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Review
. 2023 Mar 1;13(5):936.
doi: 10.3390/diagnostics13050936.

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

Masaya Mihara  1 , Tadahiro Yasuo  2 , Kotaro Kitaya  1
Affiliations
Review

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

Masaya Mihara et al. Diagnostics (Basel). .

Abstract

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.

Keywords: chronic endometritis; computer-aided diagnosis; convolutional neural network; deep learning; fluid hysteroscopy.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schema of simplified architectures of XGBoost, Resnet18, and YOLOX.
Figure 2
Figure 2
Basic architecture of VGG16 CNN model.
Figure 2
Figure 2
Basic architecture of VGG16 CNN model.
See this image and copyright information in PMC

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