Resumen
This article discusses the potential of deep learning (DL) models in aiding the diagnosis of endometrial pathologies through hysteroscopic images. While hysteroscopy with endometrial biopsy is currently the gold standard for diagnosis, it heavily relies on the expertise of gynecologists. The study aims to develop a DL model for automated detection and classification of endometrial pathologies. Conducted as a monocentric observational retrospective cohort study, it reviewed records and videos of hysteroscopies from patients with confirmed intrauterine lesions. The DL model was trained using these images, with or without incorporating clinical factors. Results indicate that while the DL model showed promising results, its diagnostic performance remained relatively low, even with the inclusion of clinical data. The best performance was achieved when clinical factors were included, with precision, recall, specificity, and F1 scores ranging from 80 to 90% for classification and 85 to 93% for identification tasks. Despite slight improvements in clinical data, further refinement of DL models is warranted for more accurate diagnosis of endometrial pathologies.