Construction of deep learning-based convolutional neural network model for automatic detection of fluid hysteroscopic endometrial micropolyps in infertile women with chronic endometritis.

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 atypical uterine bleeding and iron deficiency anemia. Diagnosis of CE has traditionally relied on endometrial biopsy and detection of CD138(+) endometrial stromal plasmacytes. To develop a less invasive diagnostic system for CE, we aimed to construct a deep learning-based convolutional neural network (CNN) model for the automatic detection of endometrial micropolyps (EMiP), a fluid hysteroscopy (F-HSC) finding recognized as tiny protrusive lesions that are closely related to this disease. STUDY DESIGN: This is an in silico study using archival images of F-HSC performed at an infertility center in a private clinic. A total of 244 infertile women undergoing F-HSC on the days 6-12 of the menstrual cycle between April 2019 and December 2021 with histopathologically-confirmed CE with the aid of immunohistochemistry for CD138 were utilized. RESULTS: The archival F-HSC images of 208 women (78 with EMiP and 130 without EMiP) who met the inclusion criteria were finally subjected to analysis. Following preprocessing of the images, half a set was input into a CNN architecture for training, whereas the remaining images were utilized as the test set to evaluate the performance of the model, which was compared with that of the experienced gynecologists. The sensitivity, specificity, accuracy, precision, and F1-score of the CNN model-aided diagnosis were 93.6 %, 92.3 %, 92.8 %, 88.0 %, and 0.907, respectively. The area under the receiver operating characteristic curves of the CNN model-aided diagnosis (0.930) was at a similar level (p > .05) to the value of conventional diagnosis by three experienced gynecologists (0.927, 0.948, and 0.906). CONCLUSION: These findings indicate that our deep learning-based CNN is capable of recognizing EMiP in F-HSC images and holds promise for further development of the computer-aided diagnostic system for CE.

Precision Medicine for Chronic Endometritis: Computer-Aided Diagnosis Using Deep Learning Model.

Chronic endometritis (CE) is a localized mucosal infectious and inflammatory disorder marked by infiltration of CD138(+) endometrial stromal plasmacytes (ESPC). CE is drawing interest in the field of reproductive medicine because of its association with female infertility of unknown etiology, endometriosis, repeated implantation failure, recurrent pregnancy loss, and multiple maternal/newborn complications. The diagnosis of CE has long relied on somewhat painful endometrial biopsy and histopathologic examinations combined with immunohistochemistry for CD138 (IHC-CD138). With IHC-CD138 only, CE may be potentially over-diagnosed by misidentification of endometrial epithelial cells, which constitutively express CD138, as ESPCs. Fluid hysteroscopy is emerging as an alternative, less-invasive diagnostic tool that can visualize the whole uterine cavity in real-time and enables the detection of several unique mucosal findings associated with CE. The biases in the hysteroscopic diagnosis of CE; however, are the inter-observer and intra-observer disagreements on the interpretation of the endoscopic findings. Additionally, due to the variances in the study designs and adopted diagnostic criteria, there exists some dissociation in the histopathologic and hysteroscopic diagnosis of CE among researchers. To address these questions, novel dual immunohistochemistry for CD138 and another plasmacyte marker multiple myeloma oncogene 1 are currently being tested. Furthermore, computer-aided diagnosis using a deep learning model is being developed for more accurate detection of ESPCs. These approaches have the potential to contribute to the reduction in human errors and biases, the improvement of the diagnostic performance of CE, and the establishment of unified diagnostic criteria and standardized clinical guidelines for the disease.

Progestins and estrogens and Alzheimer's disease.

Sex-specific incidence rates for Alzheimer's disease (AD) are higher in women than men. Many fundamental researches and some clinical investigations have reported therapeutic and preventive effects of estrogens on AD. But WHIMS [S.A. Shumaker, C. Legault, S.R. Rapp, L. Thal, R.B. Wallace, J.K. Ockene, S.L. Hendrix, B.N. Jones IIIrd, A.R. Assaf, R.D. Jackson, J.M. Kotchen, S. Wabertheil-Smoller, J. Wactawsk-Wende, WHIMS investigators, Estrogen plus progestin and the incidence of dementia and mild cognitive impairment in postmenopausal women. The women's health initiative memory study: a randomized controlled trial, JAMA 289 (2003) 2651-2662], which used daily continuous hormone replacement therapy (HRT), reported that the hazard ratio of the HRT for probable dementia was 2.05. Effect of progestins, and continuous (not cyclically) HRT, even only with estrogen should be reconsidered. In our clinical study, conjugated equine estrogen (CEE) alone showed good changes of psychiatric tests for AD on the 3rd week, but addition of medroxyprogesterone acetate (MPA) or norethindrone since 4th week suppressed these tests. Using human umbilical vein epithelial cell (HUVEC), levonorgestrel (LNG), norethindrone acetate (NETA), MPA increased intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1) and E-secretin but dienogest (DNG) showed no effect. In vitro flow system, estradiol (E2), suppressed adhesion of white cell, but LNG, NETA, MPA increased the adhesions. DNG showed less effect. Non-feminizing estrogen J 861, which has delta8,9 double bond and straight in its structure and has less effect on sexual organs. J 861 has shown ameliorative effects on central nervous system (CNS) (increasing of cholineacetyltransferase immunoreactive cells in substantia innominata (SI), etc.) like E2. More investigations about progestins and estrogens and AD should be done.

Estrogen and non-feminizing estrogen for Alzheimer's disease.

The preventive effect of estrogen on Alzheimer's disease (AD) has become clearer with many epidemiological reports. However, the therapeutic effects of estrogen have been controversial until now. In our trials, estrogen treatment showed a beneficial therapeutic effect for women with mild to moderate AD. Improvement of cognitive function was recognized during the third week from the beginning of administration and maintained as long as estrogen treatment continued. The longer the duration of HRT, the more HRT is useful for the prevention and therapy of AD. However, in most cases, administration of estrogen is discontinued because of the adverse effects on the uterus and breast. J 861 is a derivative of estradiol-17alpha, which has little effect on the sexual organs. The effects of estradiol-17beta (E2) and J 861 on neuronal function and vascular factors were investigated. J 861 was suggested to prevent both the intracellular calcium increase and peroxidation induced by amyloid beta (Abeta), more effectively than E2. The effect of J 861 may be related with both the direct non-genomic and the ER-mediated systems. J 861 showed neurotrophic effects like E2. J 861 inhibited the adhesion of monocytes to vascular endothelium, more effectively than E2. Also, J 861 suppressed the expression of adhesive factors, such as E-selectin and intercellular cell adhesion molecule-1 (ICAM-1), more effectively than E2.