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

Estrogen receptor-alpha (ER-alpha) and defects in uterine receptivity in women

Endometriosis is a disorder that affects 5% of the normal population but is present in up to 40% of women with pelvic pain and/or infertility. Recent evidence suggests that the endometrium of women with endometriosis exhibits progesterone insensitivity. One endometrial protein that fluctuates in response to progesterone is the estrogen receptor-alpha (ER alpha), being down-regulated at the time of peak progesterone secretion during the window of implantation. Here we demonstrate that the biomarker of uterine receptivity, beta 3 integrin subunit, is reduced or absent in some women with endometriosis and that such defects are accompanied by inappropriate over-expression of ER alpha during the mid-secretory phase. Using a well-differentiated endometrial cell line we showed that the beta 3 integrin protein is negatively regulated by estrogen and positively regulated by epidermal growth factor (EGF). By competing against estrogen with various selective estrogen receptor modulators (SERMs) and estrogen receptor agonists and antagonists, inhibition of expression of the beta 3 integrin by estrogen can be mitigated. In conclusion, we hypothesize that certain types of uterine receptivity defects may be caused by the loss of appropriate ER alpha down-regulation in the mid-secretory phase, leading to defects in uterine receptivity. Such changes might be effectively treated by timely administration of the appropriate anti-estrogens to artificially block ER alpha and restore normal patterns of gene expression. Such treatments will require further clinical studies