Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning.

IMPORTANCE: Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown. OBJECTIVE: To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted. Data were collected from June 1, 2012, to January 31, 2017, for set 1 and from January 1 to December 31, 2017, for set 2; graded between November 2018 and January 2020; and analyzed from February 2020 to November 2020. MAIN OUTCOMES AND MEASURES: Rating and stack ranking for clinical applicability by retinal specialists, model-grader agreement for voxelwise segmentations, and total volume evaluated using Dice similarity coefficients, Bland-Altman plots, and intraclass correlation coefficients. RESULTS: Among the 173 patients included in the analysis (92 [53%] women), qualitative assessment found that automated whole-volume segmentation ranked better than or comparable to at least 1 expert grader in 127 scans (73%; 95% CI, 66%-79%). A neutral or positive rating was given to 135 model segmentations (78%; 95% CI, 71%-84%) and 309 expert gradings (2 per scan) (89%; 95% CI, 86%-92%). The model was rated neutrally or positively in 86% to 92% of diabetic macular edema scans and 53% to 87% of age-related macular degeneration scans. Intraclass correlations ranged from 0.33 (95% CI, 0.08-0.96) to 0.96 (95% CI, 0.90-0.99). Dice similarity coefficients ranged from 0.43 (95% CI, 0.29-0.66) to 0.78 (95% CI, 0.57-0.85). CONCLUSIONS AND RELEVANCE: This deep learning-based segmentation tool provided clinically useful measures of retinal disease that would otherwise be infeasible to obtain. Qualitative evaluation was additionally important to reveal clinical applicability for both care management and research

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

PURPOSE: Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype–phenotype correlations. METHODS: International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. RESULTS: Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. CONCLUSIONS: This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance: Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies

Investigation of the viewing-angle compensation possibilities of π-cells

The aim of this paper is to clarify which kinds of retardation foils are most suited to extend the viewing angle of π-cells [1]. For this reason we focussed on the simulated conoscopic retardation plots of the π-cell on the one hand and of the retardation foils on the other hand. Among the foils we investigated are hybrid retardation layers with positive and negative birefringence, homogeneous biaxial retardation layers and compensation structures consisting of a Fuji Wide-View (FWV) foil and an extra retardation layer. We believe that our approach excludes the existence of other compensation foil candidates than the ones we proposed

Measurement and evaluation of head tracked auto-stereoscopic displays.

This paper describes objective and subjective display-related measurement methods to evaluate the performance of a laser illuminated head tracked auto-stereoscopic display. Essential characteristics are speckle, exit pupils and crosstalk. The described methods are used to evaluate a first prototype of an auto-stereoscopic display developed within the European Union-funded HELIUM3D project

Expression of genes involved in the embryo-maternal interaction in the early pregnant canine uterus

Although there is no acute luteolytic mechanism in the absence of pregnancy in the bitch a precise and well-timed embryo-maternal interaction seems to be required for the initiation and maintenance of gestation. Since only limited information is available about these processes in dogs, here, the uterine expression of possible decidualization markers was investigated during the pre-implantation stage (days 10-12) of pregnancy and in corresponding non-pregnant controls. Additionally, the expression of selected genes associated with blastocyst development and/or implantation was investigated in embryos flushed from the uteri used for this study (unhatched and hatched blastocysts). There was an upregulated expression of prolactin receptor (PRLr) and insulin-like growth factor 2 (IGF2) observed pre-implantation. The expression of PRL and of IGF1 was unaffected, and neither was the expression of progesterone- or estrogen receptor β (ERβ). In contrast, ERα-levels were elevated during early pregnancy. Prostaglandin (PG)-system revealed upregulated expression of PGE2-synthase and its receptors, EP2 (PTGER2) and EP4 (PTGER4), and of the PG-transporter. Elevated levels of PGF2α-synthase (PGFS/AKR1C3) mRNA, but not the protein itself, were noted. Expression of prostaglandin-endoperoxide synthase 2 (PTGS2,COX2) remained unaffected. Most of the transcripts were predominantly localized to the uterine epithelial cells, myometrium and, to a lesser extent, to the uterine stroma. PGES mRNA was abundantly expressed in both groups of embryos and appeared higher in the hatched ones. The expression level of IGF2 mRNA appeared higher than that of IGF1 mRNA in hatched embryos. In unhatched embryos IGF1, IGF2 and COX2 mRNA levels were below the detection limit