Retinal phenotyping of variants of Alzheimer's disease using ultra-widefield retinal images.
Ontology highlight
ABSTRACT: Background
Posterior cortical atrophy (PCA) is the most common atypical variant of Alzheimer's disease (AD). Changes associated with PCA in the brain affect the visual cortex, but little is known about retinal changes in PCA. In this study, we explored retinal phenotypic variations in typical AD (tAD) and PCA.Methods
Retinal phenotyping was carried out on ultra-widefield (UWF) images of 69 control, 24 tAD, and 25 PCA participants.Results
Individuals with tAD (odds ratio [OR] = 2.76 [confidence interval (CI):1.24 to 6.10], P = .012) and PCA (OR = 3.40 [CI:1.25 to 9.22], P = .016) were more likely phenotyped as hard drusen. tAD (OR = 0.34 [CI:0.12 to 0.92], P = .035) were less likely to have soft drusen compared to control. Almost 3-fold increase in reticular pseudodrusen formation in tAD (OR = 2.93 [CI:1.10 to 7.76], P = .030) compared to control was estimated.Discussion
Studying the peripheral retina may contribute to a better understanding of differences in retinal phenotypes of different AD variants.
SUBMITTER: Csincsik L
PROVIDER: S-EPMC8377778 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.
| S-EPMC6949241 | biostudies-literature
Project description:PurposeTo examine the usefulness of red channel fundus imaging to detect the ischemic status in eyes with central retinal vein occlusion (CRVO).MethodsUltra-widefield (UWF) fundus images were obtained from 42 eyes with CRVO. Twenty-one eyes were ischemic, and 21 eyes were non-ischemic. Rubeosis was found in 11 ischemic eyes. UWF images were split into red and green channels using ImageJ software. Both the color and red channel images were used to predict the presence or absence of ischemia when examined by masked graders. The sensitivity and specificity of UWF imagings for the detection of ischemia were calculated in Group A (total 42 eyes), Group B (32 eyes excluding non-rubeotic ischemic CRVO) and Group C (31 eyes excluding rubeotic ischemic CRVO), respectively. Moreover, a linear mixed model was conducted to investigate the relationship between the type of images and the accuracy of prediction in each group.ResultsNo significant difference in the sensitivity of color fundus imaging was seen between Group A and Group B. By contrast, a significant difference in the sensitivity of red channel imaging was seen between Group A and Group B (p = 0.031). The accuracies of the predictions were not associated with the type of image in Group A and Group B, but were significantly associated in Group C (p = 0.026).ConclusionsUWF red channel imaging enabled more accurate detection of the ischemic status, compared with color fundus images, especially in non-rubeotic CRVO eyes.
| S-EPMC8610275 | biostudies-literature
Project description:Ultra-widefield retinal imaging is increasingly used in ophthalmology and optometry practices to image patients identifying peripheral abnormalities. However, the clinical relevance of these peripheral retinal abnormalities is unclear. This cross-sectional study aims to firstly validate a new grading system, secondly, assess the prevalence of peripheral retinal abnormalities in retinal patients, and finally understand how peripheral findings may associate with retinal disease. Ultra-widefield pseudocolor fundus images were taken from the eyes of clinic patients. Demographic data and clinical diagnosis for each patient was noted. The grading system was validated using masked retinal specialists. Logistic regression identified associations between retinal disease and peripheral retinal findings. Using the grading system, inter-observer agreement was 76.1% with Cohen's Kappa coefficient 0.542 (p < 0.0001) and the test-retest agreement was 95.1% with Kappa 0.677(p < 0.0001). 971 images were included, with 625 eyes (64.4%) having peripheral abnormalities. Peripheral drusen was the most common abnormality (n = 221, 22.76%) and correlated with age-related macular degeneration (p < 0.001). Novel correlations were also identified between diabetic retinopathy and retinal pigmentation as well as pigmentary degeneration. This study provides a validated system for identifying peripheral abnormalities and adds to literature highlighting peripheral retinal associations with retinal disease which would benefit from further study.
| S-EPMC10665364 | biostudies-literature
Project description:IntroductionCompared with traditional fundus examination techniques, ultra-widefield fundus (UWF) images provide 200° panoramic images of the retina, which allows better detection of peripheral retinal lesions. The advent of UWF provides effective solutions only for detection but still lacks efficient diagnostic capabilities. This study proposed a retinal lesion detection model to automatically locate and identify six relatively typical and high-incidence peripheral retinal lesions from UWF images which will enable early screening and rapid diagnosis.MethodsA total of 24,602 augmented ultra-widefield fundus images with labels corresponding to 6 peripheral retinal lesions and normal manifestation labelled by 5 ophthalmologists were included in this study. An object detection model named You Only Look Once X (YOLOX) was modified and trained to locate and classify the six peripheral retinal lesions including rhegmatogenous retinal detachment (RRD), retinal breaks (RB), white without pressure (WWOP), cystic retinal tuft (CRT), lattice degeneration (LD), and paving-stone degeneration (PSD). We applied coordinate attention block and generalized intersection over union (GIOU) loss to YOLOX and evaluated it for accuracy, sensitivity, specificity, precision, F1 score, and average precision (AP). This model was able to show the exact location and saliency map of the retinal lesions detected by the model thus contributing to efficient screening and diagnosis.ResultsThe model reached an average accuracy of 96.64%, sensitivity of 87.97%, specificity of 98.04%, precision of 87.01%, F1 score of 87.39%, and mAP of 86.03% on test dataset 1 including 248 UWF images and reached an average accuracy of 95.04%, sensitivity of 83.90%, specificity of 96.70%, precision of 78.73%, F1 score of 81.96%, and mAP of 80.59% on external test dataset 2 including 586 UWF images, showing this system performs well in distinguishing the six peripheral retinal lesions.ConclusionFocusing on peripheral retinal lesions, this work proposed a deep learning model, which automatically recognized multiple peripheral retinal lesions from UWF images and localized exact positions of lesions. Therefore, it has certain potential for early screening and intelligent diagnosis of peripheral retinal lesions.
| S-EPMC9872743 | biostudies-literature
Project description:BackgroundRetinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screening on a large scale is time-consuming and labour-intensive. Here, we aim to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images.MethodsA total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The Zhongshan Ophthalmic Center (ZOC) dataset was selected to compare the performance of the system to that of retina specialists in RED detection. The saliency map visualization technique was used to understand which areas in the UWF image had the most influence on our deep learning system when detecting RED.ResultsThe system for RED detection achieved areas under the receiver operating characteristic curve of 0.994 (95% confidence interval [CI]: 0.991-0.996), 0.972 (95% CI: 0.957-0.984), and 0.988 (95% CI: 0.983-0.992) in three independent datasets. The performance of the system in the ZOC dataset was comparable to that of an experienced retina specialist. Regions of RED were highlighted by saliency maps in UWF images.ConclusionsOur deep learning system is reliable in the automated detection of RED in UWF images. As a screening tool, our system may promote the early diagnosis and management of RED-related fundus diseases.
| S-EPMC9307785 | biostudies-literature
Project description:We evaluated the clinical utility of ultra-widefield imaging as an adjunctive tool for training resident ophthalmologists in the detection of retinal breaks. This was a prospective study conducted at a secondary health care center (Tri-Service General Hospital) in Taiwan. Participants were 44 patients (53 eyes) who were referred to our hospital after being diagnosed with retinal breaks. Patients first underwent an indirect ophthalmoscopy examination of the total fundus without scleral depression by our junior (first and second year) or senior (third and fourth year) resident ophthalmologist and then underwent an ultra-widefield imaging examination with a central image and four gaze-steered (up, down, nasal, and temporal) images to determine the number of retinal breaks in the total fundus and the four quadrants. Of the total 53 eyes, 31 were examined by junior residents and 22 were examined by senior residents. In the group of junior residents, ultra-widefield imaging was significantly better at detecting retinal breaks of the total fundus (49 vs. 33 retinal breaks, p < 0.001) and the temporal quadrant (17 vs. 10 retinal breaks, p = 0.018) than indirect ophthalmoscopy. In the group of senior residents, there was no significant difference in the ability to detect retinal breaks in the total fundus or each of the four quadrants with ultra-widefield imaging or indirect ophthalmoscopy. Our results indicate that, compared to indirect ophthalmoscopy, ultra-widefield imaging with a central image and four gaze-steered images has a better performance and is a useful adjunct tool for the detection of retinal breaks in junior resident training. Additionally, it could be a useful method for teaching indirect ophthalmoscopy examination to junior residents.
| S-EPMC8221479 | biostudies-literature
Project description:PurposeTo investigate retinal vascular characteristics using ultra-widefield (UWF) scanning laser ophthalmoscopy in Parkinson disease (PD).MethodsIndividuals with an expert-confirmed clinical diagnosis of PD and controls with normal cognition without PD underwent Optos California UWF imaging. Patients with diabetes, uncontrolled hypertension, glaucoma, dementia, other movement disorders, or known retinal or optic nerve pathology were excluded. Images were analyzed using Vasculature Assessment and Measurement Platform for Images of the Retina (VAMPIRE-UWF) software, which describes retinal vessel width gradient and tortuosity, provides vascular network fractal dimensions, and conducts alpha-shape analysis to further characterize vascular morphology (complexity, Opαmin; spread, OpA).ResultsIn the PD cohort, 53 eyes of 38 subjects were assessed; in the control cohort, 51 eyes of 33 subjects were assessed. Eyes with PD had more tortuous retinal arteries in the superotemporal quadrant (P = 0.043). In eyes with PD, alpha-shape analysis revealed decreased OpA, indicating less retinal vasculature spread compared to controls (P = 0.032). Opαmin was decreased in PD (P = 0.044), suggesting increased vascular network complexity. No differences were observed in fractal dimension in any region of interest.ConclusionsThis pilot study suggests that retinal vasculature assessment on UWF images using alpha-shape analysis reveals differences in retinal vascular network spread and complexity in PD and may be a more sensitive metric compared to fractal dimension.Translational relevanceRetinal vasculature assessment using these novel methods may be useful in understanding ocular manifestations of PD and the development of retinal biomarkers.
| S-EPMC10795547 | biostudies-literature
Project description:ImportanceThere is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically.ObjectiveTo examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits.Design, setting, and participantsData for this multicenter, retrospective, cross-sectional study were collected between January 1, 2012, and December 31, 2018. This study included 695 consecutive patients with retinitis pigmentosa who were examined at 5 institutions. Each of the 3 types of input images-ultra-widefield pseudocolor images, ultra-widefield fundus autofluorescence images, and both ultra-widefield pseudocolor and fundus autofluorescence images-was paired with 1 of the 31 types of ensemble models constructed from 5 deep learning models (Visual Geometry Group-16, Residual Network-50, InceptionV3, DenseNet121, and EfficientNetB0). We used 848, 212, and 214 images for the training, validation, and testing data, respectively. All data from 1 institution were used for the independent testing data. Data analysis was performed from June 7, 2021, to December 5, 2022.Main outcomes and measuresThe mean deviation on the Humphrey field analyzer, central retinal sensitivity, and best-corrected visual acuity were estimated. The image type-ensemble model combination that yielded the smallest mean absolute error was defined as the model with the best estimation accuracy. After removal of the bias of including both eyes with the generalized linear mixed model, correlations between the actual values of the testing data and the estimated values by the best accuracy model were examined by calculating standardized regression coefficients and P values.ResultsThe study included 1274 eyes of 695 patients. A total of 385 patients were female (55.4%), and the mean (SD) age was 53.9 (17.2) years. Among the 3 types of images, the model using ultra-widefield fundus autofluorescence images alone provided the best estimation accuracy for mean deviation, central sensitivity, and visual acuity. Standardized regression coefficients were 0.684 (95% CI, 0.567-0.802) for the mean deviation estimation, 0.697 (95% CI, 0.590-0.804) for the central sensitivity estimation, and 0.309 (95% CI, 0.187-0.430) for the visual acuity estimation (all P < .001).Conclusions and relevanceResults of this study suggest that the visual function estimation in patients with retinitis pigmentosa from ultra-widefield fundus autofluorescence images using deep learning might help assess disease progression objectively. Findings also suggest that deep learning models might monitor the progression of retinitis pigmentosa efficiently during follow-up.
| S-EPMC9951103 | biostudies-literature
Project description:To compare early diabetic retinopathy (DR) severity level and the abilities in detecting early DR lesions among conventional five-field, ultrawide-field (UWF) Optos, and UWF Clarus fundus imaging methods. This was a single-center, prospective, clinic-based, and comparative study. In total, 157 consecutive patients with diabetes mellitus were enrolled in this study. All patients underwent comprehensive ophthalmological examinations. Following mydriasis, each eye was examined with conventional five-field, UWF Optos, and UWF Clarus fundus imaging methods. The initial UWF images were overlaid with a template mask that obscured the retina, which created a five-field view from UWF images (covered UWF images). The covered UWF images were then graded, after which the template mask was removed, and the original UWF images were also evaluated. All images were graded using the International Clinical DR severity scale. DR grades were compared and analyzed by weighted kappa statistics among the three fundus imaging methods. In total, 157 consecutive patients with diabetes (302 eyes) were enrolled in this study. Weighted kappa statistics for agreement were 0.471 (five-field vs. covered Optos), 0.809 (five-field vs. covered Clarus), 0.396 (covered Optos vs. covered Clarus), 0.463 (five-field vs. Optos), 0.521 (five-field vs. Clarus 133°), 0.500 (five-field vs. Clarus 200°), 0.323 (Optos vs. Clarus 133°), and 0.349 (Optos vs. Clarus 200°). The area under curve of covered Clarus images was higher than that of conventional five-field images at three different thresholds. Compared with conventional five-field and Optos fundus imaging methods, Clarus fundus imaging methods exhibited excellent performance in assessing early DR severity. Thus, Clarus fundus imaging methods were superior for early detection of DR.
| S-EPMC10564714 | biostudies-literature
Project description:PurposeThe purpose of the study was to audit the use of non-contact ultra-widefield retinal imaging in infants with suspected abusive head trauma (AHT) using the Optos P200MA Scanning Laser Ophthalmoscope.Patients and methodsA retrospective, observational case series. Ten eyes of five consecutive infants (aged 1-15 months) with suspected (or in 1 case, known) AHT referred for an ophthalmological opinion were included. Each infant underwent non-contact ultra-widefield retinal imaging using the Optos P200MA scanning laser ophthalmoscope. Optos fundus fluorescein angiography (FFA) was performed in one infant with oral sedation. The other four infants did not require sedation. The main outcome measure was the acquisition of a single, definitive ultra-widefield retinal image in each eye. Safety was audited by determining adverse changes in heart rate and oxygen saturations that required cessation of imaging.ResultsThe Optos P200MA ultra-widefield scanning laser ophthalmoscope acquired good quality retinal images in all infants. Documentation of acute, widespread retinal haemorrhages contributed to a diagnosis of AHT in three infants. Chronic pre-macular haemorrhage and macular schisis were documented by FFA in a fourth infant. The absence of retinal haemorrhages was documented in a fifth infant contributing to the exclusion of a diagnosis of AHT. There were no adverse safety signals in any infant in this series.ConclusionThe Optos P200MA ultra-widefield scanning laser ophthalmoscope appears safe to use in infants with suspected AHT, providing high-quality retinal images in a single frame without ocular contact. Optos P200MA may be used as alternative to RetCam to document retinal haemorrhages in stable infants with suspected AHT.
| S-EPMC5350378 | biostudies-literature