Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium.

Background: The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods: We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results: Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists' assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women's samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion: The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.

AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF).

Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138- cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138- and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36-0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion: Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders.

Artificial intelligence deep learning model assessment of leukocyte counts and proliferation in endometrium from women with and without polycystic ovary syndrome.

OBJECTIVE: To study whether artificial intelligence (AI) technology can be used to discern quantitative differences in endometrial immune cells between cycle phases and between samples from women with polycystic ovary syndrome (PCOS) and non-PCOS controls. Only a few studies have analyzed endometrial histology using AI technology, and especially, studies of the PCOS endometrium are lacking, partly because of the technically challenging analysis and unavailability of well-phenotyped samples. Novel AI technologies can overcome this problem. DESIGN: Case-control study. SETTING: University hospital-based research laboratory. PATIENT(S): Forty-eight women with PCOS and 43 controls. Proliferative phase samples (26 control and 23 PCOS) and luteinizing hormone (LH) surge timed LH+ 7-9 (10 control and 16 PCOS) and LH+ 10-12 (7 control and 9 PCOS) secretory endometrial samples were collected during 2014-2019. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Endometrial samples were stained with antibodies for CD8+ T cells, CD56+ uterine natural killer cells, CD68+ macrophages, and proliferation marker Ki67. Scanned whole slide images were analyzed with an AI deep learning model. Cycle phase differences in leukocyte counts, proliferation rate, and endometrial thickness were measured within the study populations and between the PCOS and control samples. A subanalysis of anovulatory PCOS samples (n = 11) vs. proliferative phase controls (n = 18) was also performed. RESULT(S): Automated cell counting with a deep learning model performs well for the human endometrium. The leukocyte numbers and proliferation in the endometrium fluctuate with the menstrual cycle. Differences in leukocyte counts were not observed between the whole PCOS population and controls. However, anovulatory women with PCOS presented with a higher number of CD68+ cells in the epithelium (controls vs. PCOS, median [interquartile range], 0.92 [0.75-1.51] vs. 1.97 [1.12-2.68]) and fewer leukocytes in the stroma (CD8%, 3.72 [2.18-4.20] vs. 1.44 [0.77-3.03]; CD56%, 6.36 [4.43-7.43] vs. 2.07 [0.65-4.99]; CD68%, 4.57 [3.92-5.70] vs. 3.07 [1.73-4.59], respectively) compared with the controls. The endometrial thickness and proliferation rate were comparable between the PCOS and control groups in all cycle phases. CONCLUSION(S): Artificial intelligence technology provides a powerful tool for endometrial research because it is objective and can efficiently analyze endometrial compartments separately. Ovulatory endometrium from women with PCOS did not differ remarkably from the controls, which may indicate that gaining ovulatory cycles normalizes the PCOS endometrium and enables normalization of leukocyte environment before implantation. Deviant endometrial leukocyte populations observed in anovulatory women with PCOS could be interrelated with the altered endometrial function observed in these women.

Estradiol Valerate in COC Has More Favorable Inflammatory Profile Than Synthetic Ethinyl Estradiol: A Randomized Trial.

CONTEXT: Combined oral contraceptives (COCs) alter inflammatory status and lipid metabolism. Whether different estrogens have different effects is poorly understood. OBJECTIVE: We compared the effects of COCs containing ethinyl estradiol (EE) or estradiol valerate (EV) and dienogest (DNG) with those containing DNG only on inflammation and lipid metabolism. DESIGN: Randomized, controlled, open-label clinical trial. SETTING: Two-center study in Helsinki and Oulu University Hospitals. PARTICIPANTS: Fifty-nine healthy, young, nonsmoking women with regular menstrual cycles. Age, body mass index, and waist-to-hip ratio were comparable in all study groups at the beginning. Fifty-six women completed the study (EV + DNG, n = 20; EE + DNG, n = 19; DNG only, n = 17). INTERVENTIONS: Nine-week continuous use of COCs containing either EV + DNG or EE + DNG, or DNG only as control. MAIN OUTCOME MEASURES: Parameters of chronic inflammation (high-sensitivity C-reactive protein [hs-CRP], and pentraxin 3 [PTX-3]) and lipid profile (high-density lipoprotein [HDL], low-density lipoprotein [LDL], triglycerides, and total cholesterol). RESULTS: Serum hs-CRP increased after 9-week use of EE + DNG (mean change ± standard deviation 1.10 ± 2.11 mg/L) compared with EV + DNG (-0.06 ± 0.97 mg/L, P = 0.001) or DNG only (0.13 ± 0.68 mg/L, P = 0.021). Also, PTX-3 increased in the EE + DNG group compared with EV + DNG and DNG-only groups (P = 0.017 and P = 0.003, respectively). In the EE + DNG group, HDL and triglycerides increased compared with other groups (HDL: EE + DNG 0.20 ± 0.24 mmol/L vs EV + DNG 0.02 ± 0.20 mmol/L [P = 0.002] vs DNG 0.02 ± 0.18 mmol/L [P = 0.002]; triglycerides: EE + DNG 0.45 ± 0.21 mmol/L vs EV + DNG 0.18 ± 0.36 mmol/L [P = 0.003] vs DNG 0.06 ± 0.18 mmol/L [P < 0.001]). CONCLUSIONS: EV + DNG and DNG only had a neutral effect on inflammation and lipids, while EE + DNG increased both hs-CRP and PTX-3 levels as well as triglycerides and HDL. TRIAL REGISTRATION: ClinicalTrials.gov NCT02352090.