.

To analyse structural changes in conjunctiva, sclera and Schlemm's canal (SC) following canaloplasty with optical coherence tomography (AS-OCT) and ultrasound biomicroscopy (UBM). Fifteen patients undergoing canaloplasty were included in this prospective study. AS-OCT images were acquired pre- and 1, 7, 30 and 90days postoperatively. UBM was performed 3months postoperatively. The surgical site was evaluated for the presence of SC, transscleral filtration, a scleral lake and the visibility of intra-Schlemm-sutures. The height and width of SC were measured at the 3 and 9 o'clock limbus position. After canaloplasty, SC was detectable with AS-OCT in 93% of the patients on day 1. The increase in height was higher than that in width (height: +369%, p=0.0004, width: +152%, p=0.002). IOP was negatively correlated to SC's width 1week postoperatively (r=-0.63, p=0.04) and to SC's height until 3 months (r=-0.66, p=0.02) postoperatively. Using UBM, a reflection of the traction sutures indicated SC's position in all patients. Transscleral filtration was found in all patients using AS-OCT, demonstrating a peak 1week postoperatively. At 3months, a scleral lake could be visualized in 50% and 83% of patients using AS-OCT and UBM, respectively. AS-OCT offers a high resolution for imaging superficial conjunctival areas and SC after canaloplasty, whereas UBM is capable of detecting deeper structures such as scleral lakes or intra-canal-sutures. The results imply a correlation of the dilation of SC with the IOP-lowering effect and an early pronounced transscleral filtration following canaloplasty.