The role of anterior segment optical coherence tomography in the evaluation of the pterygium.

BACKGROUND: To evaluate the ability of anterior segment optical coherence tomography (AS-OCT) to visualize the anatomic features of the pterygium and its invasion of the corneal layers. METHODS: Seventy-five eyes of 54 patients diagnosed with pterygium were included. All subjects underwent complete ophthalmologic examinations, including AS-OCT. The limbus-apex distance, vertical height at the limbus, invasion of the Bowman's and stromal layers, and other morphologic structures of the pterygium tissue were analyzed. RESULTS: The mean age of the patients was 49.67 ± 16.49 (20-85) years. The mean apex-limbus distance was 2548.37 ± 1026.32 (933-4597) µm, and the mean vertical height at the limbus was 4843.89 ± 1374.10 (1740-7784) µm. A space was observed beneath the pterygium tissue in 44 (58.67%) eyes. The mean width and height of this space were 1756.33 ± 560.22 (1009-3095) µm and 231.70 ± 85.88 (109-465) µm, respectively. Invasion of the Bowman's layer was apparent in 74 (98.67%) eyes, and invasion of the stromal layer was detected in 33 (44%) eyes. A hyperreflective layer was observed beneath the epithelial layer at the edge of the pterygium apex in 31 (41.33%) eyes. In 24 (92.31%) of the 26 advanced pterygium cases and 20 (40.82%) of the 49 early pterygium cases, a subpterygium space was found beneath the lesion (p = 0.0001). CONCLUSION: AS-OCT enables measurement of the actual size and thickness of pterygia, assessment of invasion of the Bowman's and stromal layers of the cornea, and evaluation of the pterygium structure. Over half of the eyes exhibited space beneath the pterygium.

Pterygium Screening and Lesion Area Segmentation Based on Deep Learning.

A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models. A total of 150 normal and 150 pterygium anterior segment images were used to test the models, and the results were compared. The main evaluation indicators, including sensitivity, specificity, area under the curve, kappa value, and receiver operator characteristic curves of the four models, were compared. Simultaneously, 367 pterygium anterior segment images were used to train two improved pterygium segmentation models based on PSPNet. A total of 150 pterygium images were used to test the models, and the results were compared with those of the other four segmentation models. The main evaluation indicators included mean intersection over union (MIOU), IOU, mean average precision (MPA), and PA. Among the two-category models of pterygium, the best diagnostic result was obtained using the VGG16 model. The diagnostic accuracy, kappa value, diagnostic sensitivity of pterygium, diagnostic specificity of pterygium, and F1-score were 99%, 98%, 98.67%, 99.33%, and 99%, respectively. Among the pterygium segmentation models, the double phase-fusion PSPNet model had the best results, with MIOU, IOU, MPA, and PA of 86.57%, 78.1%, 92.3%, and 86.96%, respectively. This study designed a pterygium two-category model and a pterygium segmentation model for the images of the normal anterior and pterygium anterior segments, which could help patients self-screen easily and assist ophthalmologists in establishing the diagnosis of ophthalmic diseases and marking the actual scope of surgery.

Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images.

AIMS: The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study. METHODS: Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker. The model diagnosed pterygium based on biomarkers of pterygium. First, a total of 436 anterior segment images were collected; then, two intelligent-assisted lightweight pterygium diagnosis models (MobileNet 1 and MobileNet 2) based on raw data and augmented data were trained via transfer learning. The results of the lightweight models were compared with the clinical results. The classic models (AlexNet, VGG16 and ResNet18) were also used for training and testing, and their results were compared with the lightweight models. A total of 188 anterior segment images were used for testing. Sensitivity, specificity, F1-score, accuracy, kappa, area under the concentration-time curve (AUC), 95% CI, size, and parameters are the evaluation indicators in this study. RESULTS: There are 188 anterior segment images that were used for testing the five intelligent-assisted pterygium diagnosis models. The overall evaluation index for the MobileNet2 model was the best. The sensitivity, specificity, F1-score, and AUC of the MobileNet2 model for the normal anterior segment image diagnosis were 96.72%, 98.43%, 96.72%, and 0976, respectively; for the pterygium observation period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 83.7%, 90.48%, 82.54%, and 0.872, respectively; for the surgery period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 84.62%, 93.50%, 85.94%, and 0.891, respectively. The kappa value of the MobileNet2 model was 77.64%, the accuracy was 85.11%, the model size was 13.5 M, and the parameter size was 4.2 M. CONCLUSION: This study used deep learning methods to propose a three-category intelligent lightweight-assisted pterygium diagnosis model. The developed model can be used to screen patients for pterygium problems initially, provide reasonable suggestions, and provide timely referrals. It can help primary doctors improve pterygium diagnoses, confer social benefits, and lay the foundation for future models to be embedded in mobile devices.

Evaluation of pterygium severity with en face anterior segment optical coherence tomography and correlations with in vivo confocal microscopy.

PURPOSE: To describe en face anterior segment optical coherence tomography (EF-OCT) characteristics of pterygia and their correlation with in vivo confocal microscopy (IVCM). PATIENTS AND METHODS: In this observational case series, we prospectively included 21 eyes of 17 subjects with pterygium. All subjects underwent detailed ophthalmic examination, anterior segment photography, an ocular surface disease index (OSDI) questionnaire, IVCM, and EF-OCT. Eyes were divided into two groups according to pterygium severity (Modified Pterygium Classification System) and OSDI score. EF-OCT images for both groups were analyzed for surface area of Fuchs Patches (FP). The IVCM activity score was based on the number of inflammatory cells, blood vessels, activated keratocytes and the appearance of the cornea/pterygium at the head of the pterygium. The correlations between EF-OCT and IVCM images were then analyzed and compared in both groups. RESULTS: EF-OCT permits clear visualization and evaluation of FPs and the border between the pterygium and the adjacent cornea. The severe pterygium group was characterized by irregular borders and larger FPs (0.13±0.06 mm2 versus 0.06±0.02 mm2 respectively) (P=0.003). The mean IVCM activity score was 2.36±0.81 in the severe pterygium group and 1.2±0.42 in the mild pterygium group (P=0.0013). There was a positive correlation between FP surface area and IVCM activity score. A larger FP surface area was associated with a higher activity score on IVCM. CONCLUSION: EF-OCT allows good evaluation of pterygium extension, borders and FP surface area. EF-OCT analysis of pterygium could represent a simple, non-invasive and reproducible method to evaluate pterygium severity and activity.

A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.

PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images. METHODS: A total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean. RESULTS: The best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545). CONCLUSIONS: The deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.

Temperature-dependent autoactivation associated with clinical variability of PDGFRB Asn666 substitutions.

Ocular pterygium-digital keloid dysplasia (OPDKD) presents in childhood with ingrowth of vascularized connective tissue on the cornea leading to severely reduced vision. Later the patients develop keloids on digits but are otherwise healthy. The overgrowth in OPDKD affects body parts that typically have lower temperature than 37°C. We present evidence that OPDKD is associated with a temperature sensitive, activating substitution, p.(Asn666Tyr), in PDGFRB. Phosphorylation levels of PDGFRB and downstream targets were higher in OPDKD fibroblasts at 37°C but were further greatly increased at the average corneal temperature of 32°C. This suggests that the substitution cause significant constitutive autoactivation mainly at lower temperature. In contrast, a different substitution in the same codon, p.(Asn666Ser), is associated with Penttinen type of premature aging syndrome. This devastating condition is characterized by widespread tissue degeneration, including pronounced chronic ulcers and osteolytic resorption in distal limbs. In Penttinen syndrome fibroblasts, equal and high levels of phosphorylated PDGFRB was present at both 32°C and 37°C. This indicates that this substitution causes severe constitutive autoactivation of PDGFRB regardless of temperature. In line with this, most downstream targets were not affected by lower temperature. However, STAT1, important for tissue wasting, did show further increased phosphorylation at 32°C. Temperature-dependent autoactivation offers an explanation to the strikingly different clinical outcomes of substitutions in the Asn666 codon of PDGFRB.

Influences of the three-dimensional parameters of pterygium on corneal astigmatism and the intraocular lens power calculation.

Pterygium morphology had great effect on corneal astigmatism and intraocular lens (IOL) power calculation in cataract patients. However, previous studies all focused on the pterygium surface parameters, the invasion degree or cross-sectional area of the pterygia into the corneal stroma were neglected. We studied the effect of three-dimensional parameters of pterygium on corneal astigmatism and IOL power prediction. We enrolled 81 eyes of 81 patients with primary nasal pterygium, measured the corneal astigmatism (Pentacam HR) and predicted IOL power change (IOLmaster500) before and after pterygium surgery. The three-dimensional parameters of pterygium (length, width, area, height and invasion cross-sectional area) were measured by slit lamp photography and Scheimpflug images. After pterygium surgery, corneal astigmatism decreased from 4.35 ± 4.24 to 1.07 ± 0.95 D and total corneal refractive power increased from 43.02 ± 1.96 to 43.95 ± 0.95 D (both P < 0.001). The predicted IOL power decreased from 22.87 ± 2.82 to 21.71 ± 2.85 D (P < 0.001) after surgery. Notably, 34 eyes (41.98%) had ≥3.0 D of pterygium induced astigmatism (PIA), and 33 eyes (40.74%) had ≥1.0 D of predicted IOL power change. PIA was independently influenced by the pterygium surface area (r = 0.43, P < 0.001) and cross-sectional area (r = 1.25, P = 0.018), while the predicted IOL power change was independently affected by the pterygium width (r = 0.70, P < 0.001). Cataract surgeons could evaluate the effects of a pterygium according to its three-dimensional parameters and prepare an optimal surgical strategy for cataract combined pterygium patients.

Tomografía de coherencia óptica de segmento anterior de alta resolución como método de diagnóstico diferencial entre neoplasia intraepitelial córneo-conjuntival y pterygium / High resolution anterior segment optical coherence tomography for differential diagnosis between corneo-conjunctival intraepithelial neoplasia and pterygium

OBJETIVO: Valorar si la tomografía de coherencia óptica de segmento anterior (OCT-SA) es un método de diagnóstico no invasivo adecuado para diferenciar lesiones córneo-conjuntivales benignas (pterygium) de las premalignas (neoplasia intraepitelial córneo-conjuntival [CIN]). MATERIAL Y MÉTODOS: Estudio observacional, analítico y transversal de 22 ojos con diagnóstico de sospecha de pterygium y CIN durante 2 años. Con la OCT-SA se estudiaron las características morfológicas y se compararon espesores epiteliales (EE) y grado de extensión sobre la superficie corneal (GIC). Posteriormente se confirmó el diagnóstico con el estudio histopatológico tras exéresis quirúrgica. RESULTADOS: La edad media de los pacientes con pterygium (n = 18) fue de 52,67 ± 15 años y 74 ± 12 años en los sujetos con CIN (n = 4) (p < 0,021). En los pterygium, la OCT-SA mostró EE normal, adelgazado o levemente aumentado (77,4 ± 26 μm), además de aumento del tejido subepitelial en forma de cuña. Los CIN presentaron un aumento del EE (262,5 ± 124 μm), que era fuertemente hiperreflectivo, con transición abrupta entre epitelio sano y patológico. El análisis de los EE en OCT-SA entre pterygium y CIN reveló diferencias estadísticamente significativas (p < 0,002). La curva ROC reveló una sensibilidad y especificidad del 100% de la OCT-SA en la diferenciación entre CIN y pterygium, utilizando EE de 141μm como punto de corte. CONCLUSIÓN: La OCT-SA es una herramienta útil para la diferenciación entre pterygium y CIN, ya que proporciona características morfológicas típicas. Un aumento del espesor del EE córneo-conjuntival mayor de 141 μm en OCT-SA sugiere una sensibilidad y especificidad del 100% para diagnosticar CIN

OBJECTIVE: To assess if anterior segment optical coherence tomography (AS-OCT) is a noninvasive diagnostic method suitable to differentiate benign corneo-conjunctival lesions (pterygium) from premalignant lesions (corneo-conjunctival intraepithelial neoplasia, CIN). MATERIAL AND METHODS: An observational, analytical and cross-sectional study was conducted in 22 eyes with conjunctival lesions clinically suspicious for pterygium and CIN during two years. Morphological differences between both lesions were studied with AS-OCT; epithelial thicknesses (EE) and extension length on corneal surface (GIC) were compared between both groups. A surgical excision of the lesion was performed for histopathological diagnosis. RESULTS: Mean age of patients with pterygium (n = 18) was 52.67 ± 15 y.o and 74 ± 12 y.o in subjects with CIN (n = 4) (p < 0.021). In pterygia, AS-OCT showed typical features (normal, thinning or slightly thickened EE; 77.4 ± 26 μm), in addition to an increase in wedge-shaped subepithelial tissue. Patients with CIN had a mean thickened EE (262.5 ± 124μm) and strongly hyperreflective, with abrupt transition between normal and pathological epithelium. Analysis of EE between subjects with pterygium and CIN revealed statistically significant differences (p < 0.002). ROC curve revealed a 100% sensitivity and specificity of OCT-SA in differentiation between CIN and pterygium, using 141μm as cutoff point of EE. CONCLUSION: AS-OCT is a useful tool for the differentiation between pterygium and CIN able to provide typical morphological characteristics. An EE greater than 141 μm in AS-OCT suggests a sensitivity and specificity of 100% for the diagnosis of CIN

High resolution anterior segment optical coherence tomography for differential diagnosis between corneo-conjunctival intraepithelial neoplasia and pterygium. / Tomografía de coherencia óptica de segmento anterior de alta resolución como método de diagnóstico diferencial entre neoplasia intraepitelial córneo-conjuntival y pterygium.

OBJECTIVE: To assess if anterior segment optical coherence tomography (AS-OCT) is a noninvasive diagnostic method suitable to differentiate benign corneo-conjunctival lesions (pterygium) from premalignant lesions (corneo-conjunctival intraepithelial neoplasia, CIN). MATERIAL AND METHODS: An observational, analytical and cross-sectional study was conducted in 22 eyes with conjunctival lesions clinically suspicious for pterygium and CIN during two years. Morphological differences between both lesions were studied with AS-OCT; epithelial thicknesses (EE) and extension length on corneal surface (GIC) were compared between both groups. A surgical excision of the lesion was performed for histopathological diagnosis. RESULTS: Mean age of patients with pterygium (n=18) was 52.67±15 y.o and 74±12 y.o in subjects with CIN (n=4) (p<0.021). In pterygia, AS-OCT showed typical features (normal, thinning or slightly thickened EE; 77.4±26µm), in addition to an increase in wedge-shaped subepithelial tissue. Patients with CIN had a mean thickened EE (262.5±124µm) and strongly hyperreflective, with abrupt transition between normal and pathological epithelium. Analysis of EE between subjects with pterygium and CIN revealed statistically significant differences (p<0.002). ROC curve revealed a 100% sensitivity and specificity of OCT-SA in differentiation between CIN and pterygium, using 141µm as cutoff point of EE. CONCLUSION: AS-OCT is a useful tool for the differentiation between pterygium and CIN able to provide typical morphological characteristics. An EE greater than 141µm in AS-OCT suggests a sensitivity and specificity of 100% for the diagnosis of CIN.

Optical coherence tomography angiography for marginal corneal vascular remodelling after pterygium surgery with limbal-conjunctival autograft.

PURPOSE: To demonstrate the marginal corneal vascular remodelling using optical coherence tomography angiography (OCTA) after pterygium surgery. METHODS: Twenty-two eyes of 19 patients (8 males, 11 females; age, 58.68 ± 0.34 years) with primary grade-T3 nasal pterygium were enroled in this study. The eyes underwent excision of the pterygium followed by a free limbal-conjunctival autograft. OCTA was performed in the nasal limbal area before surgery and at 10 days, 1 month, and 3 months after surgery. The scans were analyzed in terms of postoperative vascular remodelling of the autograft and marginal corneal vascular arcades (MCAs). RESULTS: Preoperatively, the pterygium presented as abnormal centripetal vascular growth in OCTA scans. The conjunctival vessel density in the nasal quadrant was 29.26% ± 1.00%, 15.80% ± 0.83%, 19.80% ± 0.88%, and 20.26% ± 0.89% before and 10 days, 1 month, and 3 months, respectively, after surgery (F = 1.55, P < 0.01). The vessel density of MCAs was 28.33% ± 0.88%, 42.09% ± 0.41%, and 42.46% ± 0.31% 10 days, 1 month, and 3 months, respectively, after surgery (F = 188.2, P < 0.01). CONCLUSIONS: We describe a new application of OCTA for MCA vasculature imaging. Vascular remodelling of the graft and MCAs appeared at 1 month and continued for 3 months after surgery.

Corneo-pterygium total area measurements utilising image analysis method / Mediciones del área total de pterigium corneal utilizando un método de análisis de imagen

PURPOSE: To describe an objective method to accurately quantify corneo-pterygium total area (CPTA) by utilising image analysis method and to evaluate its association with corneal astigmatism (CA). METHODS: 120 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We adopted image analysis software in calculating the size of invading pterygium to the cornea. The marking of the calculated area was done manually, and the total area size was measured in pixel. The computed area is defined as the area from the apex of pterygium to the limbal-corneal border. Then, from the pixel, it was transformed into a percentage (%), which represents the CPTA relative to the entire corneal surface area. Intra- and inter-observer reliability testing were performed by repeating the tracing process twice with a different sequence of images at least one (1) month apart. Intraclass correlation (ICC) and scatter plot were used to describe the reliability of measurement. RESULTS: The overall mean (N = 120) of CPTA was 45.26 ± 13.51% (CI: 42.38-48.36). Reliability for region of interest (ROI) demarcation of CPTA were excellent with intra and inter-agreement of 0.995 (95% CI, 0.994-0.998; P < 0.001) and 0.994 (95% CI, 0.992-0.997; P < 0.001) respectively. The new method was positively associated with corneal astigmatism (P < 0.01). This method was able to predict 37% of the variance in CA compared to 21% using standard method. CONCLUSIONS: Image analysis method is useful, reliable and practical in the clinical setting to objectively quantify actual pterygium size, shapes and its effects on the anterior corneal curvature

OBJETIVO: Describir un método objetivo para cuantificar con precisión el área total corneal invadida por pterigium (CPTA) utilizando un método de análisis de imagen evaluando su asociación con el astigmatismo de la córnea (AC). MÉTODOS: Se seleccionaron 120 participantes con pterigium primario de entre los pacientes que acudieron a la clínica oftalmológica. Utilizamos un software de análisis de imagen para calcular el tamaño del pterigión invasivo hacia la córnea. La marcación del área calculada se realizó manualmente, midiéndose en píxeles el tamaño del área total. El área computada se define como el área desde el ápex del pterigium al borde del limbo corneal. A continuación, a partir del análisis de pixels, se transformó en un porcentaje (%), que representa el CPTA relativo al área total de la superficie de la córnea. Se realizaron pruebas de fiabilidad Intra- e inter-observador mediante un proceso, de doble repetición, con una secuencia de imágenes diferente, con separación de un (1) mes como mínimo. Se utilizaron la correlación intra-clase (ICC) y el gráfico de dispersión para describir la fiabilidad de las mediciones. RESULTADOS: La media global (N = 120) de CPTA fue 45,26 ± 13,51% (IC: 42,38-48,36). La fiabilidad para la demarcación de la región de interés (ROI) de CPTA fue excelente con intra e inter-acuerdo de 0,995 (95% IC, 0,994-0,998; P < 0,001) y 0,994 (95% IC, 0,992-0,997; P < 0,001) respectivamente. El nuevo método se asoció positivamente al astigmatismo de la córnea (p < 0,01). Este método fue capaz de predecir el 37% de la varianza de AC, en comparación con el 21% utilizando el método estándar. CONCLUSIONES: El método de análisis de imagen descrito es útil, fiable y práctico en el entorno clínico, para cuantificar objetivamente el tamaño real del pterigium, así como sus formas y efectos sobre la curvatura anterior de la córnea

Corneo-pterygium total area measurements utilising image analysis method.

PURPOSE: To describe an objective method to accurately quantify corneo-pterygium total area (CPTA) by utilising image analysis method and to evaluate its association with corneal astigmatism (CA). METHODS: 120 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We adopted image analysis software in calculating the size of invading pterygium to the cornea. The marking of the calculated area was done manually, and the total area size was measured in pixel. The computed area is defined as the area from the apex of pterygium to the limbal-corneal border. Then, from the pixel, it was transformed into a percentage (%), which represents the CPTA relative to the entire corneal surface area. Intra- and inter-observer reliability testing were performed by repeating the tracing process twice with a different sequence of images at least one (1) month apart. Intraclass correlation (ICC) and scatter plot were used to describe the reliability of measurement. RESULTS: The overall mean (N=120) of CPTA was 45.26±13.51% (CI: 42.38-48.36). Reliability for region of interest (ROI) demarcation of CPTA were excellent with intra and inter-agreement of 0.995 (95% CI, 0.994-0.998; P<0.001) and 0.994 (95% CI, 0.992-0.997; P<0.001) respectively. The new method was positively associated with corneal astigmatism (P<0.01). This method was able to predict 37% of the variance in CA compared to 21% using standard method. CONCLUSIONS: Image analysis method is useful, reliable and practical in the clinical setting to objectively quantify actual pterygium size, shapes and its effects on the anterior corneal curvature.

Super-Thick Amniotic Membrane Graft for Ocular Surface Reconstruction.

PURPOSE: The purpose of this study was to evaluate super-thick amniotic membrane grafts (ST-AMGs) for ocular surface reconstruction. DESIGN: Retrospective, interventional case series. METHODS: This was a single-center study of clinical practice that included select patients with typically large ocular surface abnormalities that required reconstruction. The intervention studied was surgical insertion of a ST-AMG for reconstruction or repair of the ocular surface. Main outcome measures included intraoperative handling, graft position at 1 week post implantation, graft dissolution at 3 weeks, epithelialization of the ocular surface and symblepharon. RESULTS: Eleven ST-AMGs were implanted after resection with cryotherapy: 5 conjunctival melanoma, 4 squamous cell carcinoma, 1 sebaceous carcinoma, and 1 atypical pterygium. In addition, 1 was implanted for scleral necrosis. ST-AMGs were up to nine times thicker than standard amniotic grafts and were therefore amenable to both running and interrupted 7-0 Vicryl sutures without cheese-wiring. All cases had a well-positioned ST-AMG at 1 week and 75% (n = 9) had partial graft dissolution at 3 weeks. Complete epithelialization without wound dehiscence was noted in all cases. However, secondary (after additional tumor treatment) symblepharon formed in 16.7% (n = 2). In all cases, the mean visual acuity and intraocular pressures remained unchanged during conjunctival reconstruction and subsequent secondary treatments. Post epithelialization adjuvant topical chemotherapy was given to extend treatment margins and treat presumed occult disease in 50% (n = 6). At mean follow-up of 25.5 months (median 10, range 3-90), 10 cases (83.3%) showed complete local tumor control, 1 showed revascularization of the scleral melt, and 1 required orbital exenteration. CONCLUSION: ST-AMGs were easy to suture and relatively persistent. Epithelialization of the ocular surface without primary symblepharon formation was noted. ST-AMGs should be considered an alternative for ocular surface reconstruction.

Dermoscopy for the Diagnosis of Conjunctival Lesions.

This article describes the present literature on dermoscopy of conjunctiva and shows the results of a dermoscopy study of 147 conjunctival tumors. Melanomas were characterized by a heavy pigmentation, irregular dots, and a higher prevalence of gray color compared with nevi. Squamous cell carcinomas had peculiar hairpin and glomerular vessels. Primary acquired melanoses were characterized by regularly distributed light brown dots. A large part of nevi had small cysts.

Automated pterygium detection method of anterior segment photographed images.

BACKGROUND AND BJECTIVE: Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images. METHODS: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network. RESULTS: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively. CONCLUSION: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.

Morphometric characterisation of pterygium associated with corneal stromal scarring using high-resolution anterior segment optical coherence tomography.

AIMS: To investigate the role of high-resolution anterior segment optical coherence tomography (HR-ASOCT) in the assessment of pterygia. METHODS: Single centre cross-sectional study. Patients with primary pterygium and/or pingueculae were included. Clinical assessment included HR-ASOCT, colour photography, keratometry followed by histology. Associations were tested between HR-ASOCT features of the pterygium and the degree of corneal scarring and elastotic degeneration, astigmatism and best-corrected visual acuity. RESULTS: 29 eyes of 26 patients with pterygium and 6 patients with pinguecula were included. Apical anterior stromal scarring was found in 23 cases (79.3%) reaching a mean depth of 68.8±21.7 µm (minimum: 33 µm, maximum: 126 µm). Increased stromal scarring and subepithelial elastotic degenerative tissue was significantly associated with HR-ASOCT features of flat bridging of the corneoscleral transition zone (p<0.01) reduced thickness of the pterygium head (p=0.01), and a greater degree of corneal astigmatism (p=0.04). CONCLUSIONS: HR-ASOCT is a useful tool for the assessment and monitoring of pterygia in clinical practice. Features associated with increased stromal scarring and astigmatism are reduced thickness of the head of the pterygium and flat bridging of the corneoscleral transition zone.

[Study on the evaluation of pterygium activity with in vivo confocal microscopy].

Objective: To observe the characteristics of pterygium with in vivo confocal microscopy (IVCM) and understand pterygium activity with the density of inflammatory cells, formation of new blood vessels, and the number of activated keratocytes within the stroma. Methods: A prospective case-controlled study. Thirty-six pterygia from 28 patients were analyzed in this study. A pterygium activity score was obtained by summing four scores of ocular discomfort, pterygium hyperemia, keratitis, and the presence of Fuchs patches. Among them, the low activity of pterygium (PAS score less than 3 points) appeared in 12 eyes and high activity of pterygium (PAS score ≥ 4 points) in 24 eyes. All Patients underwent pterygium IVCM quantitative analysis by observing the boundaries between the pterygium and the adjacent cornea, the density of goblet cells and dendritiform inflammatory cells and Fuchs patches. The correlation of pterygium activity between IVCM and PAS with slit lamp was analyzed with Spearman correlation analysis. Results: The presence of inflammatory cells, numerous blood vessels, and irregular boundary between the cornea and the pterygium with infiltration of hyper-reflective cells in the adjacent corneal epithelium were signs observed with IVCM associated with pterygium activity. With IVCM technique, epithelial cells, goblet cells, and dendritiform inflammatory cells of various sizes were observed within the pterygium epithelium.The active (PAS ≥4) pterygium showed irregular boundaries between the pterygium and the adjacent cornea (score, 0.84±0.51) comparing with inactive subjects (score 0.23±0.12, t=2.68, P=0.009). Fuchs patches were observed as islets of hyper-reflective polygonal cells in front of the pterygium head with blurred boundary. The score (0.75±0.25) in active group showed significant changes as compared with normal subjects (0.23±0.12, t=3.79, P=0.001). The score of dendritiform inflammatory cells, activated keratocytes, and goblet cells in active group were 2.75±0.76, 1.92±0.68, and 2.08±0.42, which were significantly higher than those in inactive group (1.25±0.55, 0.50±0.25, 1.15±0.32, P=0.035, 0.030, <0.01). There was significant positive correlation between IVCM activity and traditional slit lamp PAS. Conclusion: Quantitative analysis of dendritiform inflammatory cells, vascular proliferation and activated keratocytes of pterygium by IVCM may be a reliable evaluation method to evaluate the activity of pterygium.(Chin J Ophthalmol, 2016, 52: 755-763).

Risk Factors for Recurrence After Pterygium Surgery: An Image Analysis Study.

PURPOSE: To determine the risk factors related to recurrence of pterygium using automated image analysis. METHODS: This study included 149 eyes of 149 patients who underwent pterygium excision and limbal-conjunctival autograft. Demographic variables including age and sex were collected. Image analysis was performed using anterior segment photographs taken preoperatively to measure relative length (horizontal length of pterygium invading cornea/horizontal corneal diameter), relative width (width of pterygium invading cornea/vertical corneal diameter), relative area (RA; area of pterygium invading cornea/total corneal area), and vascularity index (VI; degree of vascularity). In all patients, recurrence of pterygium was determined at 1 year after surgery. Association between these factors and recurrence rate was evaluated with univariate and multivariate analyses. RESULTS: Recurrence at 1 year was reported in 18.8% (28/149) of the patients. Univariate analysis showed that relative length (P = 0.001), relative width (P = 0.031), relative area (P = 0.009), and VI (P < 0.001) were significantly associated with increased risk of recurrence, whereas age and sex had no significant association with recurrence. In multivariate analysis, only VI (P < 0.001) had significant correlation with recurrence. Patients with VI ≥30 had significantly higher recurrence rate than those with VI <30 (33.3% vs. 8.1%, P < 0.001). CONCLUSIONS: Increased vascularity was associated with a higher risk of recurrence. Quantification of vascularity using automated image analysis might be useful in predicting the risk of recurrence.

Prenatal diagnosis and genetic analysis of fetal akinesia deformation sequence and multiple pterygium syndrome associated with neuromuscular junction disorders: a review.

Fetal akinesia deformation sequence is a clinically and genetically heterogeneous disorder characterized by a variable combination of arthrogryposis, fetal akinesia, intrauterine growth restriction, developmental abnormalities such as cystic hygroma, pulmonary hypoplasia, cleft palate, cryptorchidism, cardiac defects and intestinal malrotation, and occasional pterygia of the limbs. Multiple pterygium syndrome is a clinically and genetically heterogeneous disorder characterized by pterygia of the neck, elbows and/or knees, arthrogryposis, and other phenotypic features such as short stature, genital abnormalities, craniofacial abnormalities, clubfoot, kyphoscoliosis, and cardiac abnormalities. Fetal akinesia deformation sequence may phenotypically overlap with the lethal type of multiple pterygium syndrome. This article provides a comprehensive review of prenatal diagnosis and genetic analysis of fetal akinesia deformation sequence and multiple pterygium syndrome associated with neuromuscular junction disorders. Prenatal diagnosis of fetal akinesia along with cystic hygroma, increased nuchal translucency, nuchal edema, hydrops fetalis, arthrogryposis, pterygia, and other structural abnormalities should include a differential diagnosis of neuromuscular junction disorders. Genetic analysis of mutations in the neuromuscular junction genes such as CHRNA1, CHRND, CHRNG, CNTN1, DOK7, RAPSN, and SYNE1 may unveil the pathogenetic cause of fetal akinesia deformation sequence and multiple pterygium syndrome, and the information acquired is helpful for genetic counseling and clinical management.

CHRNG genotype-phenotype correlations in the multiple pterygium syndromes.

BACKGROUND: Germline mutations in the CHRNG gene that encodes the γ subunit of the embryonal acetylcholine receptor may cause the non-lethal Escobar variant (EVMPS) or the lethal form (LMPS) of multiple pterygium syndrome (MPS). In addition CHRNG mutations and mutations in other components of the embryonal acetylcholine receptor may present with fetal akinesia deformation sequence (FADS) without pterygia. METHODS: In order to elucidate further the role of CHRNG mutations in MPS/FADS, this study evaluated the results of CHRNG mutation analysis in 100 families with a clinical diagnosis of MPS/FADS. RESULTS: CHRNG mutations were identified in 11/41 (27%) of families with EVMPS and 5/59 (8%) with LMPS/FADS. Most patients with a detectable CHRNG mutation (21 of 24 (87.5%)) had pterygia but no CHRNG mutations were detected in the presence of central nervous system anomalies. DISCUSSION: The mutation spectrum was similar in EVMPS and LMPS/FADS kindreds and EVMPS and LMPS phenotypes were observed in different families with the same CHRNG mutation. Despite this intrafamilial variability, it is estimated that there is a 95% chance that a subsequent sibling will have the same MPS phenotype (EVMPS or LMPS) as the proband (though concordance is less for more distant relatives). Based on these findings, a molecular genetic diagnostic pathway for the investigation of MPS/FADS is proposed.