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

Abstract

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.