Evaluation of CRISPR/Cas9 exon-skipping vector for choroideremia using human induced pluripotent stem cell-derived RPE.

BACKGROUND: Exon-skipping is a powerful genetic tool, especially when delivering genes using an AAV-mediated full-length gene supplementation strategy is difficult owing to large length of genes. Here, we used engineered human induced pluripotent stem cells and artificial intelligence to evaluate clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9-based exon-skipping vectors targeting genes of the retinal pigment epithelium (RPE). The model system was choroideremia; this is an X-linked inherited retinal disease caused by mutation of the CHM gene. METHODS: We explored whether artificial intelligence detected differentiation of human OTX2, PAX6 and MITF (hOPM) cells, in which OTX2, PAX6 and MITF expression was induced by doxycycline treatment, into RPE. Plasmid encoding CHM exon-skipping modules targeting the splice donor sites of exons 6 were constructed. A clonal hOPM cell line with a frameshift mutation in exon 6 was generated and differentiated into RPE. CHM exon 6-skipping was induced, and the effects of skipping on phagocytic activity, cell death and prenylation of Rab small GTPase (RAB) were evaluated using flow cytometry, an in vitro prenylation assay and western blotting. RESULTS: Artificial intelligence-based evaluation of RPE differentiation was successful. Retinal pigment epithelium cells with a frameshift mutation in exon 6 showed increased cell death, reduced phagocytic activity and increased cytosolic unprenylated RABs only when oxidative stress was in play. The latter two phenotypes were partially rescued by exon 6-skipping of CHM. CONCLUSIONS: CHM exon 6-skipping contributed to RPE phagocytosis probably by increasing RAB38 prenylation under oxidative stress.

Binocular metamorphopsia in patients with branch retinal vein occlusion: a multi-center study.

PURPOSE: The pathology of branch retinal vein occlusion (BRVO), a retinal circulatory disease, is related to monocular metamorphopsia-related vision impairment of the affected eyes, but the association of binocular metamorphopsia in such patients is unclear. This study aimed to examine the frequency of binocular metamorphopsia and its association with the clinical characteristics of patients with BRVO. METHODS: A total of 87 patients who were treated for BRVO-associated macular edema (ME) were included in this study. At baseline and 1 and 3 months after the initiation of anti-vascular endothelial growth factor (VEGF) treatment, we quantified metamorphopsia in the affected eyes and binocular metamorphopsia using the M-CHARTS® diagnostic tool. RESULTS: At baseline, 53 and 7 patients had metamorphopsia in the affected eyes and binocular metamorphopsia, respectively. Although the visual acuity improved significantly after the initiation of anti-VEGF treatment, the mean M-CHARTS score in the affected eyes did not change from the baseline score. At 3 months, 9 patients showed binocular metamorphopsia; it was significantly associated with metamorphopsia in the affected eyes with a 95% confidence interval of 0.021-0.122 (ß = 0.306, p = 0.006). CONCLUSION: Metamorphopsia in the affected eyes can cause binocular metamorphopsia in patients with BRVO-ME.

Developing an iOS application that uses machine learning for the automated diagnosis of blepharoptosis.

PURPOSE: To assess the performance of artificial intelligence in the automated classification of images taken with a tablet device of patients with blepharoptosis and subjects with normal eyelid. METHODS: This is a prospective and observational study. A total of 1276 eyelid images (624 images from 347 blepharoptosis cases and 652 images from 367 normal controls) from 606 participants were analyzed. In order to obtain a sufficient number of images for analysis, 1 to 4 eyelid images were obtained from each participant. We developed a model by fully retraining the pre-trained MobileNetV2 convolutional neural network. Subsequently, we verified whether the automatic diagnosis of blepharoptosis was possible using the images. In addition, we visualized how the model captured the features of the test data with Score-CAM. k-fold cross-validation (k = 5) was adopted for splitting the training and validation. Sensitivity, specificity, and the area under the curve (AUC) of the receiver operating characteristic curve for detecting blepharoptosis were examined. RESULTS: We found the model had a sensitivity of 83.0% (95% confidence interval [CI], 79.8-85.9) and a specificity of 82.5% (95% CI, 79.4-85.4). The accuracy of the validation data was 82.8%, and the AUC was 0.900 (95% CI, 0.882-0.917). CONCLUSION: Artificial intelligence was able to classify with high accuracy images of blepharoptosis and normal eyelids taken using a tablet device. Thus, the diagnosis of blepharoptosis with a tablet device is possible at a high level of accuracy. TRIAL REGISTRATION: Date of registration: 2021-06-25. TRIAL REGISTRATION NUMBER: UMIN000044660. Registration site: https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051004.

Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage.

Background and Objectives: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. Materials and Methods: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests-the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR-were examined. Results: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680-0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.

Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning.

PURPOSE: We assessed the ability of deep learning (DL) models to distinguish between tear meniscus of lacrimal duct obstruction (LDO) patients and normal subjects using anterior segment optical coherence tomography (ASOCT) images. METHODS: The study included 117 ASOCT images (19 men and 98 women; mean age, 66.6 ± 13.6 years) from 101 LDO patients and 113 ASOCT images (29 men and 84 women; mean age, 38.3 ± 19.9 years) from 71 normal subjects. We trained to construct 9 single and 502 ensemble DL models with 9 different network structures, and calculated the area under the curve (AUC), sensitivity, and specificity to compare the distinguishing abilities of these single and ensemble DL models. RESULTS: For the highest single DL model (DenseNet169), the AUC, sensitivity, and specificity for distinguishing LDO were 0.778, 64.6%, and 72.1%, respectively. For the highest ensemble DL model (VGG16, ResNet50, DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, and Xception), the AUC, sensitivity, and specificity for distinguishing LDO were 0.824, 84.8%, and 58.8%, respectively. The heat maps indicated that these DL models placed their focus on the tear meniscus region of the ASOCT images. CONCLUSION: The combination of DL and ASOCT images could distinguish between tear meniscus of LDO patients and normal subjects with a high level of accuracy. These results suggest that DL might be useful for automatic screening of patients for LDO.

Treatment outcomes in the neovascular glaucoma tube versus trabeculectomy study.

PURPOSE: To compare surgical outcomes between patients who underwent Baerveldt glaucoma implantation versus trabeculectomy (TLE) for the treatment of neovascular glaucoma. METHODS: We performed a prospective, randomized, controlled clinical trial at Hiroshima University and Tsukazaki Hospital, Japan. Patients were randomized to Baerveldt glaucoma implantation (23 patients) or TLE (27 patients). The primary outcome measure was the rate of intraocular-pressure control. Secondary outcome measures included complications and interventions. We defined "failure" as intraocular pressure ≥ 22 mmHg or < 20% reduction from baseline pressure, on two consecutive follow-up visits; need for additional glaucoma surgery; vision-threatening complications; or loss of light perception. Kaplan-Meier analysis was used to determine surgical success rates. Postoperative complications and interventions were compared between the two groups with Pearson Chi-square tests. RESULTS: There were no significant differences in demographic or ocular characteristics between the treatment groups at baseline. The postoperative follow-up was 26.6 ± 19.4 months in the Baerveldt group and 27.3 ± 20.1 months in the TLE group. There were no statistical differences (Mann-Whitney nonparametric tests with Bonferroni correction) in postoperative intraocular pressure measured at 6-month intervals. Success rates were 59.1 and 61.6% at 1-year after Baerveldt glaucoma implantation and TLE, respectively. Kaplan-Meier analysis showed no significant difference in success between groups (P = 0.71, log-rank test). Medication use, visual acuity, and interventions were similar between groups. Thirteen late postoperative complications developed in the Baerveldt glaucoma implantation group and four complications developed in the TLE group. Tube exposure was the most common late complication. CONCLUSION: These two procedures produced similar surgical success, intraocular-pressure reductions, visual acuity, and number of medications at the last visit. The TLE group showed smaller numbers of late complications and patients who lost more than two lines of visual acuity. Therefore, TLE might be a safer and better way to treat patients with neovascular glaucoma.

Effect of Manual Upper Eyelid Elevation on Intraocular Pressure Measurement by Four Different Tonometers.

SIGNIFICANCE: This study is the first to show that the manual upper eyelid elevation (manual UEE) that is commonly used to prevent disruption of the IOP measurement due to blinking or upper eyelid contact with the tip of the tonometer does not affect the IOP values. PURPOSE: We investigated whether manual UEE affects the IOP readings using three rebound tonometers (Icare TA01i, Icare PRO, and Icare ic100) and Goldmann applanation tonometry (GAT). METHODS: One eye was measured for 101 patients (56 eyes of primary open-angle glaucoma patients and 45 healthy subjects). The IOPs were measured without and with manual UEE. Each IOP was measured twice; the measurement order using the tonometers was randomly selected. In addition, palpebral fissure height (distance between the upper and lower eyelids) was measured. RESULTS: The IOPs without manual UEE were 12.1 ± 2.9, 13.3 ± 2.7, 11.7 ± 2.9, and 16.0 ± 3.2 mmHg (Icare TA01i, Icare PRO, Icare ic100, and GAT), and those with manual UEE were 12.3 ± 3.0, 13.3 ± 2.8, 11.7 ± 2.9, and 16.0 ± 3.3, respectively. No significant difference was found between the IOP without and with manual UEE (IOP difference; all, P > .50; paired t test). Multiple linear regression analyses revealed that palpebral fissure height did not affect IOP difference for any of the tonometers. CONCLUSIONS: Simple manual UEE when measuring the IOP has little effect on the IOP obtained using all current rebound tonometers and GAT.

A Deep Learning Approach in Rebubbling After Descemet's Membrane Endothelial Keratoplasty.

PURPOSE: To evaluate the efficacy of deep learning in judging the need for rebubbling after Descemet's endothelial membrane keratoplasty (DMEK). METHODS: This retrospective study included eyes that underwent rebubbling after DMEK (rebubbling group: RB group) and the same number of eyes that did not require rebubbling (non-RB group), based on medical records. To classify the RB group, randomly selected images from anterior segment optical coherence tomography at postoperative day 5 were evaluated by corneal specialists. The criterion for rebubbling was the condition where graft detachment reached the central 4.0-mm pupil area. We trained nine types of deep neural network structures (VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, Xception, DenseNet121, DenseNet169, and DenseNet201) and built nine models. Using each model, we tested the validation data and evaluated the model. RESULTS: This study included 496 images (31 eyes from 24 patients) in the RB group and 496 images (31 eyes from 29 patients) in the non-RB group. Because 16 picture images were obtained from the same point of each eye, a total of 992 images were obtained. The VGG19 model was found to have the highest area under the receiver operating characteristic curve (AUC) of all models. The AUC, sensitivity, and specificity of the VGG19 model were 0.964, 0.967, and 0.915, respectively, whereas those of the best ensemble model were 0.956, 0.913, and 0.921, respectively. CONCLUSIONS: This automated system that enables the physician to be aware of the requirement of RB might be clinically useful.

Objective evaluation of allergic conjunctival disease (with a focus on the application of artificial intelligence technology).

We have summarized the past efforts and results of objective measurement methods for conjunctival hyperemia classification. Severity classification using conjunctival blood vessel occupancy rate, ocular surface temperature analysis, and artificial intelligence have been reported to be clinically useful, as they have been found to correlate with the severity of conjunctival hyperemia by doctors. The AI method using slit lamp microscope images, whose main purpose is to be widely used in daily clinical practice, can be spread all over the world. As a result, it may lay the foundation for clinical research using large amounts of clinical data collected on the same basis without human bias.

Changes of choroidal structure and circulation after water drinking test in normal eyes.

PURPOSE: To determine whether water drinking test (WDT) alters choroidal structure using binarization of enhanced depth imaging optical coherence tomographic (EDI-OCT) images. METHODS: We performed a prospective study of 57 eyes of 57 normal subjects. The intraocular pressure (IOP), EDI-OCT images, and laser speckle flowgraphic images were recorded at baseline, and at 15, 30, 45, and 120 min after the WDT. The EDI-OCT images were converted to binary images using ImageJ software, and we examined luminal area, interstitial area, whole choroidal area, the ratio of luminal area to whole choroidal area (L/W ratio), subfoveal choroidal thickness (SCT), and central retinal thickness (CRT). RESULTS: The luminal area, L/W ratio, whole choroidal area, and IOP were significantly increased 30 min after water drinking; levels returned to the baseline at 120 min. No significant changes were found in the CRT and interstitial area. The fluctuations in the SCT after water intake were significantly correlated with those in the L/W ratio and luminal area but not with those of the interstitial area. The choroidal blood flow velocity was significantly decreased at 30 min. Fluctuations in the luminal area, L/W ratio, and whole choroidal area were significantly correlated with IOP fluctuations. CONCLUSIONS: The changes in the SCT after water drinking were mainly due to the changes in the choroidal vascular space. Dilatations of the choroidal vessels after water drinking may lead to choroidal thickening and subsequent IOP elevation. These findings should be considered in the evaluation of choroidal structure in patients with retinal disease.

MORPHOLOGIC CHARACTERISTICS OF MACULAR HOLE AND MACULAR HOLE RETINAL DETACHMENT ASSOCIATED WITH EXTREME MYOPIA.

PURPOSE: To investigate the surgical results and morphologic characteristics of macular hole (MH) and macular hole retinal detachment (MHRD) associated with extreme myopia. METHODS: We retrospectively reviewed consecutive cases with axial length ≥28 mm who were treated with pars plana vitrectomy for MH or MHRD. The choroidal and scleral thickness at the fovea, presence of dome-shaped macula, and the height of posterior staphyloma 3 mm from the fovea were measured from postoperative optical coherence tomography images. RESULTS: Significant improvement in visual acuity was obtained postoperatively in both MH (16 eyes; 15 patients) and MHRD (19 eyes; 18 patients) groups (P < 0.05). Final MH closure rate was not significantly different between the groups (MH: 15/16, MHRD: 14/19, P = 0.19). Axial length was not significantly different between the groups (MH: 30.5 ± 1.5 mm, MHRD: 29.6 ± 1.3 mm, P = 0.098). Eyes with MH had significantly greater choroidal thickness (MH: 61.9 ± 66.0 µm, MHRD: 24.1 ± 19.8 µm, P = 0.045), greater scleral thickness (MH: 294 ± 77 µm, MHRD: 232 ± 89 µm, P = 0.008), higher frequency of dome-shaped macula (MH: 6/16, MHRD: 1/19, P = 0.032), and lower staphyloma height (MH: 190 ± 113 µm, MHRD: 401 ± 156 µm, P < 0.001). CONCLUSION: Surgical outcomes were generally favorable. The pathogenetic differences between the two conditions may be attributable to differences with respect to eye morphology.

Descemet's membrane endothelial keratoplasty for pseudoexfoliation syndrome: a case series.

BACKGROUND: To evaluate the clinical outcomes and features of Descemet's membrane endothelial keratoplasty (DMEK) for eyes with pseudoexfoliation syndrome (PEX). METHODS: In this retrospective study, 37 DMEK cases were reviewed from available medical records. Patients who exhibited endothelial dysfunction derived from PEX or Fuchs endothelial corneal dystrophy (FECD) and successfully underwent cataract surgery about four weeks before DMEK were enrolled. The best spectacle-corrected visual acuity (BSCVA), central corneal thickness (CCT), endothelial cell density (ECD), and incidence of intra-operative/post-operative complications of DMEK were analyzed. RESULTS: This study included 14 eyes of 14 patients (PEX: n = 6, FECD: n = 8). There was no primary graft failure. In the PEX group, BSCVA improved from 0.67 ± 0.28 at the preoperative point to 0.43 ± 0.14 at 1 month, 0.27 ± 0.10 at 3 months, and 0.19 ± 0.08 at 6 months after DMEK. The donor corneal ECD was 2704 ± 225 cells/mm2 at the preoperative point and decreased to 1691 ± 498 cells/mm2 at 1 month, 1425 ± 366 cells/mm2 at 3 months, and 1281 ± 340 cells/mm2 (52.7 ± 11.7% less than ECD of the donor graft) at 6 months after DMEK. None of the patients required rebubbling. When compared with the FECD group, no statistical difference was observed in CCT (p = 0.821); BSCVA (p = 0.001) and the reduction rate of ECD (p = 0.010) were relatively worse. CONCLUSIONS: DMEK is effective for the treatment of endothelial dysfunction due to PEX.

Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system. METHODS: First, to evaluate the diagnostic accuracy of DCNN, 364 photographic images (AMD: 137) were amplified and the area under the curve (AUC), sensitivity and specificity were examined. Furthermore, in order to compare the diagnostic abilities between DCNN and six ophthalmologists, we prepared yield 84 sheets comprising 50% of normal and wet-AMD data each, and calculated the correct answer rate, specificity, sensitivity, and response times. RESULTS: DCNN exhibited 100% sensitivity and 97.31% specificity for wet-AMD images, with an average AUC of 99.76%. Moreover, comparing the diagnostic abilities of DCNN versus six ophthalmologists, the average accuracy of the DCNN was 100%. On the other hand, the accuracy of ophthalmologists, determined only by Optos images without a fundus examination, was 81.9%. CONCLUSION: A combination of DCNN with Optos images is not better than a medical examination; however, it can identify exudative AMD with a high level of accuracy. Our system is considered useful for screening and telemedicine.

Effects of kallidinogenase in patients undergoing vitrectomy for diabetic macular edema.

PURPOSE: To evaluate the effectiveness of the combination of vitrectomy with kallidinogenase for diabetic macular edema (DME). METHODS: This study was designed as a prospective, randomized, multicenter study comparing 19 eyes of 19 patients who received 150 units of kallidinogenase administered a day for 52 weeks from the day after vitrectomy (study group) with 20 eyes of 20 patients who received no kallidinogenase (control group). The main outcome measurements included logMAR visual acuity and central foveal thickness (CFT) before surgery and at 3, 6, 9, and 12 months after vitrectomy. RESULTS: During follow-up, 11 patients dropped out (six in the study group and five in the control group), leaving 28 eyes in 28 patients for analysis (13 in the study group and 15 in the control group). Visual acuity improved significantly at 12 months in both groups compared with before surgery. The degree of improvement did not differ significantly between the groups. At 12 months, the mean CFT decreased significantly in both groups, with no significant difference in the rate of change between the two groups. In the study group, the visual acuity and CFT significantly improved from 3 to 12 months and from 6 to 12 months, whereas these parameters did not continue to improve in the control group after 6 months (for visual acuity) or 3 months (for CFT). CONCLUSION: After vitrectomy for DME, visual acuity and CFT improved significantly in both groups, but only patients treated with kallidinogenase continued to have significant improvement throughout the study period.

Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.

PURPOSE: In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM). METHODS: In total, 529 3D-OCT images from the Tsukazaki hospital ophthalmology database (184 non-ERM subjects and 205 ERM patients) were assessed; 80% of the images were divided for training, and 20% for test as follows: 423 training (non-ERM 245, ERM 178) and 106 test (non-ERM 59, ERM 47) images. Using the 423 training images, a model was created with deep convolutional neural network and SVM, and the test data were evaluated. RESULTS: The DL model's sensitivity was 97.6% [95% confidence interval (CI), 87.7-99.9%] and specificity was 98.0% (95% CI, 89.7-99.9%), and the area under the curve (AUC) was 0.993 (95% CI, 0.993-0.994). In contrast, the SVM model's sensitivity was 97.6% (95% CI, 87.7-99.9%), specificity was 94.2% (95% CI, 84.0-98.7%), and AUC was 0.988 (95% CI, 0.987-0.988). CONCLUSION: DL model is better than SVM model in detecting ERM by using 3D-OCT images.

Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.

PURPOSE: We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR). METHODS: We conducted training with the DCNN using 378 photographic images (132 PDR and 246 non-PDR) and constructed a deep learning model. The area under the curve (AUC), sensitivity, and specificity were examined. RESULT: The constructed deep learning model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969. CONCLUSION: Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning.

Usability and reproducibility of tear meniscus values generated via swept-source optical coherence tomography and the slit lamp with a graticule method.

PURPOSE: To investigate the usability and the reproducibility of the tear meniscus values via swept-source optical coherence tomography (SS-OCT) and the conventional slit lamp microscope method with a graticule. METHODS: The right eye was examined in 90 healthy adult subjects who were grouped according to age (group 1: 20-39 years; group 2: 40-59 years; group 3: ≥60 years). The tear meniscus height (TMH) and tear meniscus area were measured using SS-OCT and TMH by the slit lamp microscope method. The reproducibility of each method was calculated using intraclass correlation coefficients (ICCs) in additionally enrolled 30 healthy young subjects. We also evaluated TMH at 3 mm from the corneal center in both temporal and nasal directions using SS-OCT. RESULTS: The mean of the TMH values measured by SS-OCT was significantly higher than those measured by the slit lamp method (328 vs. 212 µm, P < 0.001, respectively). High reproducibility was observed for each method (ICC > 0.75 for both). No statistically significant differences were found in TMH among the age groups using both SS-OCT and slit lamp methods (P = 0.985, 0.380, respectively). TMH values at both sides of the corneal center were significantly smaller than those at the corneal center (P < 0.0001). CONCLUSIONS: TMH values obtained by the slit lamp method were lower than those obtained by SS-OCT. However, both methods yielded highly reproducible TMH measurements, suggesting that they are clinically useful. Tear meniscus values did not vary by age but by measurement points in our cohort.

Agreement among Goldmann applanation tonometer, iCare, and Icare PRO rebound tonometers; non-contact tonometer; and Tonopen XL in healthy elderly subjects.

PURPOSE: To evaluate the inter-device agreement among the Goldmann applanation tonometer (GAT), iCare and Icare PRO rebound tonometers, non-contact tonometer (NCT), and Tonopen XL tonometer. METHODS: Sixty healthy elderly subjects were enrolled. The intraocular pressure (IOP) in each subject's right eye was measured thrice using each of the five tonometers. Intra-device agreement was evaluated by calculating intraclass correlation coefficients (ICCs). Inter-device agreement was evaluated by ICC and Bland-Altman analyses. RESULTS: ICCs for intra-device agreement for each tonometer were >0.8. IOP as measured by iCare (mean ± SD, 11.6 ± 2.5 mmHg) was significantly lower (p < 0.05) than that measured by GAT (14.0 ± 2.8 mmHg), NCT (13.6 ± 2.5 mmHg), Tonopen XL (13.7 ± 4.1 mmHg), and Icare PRO (12.6 ± 2.2 mmHg; Bonferroni test). There was no significant difference in mean IOP among GAT, NCT, and Tonopen XL. Regarding inter-device agreement, ICC was lower between Tonopen XL and other tonometers (all ICCs < 0.4). However, ICCs of GAT, iCare, Icare PRO, and NCT showed good agreement (0.576-0.700). The Bland-Altman analysis revealed that the width of the 95% limits of agreement was larger between the Tonopen XL and the other tonometers ranged from 14.94 to 16.47 mmHg. Among the other tonometers, however, the widths of 95% limits of agreement ranged from 7.91 to 9.24 mmHg. CONCLUSION: There was good inter-device agreement among GAT, rebound tonometers, and NCT. Tonopen XL shows the worst agreement with the other tonometers; therefore, we should pay attention to its' respective IOP. CLINICAL TRIAL REGISTRATION: Japan Clinical Trials Register; number: UMIN000011544.

Changes in choroidal thickness in patients with diabetic retinopathy.

PURPOSE: To investigate the changes in choroidal thickness (ChT) following panretinal photocoagulation (PRP) for diabetic retinopathy (DR) and compare ChT in relation to DR severity. METHODS: Thirty-two eyes [19 eyes with proliferative DR (PDR) and 13 eyes with severe nonproliferative DR (NPDR)] for which PRP was necessary were analyzed. ChT was measured before PRP and at 1, 3, and 6 months after PRP using the swept-source optical coherence tomography. ChT of the 61 eyes matched with the PDR patients for the mean age and axial length was also measured and statistically compared in relation to severity. RESULTS: The central field ChT before PRP treatment was 268.6 ± 104.5 µm (mean ± standard deviation) and was significantly decreased at 1, 3, and 6 months after PRP (254.5 ± 105.3, 254.2 ± 108.2, and 248.1 ± 101.8 µm, respectively, P < 0.0001). The central field ChT of severe NPDR (323.2 ± 61.3 µm) was significantly thicker than that of normal (248.3 ± 70.7 µm) and mild to moderate NPDR (230.0 ± 70.3 µm, P = 0.0455 and 0.0099, respectively). Moreover, the central field ChT of PDR (307.3 ± 84.1 µm) was significantly thicker than of mild to moderate NPDR (P = 0.0169). CONCLUSION: ChT significantly decreased after PRP, which continued for at least 6 months after treatment. ChT of severe NPDR and PDR was significantly thicker than that of mild to moderate NPDR. ChT of patients with DR was changed according to the treatment and severity of DR.

Comparison of semi-automated center-dot and fully automated endothelial cell analyses from specular microscopy images.

PURPOSE: To evaluate two specular microscopy analysis methods across different endothelial cell densities (ECDs). METHODS: Endothelial images of one eye from each of 45 patients were taken by using three different specular microscopes (three replicates each). To determine the consistency of the center-dot method, we compared SP-6000 and SP-2000P images. CME-530 and SP-6000 images were compared to assess the consistency of the fully automated method. The SP-6000 images from the two methods were compared. Intraclass correlation coefficients (ICCs) for the three measurements were calculated, and parametric multiple comparisons tests and Bland-Altman analysis were performed. RESULTS: The ECD mean value was 2425 ± 883 (range 516-3707) cells/mm2. ICC values were > 0.9 for all three microscopes for ECD, but the coefficients of variation (CVs) were 0.3-0.6. For ECD measurements, Bland-Altman analysis revealed that the mean difference was 42 cells/mm2 between the SP-2000P and SP-6000 for the center-dot method; 57 cells/mm2 between the SP-6000 measurements from both methods; and -5 cells/mm2 between the SP-6000 and CME-530 for the fully automated method (95% limits of agreement: - 201 to 284 cell/mm2, - 410 to 522 cells/mm2, and - 327 to 318 cells/mm2, respectively). For CV measurements, the mean differences were - 3, - 12, and 13% (95% limits of agreement - 18 to 11, - 26 to 2, and - 5 to 32%, respectively). CONCLUSIONS: Despite using three replicate measurements, the precision of the center-dot method with the SP-2000P and SP-6000 software was only ± 10% for ECD data and was even worse for the fully automated method. CLINICAL TRIAL REGISTRATION: Japan Clinical Trials Register ( http://www.umin.ac.jp/ctr/index/htm9 ) number UMIN 000015236.