ライフサイエンスコーパス: fundus image

1 cal coherence tomography [OCT] and widefield fundus imaging).

2 of view (FOV) is demonstrated in a snapshot fundus image.

3 lar fundus image to the peripapillary ocular fundus image.

4 stance of 2.5 cm was set with a good quality fundus image.

5 normality in 25/26 (96.2%) of the ungradable fundus images.

6 re sensitive than that of nonmydriatic color fundus images.

7 easure the vessel widths at branch points in fundus images.

8 quantify various retinal vascular changes in fundus images.

9 optic nerve head were clearly visible on the fundus images.

10 100 degrees field-of-view, composite retinal fundus images.

11 tinal images selected by the model contained fundus images.

12 SS between 1 (normal) and 9 (most severe) to fundus images.

13 tals were superimposed on aligned multimodal fundus images.

14 me to achieve vessel segmentation in retinal fundus images.

15 o estimate RNFL thickness from disc-centered fundus images.

16 ular degeneration (AMD) from ultrawide field fundus images.

17 age-related macular degeneration (AMD) using fundus images.

18 highlighting the regions of interest in the fundus images.

19 poration, Tokyo, Japan) were used to capture fundus images.

20 oth local and global appearance markers from fundus images.

21 rithms for diagnosing DR based on real-world fundus images.

22 tative measurement of vascular structures in fundus images.

23 al filtering, and thresholding of the colour fundus images.

24 y on phenotypical characteristics from color fundus images.

25 ct over 20 features from retinal vasculature fundus images.

26 that do not require subjective evaluation of fundus images.

27 in and/or modulate the observed AF signal in fundus images.

28 moscope for high-resolution confocal retinal fundus imaging.

29 iographs, and diabetic retinopathy on retina fundus imaging.

30 Diagnosis was confirmed by masked review of fundus imaging.

31 al testing, optical coherence tomography and fundus imaging.

32 and documentation of vascular change without fundus imaging.

33 raocular pressure, visual field testing, and fundus imaging.

34 , ophthalmoscopy, handheld OCT and widefield fundus imaging.

35 frared autofluorescence, and ultra-widefield fundus imaging.

36 from optical coherence tomography and color fundus imaging.

37 classify Parkinson's disease from UK Biobank fundus imaging.

38 ptical coherence tomography (OCT), and color fundus imaging.

39 d widefield pseudocolor and autofluorescence fundus imaging.

40 Retinopathy was evaluated from digital fundus images (2002-2006) using the modified Airlie Hous

41 patients with type 1 diabetes and ungradable fundus images, 361 participants were included in the ana

42 common reasons for referral were ungradable fundus image (39.3% of those referred), best-corrected V

43 Twenty-six fundus images (5.5%) were ungradable.

44 re enrolled, of whom 50 had gradable-quality fundus images (78%).

45 s of macular images estimated this VA from a fundus image, AI might provide spectacle-corrected VA wi

46 An OCT fundus image, akin to a fundus photograph was generated

47 OCT fundus images allow precise registration of OCT images a

48 A DL algorithm trained on fundus images alone can detect referable GON with higher

49 andard diagnosis when interpreting the color fundus images alone versus interpreting the color fundus

50 conventional AAV2 in GFP expression based on fundus image analysis and qRT-PCR.

51 OCT fundus images and 3D visualizations were generated with

52 uate the proposed scheme on standard retinal fundus images and achieve superior performance measures,

53 d strengths of both the remote evaluation of fundus images and bedside clinical examination of infant

54 The algorithm processed color fundus images and classified them as healthy (no retinop

55 henotypic information available from retinal fundus images and clinical measurements, in addition to

56 r exposure to OIR, which was visible in live fundus images and fixed whole-mounted retinas.

57 For each eye, two undilated 45 degrees fundus images and four undilated volume OCT image sets c

58 R-AF images are high-quality, high-contrast fundus images and image interpretation may build on clin

59 e (AI) is currently being used for screening fundus images and monitoring diabetic retinopathy (DR).

60 Fundus images and OCT scans were successfully acquired i

61 ical data, ultra-widefield (UWF) pseudocolor fundus images and Spectral-Domain Optical Coherence Tomo

62 the detection of optic disc and optic cup in fundus images and the subsequent calculation of VCDR.

63 the acquisition of two undilated 45 degrees fundus images and two undilated raster 3D-OCT scans (512

64 in the study, 9962 (99.3%) who had gradable fundus images and Visual Function Index (VF-11) data ava

65 usen (RPD) was assessed by masked grading of fundus images and was confirmed with optical coherence t

66 onfocal scanning laser ophthalmoscopy (cSLO) fundus imaging and "eye-tracked" spectral-domain optical

67 Thresholds were evaluated by fundus imaging and angiography.

68 Using ophthalmic fundus imaging and computational modeling, we show that

69 Live fundus imaging and fluorescein angiography at P17 were c

70 The degree of laser burns was confirmed by fundus imaging and histology.

71 In vivo fluorescent fundus imaging and immunofluorescent confocal microscopy

72 Compared with studies using fundus imaging and international classification systems,

73 Fundus imaging and microscopic analysis indicated that K

74 FP expression was noninvasively monitored by fundus imaging and retinal expression was analyzed 4 wee

75 OCT), near-infrared reflectance (NIR), color fundus images, and medical charts were reviewed.

76 strated tracing individual vessel trees from fundus images, and simultaneously retain vessel hierarch

77 OP]) was developed using a set of 77 digital fundus images, and the system was designed to classify i

78 their axons in the retina was determined by fundus imaging, and axonal degeneration in the optic ner

79 optical coherence tomography (OCT), infrared fundus imaging, and biomicroscopy were performed at base

80 ing complementary data sources, such as OCT, fundus imaging, and clinical information, enhances model

81 nitored over an extended period of time with fundus imaging, and further investigated with OCT and OC

82 The clinical details, digital fundus imaging, and treatment details and outcomes also

83 pert fundus grading of 468 patients and 2145 fundus images are: 98.6% and 96.3% when classifying cate

84 ll participants underwent color, FAF, and IR fundus imaging, as well as imaging with a prototype Zeis

85 rnational Classification System on digitized fundus images at 1 grading center.

86 a: (1) complete clinical records and digital fundus images at baseline and follow-up visits, (2) posi

87 We reanalyzed the fundus images at baseline, week 12, and week 52 to asses

88 Fundus images at two or more time points were analyzed u

89 ed ROP vascular severity score (from retinal fundus images) at the initial tele-retinal screening exa

90 From the 5,512 fundus images attempted, we found that 4,267 (77.41%) we

91 s underwent standard ophthalmic examination, fundus imaging, autofluorescence testing, Goldmann visua

92 Smartphone-based fundus imaging based on indirect ophthalmoscopy yielded

93 wo-field retinal imaging was used to capture fundus images before and after pupil dilatation, using a

94 Multimodal fundus images, best-corrected visual acuity (BCVA), and

95 -automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network

96 ngs and 100 vein branchings selected from 50 fundus images by comparing with vessel width measurement

97 teral bevacizumab (0.25 mg) and had adequate fundus imaging by a certified ophthalmic photographer at

98 rning-based automated assessment of AMD from fundus images can produce results that are similar to hu

99 Smartphone-based fundus imaging can meet DR screening requirements in an

100 ion cohorts and via a prospective study with fundus images captured with smartphones, and assessed th

101 oma software analyzed 42-degree disc-centric fundus images, categorizing them as normal, glaucoma, or

102 tic red-free monochromatic 60-degree digital fundus images centered on the macula and optic disc of 2

103 y was a secondary analysis of posterior pole fundus images collected during the multicenter, double-b

104 Multimodal fundus images (color, FA, and SD-OCT) of 130 eyes with n

105 , visual fields) and structural evaluations (fundus imaging [color and autofluorescence], OCT, slit-l

106 ptable performance in screening for DR using fundus images compared to human graders.

107 cial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reduci

108 Automated anaemia screening on the basis of fundus images could particularly aid patients with diabe

109 A fundus image dataset from 14 patients (200 fundus images

110 hm was evaluated on two publically available fundus-image datasets comprising 258 images (160 AMD and

111 Fundus imaging demonstrated that R172W mice developed se

112 rol the position of the OCT beam spot on the fundus image display.

113 rvised deep learning network using unlabeled fundus images, enabling data-driven feature classificati

114 s of a semiautomated optical head to acquire fundus images, evaluate visual acuity, and transmit the

115 athy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmolog

116 bright lesions (drusen and flecks) in their fundus images, even when the images were visually select

117 or worse, IOP 23-29 mm Hg, or an unreadable fundus image failed the screening and were scheduled for

118 fluorescein angiogram (FA) or red-free (RF) fundus images; fluorescein angiography was used in this

119 p, highlighting subregions within each input fundus image for further clinical review.

120 mitted the most recent color, OCT, and other fundus images for 468 participants to a reading center.

121 Fundus images for each patient were evaluated, including

122 erized by systematic review of all available fundus images for each patient, including color photogra

123 ties of 3D-OCT were higher than nonmydriatic fundus images for overall detection of retinal abnormali

124 athy Study [ETDRS] and ultra-widefield [UWF] fundus images for PDR) interpreted by trained nonmedical

125 econstructing a wide-angle composite retinal fundus image from a set of adjacent small- and wide-angl

126 Fundus images from 43 monozygotic (MZ) and 32 dizygotic

127 We used 97 895 retinal fundus images from 54 813 UK Biobank participants.

128 Fundus images from 58 eyes (in 58 patients) with interme

129 A total of 75 137 publicly available fundus images from diabetic patients were used to train

130 s using a reading center to evaluate retinal fundus images from infants at risk for retinopathy of pr

131 ature at very early stages of diabetes using fundus images from preclinical models of diabetes.

132 primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10)

133 Here we trained deep learning models on fundus images from the EyePACS dataset to predict indivi

134 n a genome-wide association study of retinal fundus images from the UK Biobank.

135 (e-ROP) Study telemedicine system of remote fundus image grading and The Children's Hospital of Phil

136 ity imaging could be obtained, non-mydriatic fundus images had a better DOR of 143 (95% CI: 82-251) a

137 w that the methodology used for grading CATT fundus images has good reproducibility.

138 ell tolerated, as assessed by clinical exam, fundus imaging, histological analysis, and intraocular p

139 lar telangiectasia type 2 in whom multimodal fundus imaging identified neuronal features without clin

140 ms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct

141 latforms for predicting binary outcomes from fundus images in ophthalmology.

142 fully automated pipeline that analyzes color fundus images in patients with tubercular serpiginous-li

143 edly suppressed angiogenesis as evaluated by fundus imaging in aged Ins2(Akita) mice even after 3mont

144 since implementation of nonmydriatic ocular fundus imaging in our ED.

145 xhibit characteristic findings on multimodal fundus imaging in the setting of high cumulative exposur

146 ation of PPS maculopathy by masked review of fundus imaging in this dataset.

147 Fundus imaging included color photography, red-free imag

148 emale with known HHT and a series of retinal fundus images including optical coherence tomography (OC

149 All patients underwent ultra-widefield fundus imaging including pseudocolor and fluorescein ang

150 Ultra-widefield fundus imaging, including color fundus photography and a

151 terized by systematic analysis of multimodal fundus imaging, including color photographs, fundus auto

152 Clinical features and fundus imaging, including fluorescein and indocyanine gr

153 mination, fundus photography, and multimodal fundus imaging, including Fourier-domain optical coheren

154 ided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate

155 ionally, pre-processing of the input retinal fundus image is performed by Global Contrast Normalizati

156 optic nerve through ophthalmoscopy or using fundus images is a crucial component of glaucoma detecti

157 segmentation of retinal vasculature from eye fundus images is a fundamental task in retinal image ana

158 idth relationship at vessel branch points in fundus images is an important biomarker of retinal and s

159 However, manual assessment of fundus images is both time-consuming and subject to vari

160 AO fundus imaging is a reliable technique for assessing and

161 tual fixation as assessed under simultaneous fundus imaging, its correlation with the established exp

162 tor (VEGF) injections when needed and stereo fundus images looking at the regression of NVs.

163 been biased by the effect of axial length on fundus image magnification and, therefore, both measured

164 The CMR sign seen on ultra-widefield fundus imaging may be a distinctive feature of foveal hy

165 eld and Optos fundus imaging methods, Clarus fundus imaging methods exhibited excellent performance i

166 Thus, Clarus fundus imaging methods were superior for early detection

167 pared with conventional five-field and Optos fundus imaging methods, Clarus fundus imaging methods ex

168 by weighted kappa statistics among the three fundus imaging methods.

169 tional five-field, UWF Optos, and UWF Clarus fundus imaging methods.

170 ultrawide-field (UWF) Optos, and UWF Clarus fundus imaging methods.

171 Smartphone-based fundus imaging might aid in alleviating the burden of DR

172 t are not generally visible with traditional fundus imaging modalities.

173 es using alternative classifications without fundus imaging most likely to diagnose late AMD (OR, 2.9

174 structure for DR detection utilizing retinal fundus image named Mobile Maxout network (MM-Net).

175 Recognizing DR utilizing color fundus imaging needs qualified specialists to know about

176 nts were at risk for developing AMD based on fundus images obtained at baseline visits.

177 Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical,

178 A total of 1046 wide-angle fundus images obtained from 19 infants at predefined stu

179 ular degeneration was easily identified from fundus images obtained from the low-cost camera.

180 me, or facilitate home monitoring of VA from fundus images obtained outside of the clinic.

181 Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less po

182 ive hundred and one sets of 3D-OCT scans and fundus images of 395 eyes of 223 patients were found in

183 al imaging and scanning laser ophthalmoscopy fundus images of all three Crb1(rd8/rd8) lines showed a

184 ences in the various extracted features from fundus images of diabetic and non-diabetic animals.

185 Nonmydriatic fundus images of diabetic eyes acquired at primary care

186 n telemedicine screening for ROP, we present fundus images of eyes with a pseudo-notch appearance; re

187 esultsWe demonstrate spectrally resolved TPE fundus images of human subjects.

188 ocal scanning laser ophthalmoscope (cSLO) AF fundus images of normal maculae were obtained from both

189 ver non-AMD and AMD eyes and ultra-widefield fundus images of patients revealed differential vulnerab

190 ning in classifying full-scale DR in retinal fundus images of patients with diabetes.

191 Three hundred thirty-five fundus images of prematurely born infants were obtained

192 d in 2007 and 2016 to classify 34 wide-field fundus images of ROP as plus, pre-plus, or normal, coded

193 rospective study, we analyzed paired retinal fundus images of the same eye captured at >= 1-year inte

194 Analysis included 7185 macular color fundus images of the study and fellow eyes from 459 part

195 autofluorescence, observed as bright dots in fundus imaging of live animals, coinciding with patholog

196 cs using simultaneous Raman spectroscopy and fundus imaging of the neuroretina.

197 d 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for e

198 We highlight retinal fundus imaging, often termed a window to the brain, as a

199 V2 produced visible transduction, as seen in fundus images, only when the retina was altered by gangl

200 study participants from the UK Biobank, the fundus-image-only, metadata-only and combined models pre

201 PE- defects in patients with AMD using Color fundus images, Optical coherence tomography (OCT), OCT-A

202 l data, color, infrared and autofluorescence fundus imaging, optical coherence tomographic scans, and

203 Multicolor fundus imaging, optical coherence tomography (OCT), and

204 dney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (

205 resence of definite irregularities on either fundus imaging or OCT by eye in this asymptomatic popula

206 gnosis, as per specialist grading of retinal fundus imaging (OR 0.90 95% CI [0.84, 0.98]; P = .011).

207 and 15 non-ICROP3 members) METHODS: Nine ROP fundus images (P1 through P9) representing increasing de

208 s who had digital autofluorescence and color fundus imaging performed at the University of Michigan K

209 ding center-certified graders based on color fundus imaging performed every 12 weeks using the invest

210 Vessel segmentation in fundus images permits understanding retinal diseases and

211 Retinal fundus images provide valuable insights into the human e

212 n order to obtain detectable signal with low fundus image quality (suboptimal setting) while in the s

213 ataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage an

214 ores generated from low- and high-resolution fundus images, respectively, can help identify patients

215 OCT, OCT angiography (OCTA), ultrawidefield fundus imaging, retinal autofluorescence, dark adaptatio

216 SIGN, SETTING, AND PARTICIPANTS: The retinal fundus images (RFIs) of neonates with parent-reported Bl

217 Smartphone-based fundus imaging (SBFI) allows for low-cost mobile fundus

218 Cheaper, smartphone-based fundus imaging (SBFI) systems have been described, but t

219 Annual fundus image sets from 114 CAPT patients who developed G

220 Normal AF fundus images show finely resolved, concentric, elliptic

221 essment of composite OCT scans and composite fundus images showed little motion artifact or blurring

222 Non-invasive fundus imaging showed widespread photoreceptor loss, con

223 OCT fundus images similar to those acquired with a scanning

224 igital image file compression and decreasing fundus image spatial resolution led to skewed measuremen

225 ull-field electroretinography and multimodal fundus imaging (spectral-domain optical coherence tomogr

226 OV now similar to autofluorescence and color fundus imaging, SS OCT imaging can be used as the sole i

227 Both eyes of all patients were studied using fundus imaging, SS-OCT, fundus fluorescein angiography (

228 ne-learning algorithms trained using retinal fundus images, study participant metadata (including rac

229 ophthalmologist masked to the results of the fundus images subsequently examined each eye with indire

230 ETTING, AND PARTICIPANTS: Deidentified color fundus images taken after dilation were used post hoc to

231 Early or late AMD, assessed through fundus images taken through dilated pupils using a 45 de

232 Fundus images, taken using a digital camera through dark

233 , validate, and correlate topical endoscopic fundus imaging (TEFI) with histologic features of murine

234 for automated AMD detection and grading from fundus images, their current reliability is insufficient

235 feature after registering the macular ocular fundus image to the peripapillary ocular fundus image.

236 ize color and nonuniform illumination of the fundus images to define a region of interest and to iden

237 eading center, including evaluation of color fundus imaging to assess AMD severity scores.

238 methods for automatically detecting AMD from fundus images using a novel application of deep learning

239 erent duration of diabetes were subjected to fundus imaging using a Micron III imaging system.

240 ternal eye examination, red reflex test, and fundus imaging using a wide-field digital retinal imagin

241 Autofluorescence fundus imaging using an adaptive optics scanning laser o

242 sual acuity (VA), best-corrected VA, digital fundus imaging, visual field testing, and measurement of

243 Ultra-widefield fundus imaging was staged per the Retina Society 1991 PV

244 Color fundus imaging was used to assess AMD severity and hyper

245 ffective, but they rely on widefield digital fundus imaging (WDFI) cameras, which are expensive, maki

246 In ultrawide-field fundus images, we observed radially arranged puncta typi

247 For each eye, the 2 fundus images were aligned using Heidelberg's AutoRescan

248 February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an

249 n in two central macular regions on baseline fundus images were determined using an image analysis al

250 Composite OCT scans and composite fundus images were generated for assessment of eye track

251 Fundus images were graded for AMD using the Wisconsin Ag

252 Fundus images were graded for the presence of peripapill

253 Fundus images were graded using the International Classi

254 Drusen area measurements on color fundus images were larger than those with SD-OCT scans.

255 ectroretinographic (ERG) records and digital fundus images were obtained at P20 +/- 1, P30 +/- 1, and

256 RetCam (Natus Medical Incorporated) fundus images were obtained from premature infants durin

257 Previous foundation models for fundus images were pre-trained with limited disease cate

258 The color fundus images were registered to the OCT data set and me

259 plore image compression, 40 natively digital fundus images were selected with good photo quality, hig

260 In total 104 fundus images were subjected to analysis for various fea

261 Ultra-widefield pseudocolor fundus images were taken from the eyes of clinic patient

262 Digital color fundus images were taken on the same day.

263 Paired monochromatic and autofluorescence fundus images were used for detailed analysis of the top

264 the publicly available EyePACS data set with fundus images with a corresponding label ranging from 0

265 The model is pre-trained on 341,896 fundus images with accompanying text descriptions gather

266 Replacement of film fundus images with digital images for DR severity level

267 single, foveal nonmydriatic 45 degrees color fundus imaging with 3D-OCT-1000 in a 4 month-period were

268 tional classification systems, studies using fundus imaging with alternative classifications were mor

269 A fundus image dataset from 14 patients (200 fundus images, with an average of 14 images per patient)

270 pigmentation was observed on widefield color fundus imaging, with hypofluorescence on FAF images and

271 c Instance Discrimination (NPID) using AREDS fundus images without labels then evaluated its performa

272 sarial networks to create vascular maps from fundus images without training using manual vessel segme