A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography

Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography scans. Design - Development and validation of deep learning models for classification and feature segmentation. Methods - A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the ROC curve (AUC), kappa, accuracy and intraclass correlation coefficient (ICC). Results - The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification model). The mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. Conclusions - The models achieved high classification and segmentation performance, similar to human performance. Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.Comment: 26 pages, 7 figure

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

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Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images

\u3cp\u3eThis paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)\u3c/p\u3

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs

Purpose: Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. Objective: To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG). Design: Cross-sectional study. Subjects: Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy. Methods: Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as “RG,” “no referable glaucoma (NRG),” or ''ungradable (UG).” In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features. Main Outcome Measures: Qualitative features in eyes with RG. Results: The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet’s AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2–86.7)% and 96.4 (96.3–96.5)%, respectively. Of all gradable eyes (n = 111 183; 97.62%) the prevalence of RG was 4.38%. The most common features of RG were the appearance of the neuroretinal rim (NRR) inferiorly and superiorly. Conclusions: A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references

Convolutional neural networks for segmentation of the left atrium from gadolinium-enhancement MRI images

\u3cp\u3eThis paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)\u3c/p\u3

Convolutional neural networks for segmentation of the left atrium from gadolinium-enhancement MRI images

This paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented

Fish consumption in healthy adults is associated with decreased circulating biomarkers of endothelial dysfunction and inflammation during a 6-year follow-up

A healthy diet rich in fish, fruit, and vegetables, moderate in alcoholic beverages, and low in dairy products has been associated with lower circulating concentrations of biomarkers of endothelial dysfunction (ED) and low-grade inflammation (LGI). It is, however, unknown how consumption of these food groups affects ED and/or LGI over time. We measured diet by the computer-assisted crosscheck dietary history method at 36 ± 0.63 y of age (n = 301, women = 161). At 36 and 42 y of age, we measured von Willebrand factor, soluble intercellular adhesion molecule 1 (sICAM-1), soluble endothelial selectin, soluble vascular cell adhesion molecule 1 and soluble thrombomodulin (circulating biomarkers of ED); and C-reactive protein, serum amyloid A, IL-6, IL-8, TNFa, and sICAM-1 (circulating biomarkers of LGI). We investigated the associations between food groups and changes in combined biomarker Z-scores of ED and LGI [higher scores associated with greater risk of (incident) cardiovascular disease]. After adjustment for sex, energy intake, BMI, physical activity, alcohol consumption, smoking behavior, and other food groups, consumption of fish (per 100 g/wk), but none of the other food groups, was inversely associated with changes in ED [ß (95%CI) = -0.06 (-0.10; -0.02); P = 0.003] and LGI [-0.05 (-0.09; -0.003); P = 0.036]. Additionally, EPA+DHA intake was inversely associated with changes in ED [ß (95%CI) = -0.13 (-0.19; -0.07); P = 0.001] and LGI [-0.09 (-0.16; -0.02); P = 0.013] and explained 83 and 40% of the association between fish and changes in ED and LGI. In conclusion, fish consumption, but not fruit, vegetable, alcoholic beverage, or dairy product consumption, was associated with decreased ED and LGI in healthy adults