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

Immunohistochemical analysis of CD138+ plasma cells has been applied for detecting endometrial inflammation, especially chronic endometritis (CE). In this study, we developed for the first time an artificial intelligence (AI) algorithm, AITAH, to identify CD138+ plasma cells within endometrial tissue, focusing on two infertility-related conditions: polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF). We obtained 193 endometrial tissues from healthy controls (n=73), women with PCOS (n=91), and RIF patients (n=29) and compared CD138+ cell percentages across cycle phases, ovulation status, and endometrial receptivity. We trained AITAH with CD138 stained tissue images, and experienced pathologists validated the training and performance of AITAH. AITAH, with high accuracy in detecting CD138+ cells (88.57%), revealed higher CD138+ cell percentages in the proliferative phase than in the secretory phase or in the anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the proliferative phase (p=0.01). Different receptivity statuses had no impact on the cell percentages in RIF samples. In summary, the AI-enabled analysis is a rapid and accurate tool to examine endometrial tissues, potentially aiding clinical decision-making. Here, the AI analysis demonstrated cycle-phase differences in CD138+ aggregations pattern, but no major alterations in PCOS or RIF samples. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded by the Academy of Finland, the Sigrid Juselius Foundation, Novo Nordisk Foundation, and the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant (MATER, grant no. 813707). This research was also funded by the Estonian Research Council (grant no.PRG1076), Horizon 2020 innovation grant (ERIN, grant no. EU952516), Enterprise Estonia (grant no. EU48695), and MSCA-RISE-2020 project (TRENDO, grant no. 101008193). The funders did not participate in any processes of the study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by The Regional Ethics Committee of the Northern Ostrobothnia Hospital District, Finland (65/2017), and informed consent was signed by all study subjects. The study was approved by the Research Ethics Committee of the University of Tartu, Estonia (340T-12) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets generated during and/or analysed during the current study are not publicly available due to sensitivity of the health data. Non-personal data can be requested from the corresponding author.