AIMC Topic: Endometrium
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Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models.
Medicina (Kaunas, Lithuania)
Jun 12, 2024
Chronic endometritis (CE) is an inflammatory pathologic condition of the uterine mucosa characterized by unusual infiltration of CD138(+) endometrial stromal plasmacytes (ESPCs). CE is often identified in infertile women with unexplained etiology, tu...
Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment.
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
May 4, 2024
OBJECTIVE: To understand the association between chronic inflammation, cellular senescence, and immunological infiltration in endometriosis.
Construction of deep learning-based convolutional neural network model for automatic detection of fluid hysteroscopic endometrial micropolyps in infertile women with chronic endometritis.
European journal of obstetrics, gynecology, and reproductive biology
Apr 21, 2024
OBJECTIVE(S): Chronic endometritis (CE) is a localized mucosal inflammatory disorder associated with female infertility of unknown etiology, endometriosis, tubal factors, repeated implantation failure, and recurrent pregnancy loss, along with atypica...
Deep learning analysis of endometrial histology as a promising tool to predict the chance of pregnancy after frozen embryo transfers.
Journal of assisted reproduction and genetics
Mar 2, 2023
PURPOSE: Endometrial histology on hematoxylin and eosin (H&E)-stained preparations provides information associated with receptivity. However, traditional histological examination by Noyes' dating method is of limited value as it is prone to subjectiv...
Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows.
PloS one
Jan 28, 2022
The assessment of polymorphonuclear leukocyte (PMN) proportions (%) of endometrial samples is the hallmark for subclinical endometritis (SCE) diagnosis. Yet, a non-biased, automated diagnostic method for assessing PMN% in endometrial cytology slides ...
Artificial intelligence deep learning model assessment of leukocyte counts and proliferation in endometrium from women with and without polycystic ovary syndrome.
F&S science
Jan 22, 2022
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 con...
Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm.
Frontiers in endocrinology
Nov 2, 2021
fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factor...
Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Sep 15, 2021
The incidence of infertility is continuously increasing nearly all over the world in recent years, and novel methods for accurate assessment are of great need. Artificial Intelligence (AI) has gradually become an effective supplementary method for th...
HOX cluster and their cofactors showed an altered expression pattern in eutopic and ectopic endometriosis tissues.
Reproductive biology and endocrinology : RB&E
Sep 1, 2021
Endometriosis is major gynecological disease that affects over 10% of women worldwide and 30%-50% of these women have pelvic pain, abnormal uterine bleeding and infertility. The cause of endometriosis is unknown and there is no definite cure mainly b...
Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography.
Abdominal radiology (New York)
Jul 21, 2021
PURPOSE: In this study, we developed radiomic models that utilize a combination of imaging features and clinical variables to distinguish endometrial cancer (EC) from normal endometrium on routine computed tomography (CT).
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