Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography.

This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs.

Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles.

Conjunctival hyperaemia is a common clinical ophthalmological finding and can be a symptom of various ocular disorders. Although several severity classification criteria have been proposed, none include objective severity criteria. Neural networks and deep learning have been utilised in ophthalmology, but not for the purpose of classifying the severity of conjunctival hyperaemia objectively.

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images.

Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted.

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.

Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion.

Aim: To investigate and compare the efficacy of two machine-learning technologies with deep-learning (DL) and support vector machine (SVM) for the detection of branch retinal vein occlusion (BRVO) using ultrawide-field fundus images.

Methods: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.

Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes.

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images).

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.

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.

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.

Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep learning, a machine-learning technology, to detect RRD using ultra-wide-field fundus images and investigated its performance.

Axial length changes in highly myopic eyes and influence of myopic macular complications in Japanese adults.

Purpose: To investigate changes of the axial length in normal eyes and highly myopic eyes and influence of myopic macular complications in Japanese adults.

Study Design: Retrospective longitudinal case series.

Methods: The changes in the axial length of 316 eyes from 316 patients (mean age, 63.

Morphologic characteristics of macular complications of a dome-shaped macula determined by swept-source optical coherence tomography.

Purpose: To investigate the morphologic characteristics of macular complications of dome-shaped maculas using swept-source optical coherence tomography (OCT).

Design: Retrospective observational case series.

Methods: Axial length measurements and swept-source OCT were performed in 49 highly myopic eyes (in 5 male and 30 female subjects) with dome-shaped maculas.

Changes in choroidal thickness after cataract surgery.

Purpose: To evaluate changes in choroidal thickness before and after cataract surgery and factors affecting the changes.

Setting: Tsukazaki Hospital, Himeji, Japan.

Design: Prospective interventional study.

Comparison of visual performance of multifocal intraocular lenses with same material monofocal intraocular lenses.

Purpose: To compare the visual performance of multifocal intraocular lenses (IOLs) and monofocal IOLs made of the same material.

Methods: The 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.

3-D choroidal thickness maps from EDI-OCT in highly myopic eyes.

Purpose: Myopic chorioretinal atrophy is a debilitating condition that causes severe loss of primary vision. However, its mechanisms and pathologic course are not well understood. We performed volumetric measurements of the posterior choroid via three-dimensional analysis of the choroid in patients with high myopia to understand its structure, and we compared the measurements with those of normal subjects.

Effects of corneal thickness and axial length on intraocular pressure and ocular pulse amplitude before and after cataract surgery.

Objective: To investigate the relationship between the biophysical properties of the cornea and eye on the intraocular pressure (IOP) and ocular pulse amplitude (OPA) before and after cataract surgery.

Design: Intervention study.

Participants: The left eyes of 311 patients.