PurposeTo compare the visual performance of multifocal intraocular lenses (IOLs) and monofocal IOLs made of the same material.MethodsThe subjects included patients implanted with either Tecnis® monofocal IOLs (ZA9003 or ZCB00) or Tecnis® multifocal IOLs (ZMA00 or ZMB00) bilaterally. We conducted a retrospective study comparing the two types of IOLs. The multifocal group included 46 patients who were implanted with Tecnis® multifocal IOLs bilaterally. The monofocal group was an age- and sex-matched control group, and included 85 patients who were implanted with Tecnis® monofocal IOLs bilaterally. Lens opacity grading, the radius of corneal curvature, corneal astigmatism, axial length and the refractive status were measured preoperatively. Pupil size, ocular aberrometry, distance, intermediate and near visual acuity, contrast sensitivity with and without glare and the responses to a quality-of-vision questionnaire were evaluated pre- and postoperatively.ResultsThe uncorrected near visual acuity was significantly better in the multifocal group, whereas both the corrected intermediate and near visual acuity were better in the monofocal group. Contrast sensitivity (with and without glare) was significantly better in the monofocal group. The rate of spectacle dependency was significantly lower in the multifocal group. There were no significant differences between the two groups regarding most items of the postoperative quality-of-vision questionnaire (VFQ-25), with the exception that the patients in the monofocal group reported fewer problems with nighttime driving.ConclusionsThe multifocal IOLs used in this study reduced spectacle dependency more so than monofocal IOLs and did not compromise the subjective visual function, with the exception of nighttime driving.

No author has a financial or proprietary interest in any material or method mentioned.

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.

PurposeTo investigate the changes in choroidal thickness (ChT) following panretinal photocoagulation (PRP) for diabetic retinopathy (DR) and compare ChT in relation to DR severity.MethodsThirty-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.ResultsThe 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).ConclusionChT 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10792-017-0459-9) contains supplementary material, which is available to authorized users.

Purpose. The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. Method. The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed. Result. The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively. Conclusion. The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.