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

Kitaya, Kotaro; Yasuo, Tadahiro; Yamaguchi, Takeshi; Morita, Yuko; Hamazaki, Atsumi; Murayama, Shinji; Mihara, Takako; Mihara, Masaya

doi:10.1016/j.ejogrb.2024.04.026

Cited by 1 publication

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“…There are currently no deep learning models that focus on aiding the identification of HysteroCE. Using archival hysteroscopic images obtained from women with HistoCE (defined as ≥0.25 CD138(+) ESPCs per HPF [ 99 ]) on days 6–12 in the menstrual cycle, we aimed to construct a VGGnet-16-based CNN model for the automatic detection of micropolyposis [ 105 ]. The images were manually processed to retain the lesions of interest and eliminate the confusing sites to enhance the performance of the CNN model.…”

Section: Application Of Cnn Model To Hysterocementioning

confidence: 99%

Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models

Kitaya,

Yasuo,

Yamaguchi

2024

Medicina

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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, tubal factors, endometriosis, repeated implantation failure, and recurrent pregnancy loss. Diagnosis of CE has traditionally relied on endometrial biopsy and histopathologic/immunohistochemistrical detection of ESPCs. Endometrial biopsy, however, is a somewhat painful procedure for the subjects and does not allow us to grasp the whole picture of this mucosal tissue. Meanwhile, fluid hysteroscopy has been recently adopted as a less-invasive diagnostic modality for CE. We launched the ARCHIPELAGO (ARChival Hysteroscopic Image-based Prediction for histopathologic chronic Endometritis in infertile women using deep LeArninG mOdel) study to construct the hysteroscopic CE finding-based prediction tools for histopathologic CE. The development of these deep learning-based novel models and computer-aided detection/diagnosis systems potentially benefits infertile women suffering from this elusive disease.

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Section: Application Of Cnn Model To Hysterocementioning

confidence: 99%