Evaluation of the combination of magnetic resonance imaging and artificial intelligence in the diagnosis of endometriosis: a cross-sectional study

Abstract

BACKGROUND Endometriosis is an inflammatory oestrogen-dependent condition characterized by the presence of endometrial tissue outside the uterus. It affects women during childbearing age and has an association with pelvic pain and infertility. Despite a range of symptoms, diagnosis of endometriosis is often delayed due to a lack of non-invasive, definitive tools for the diagnosis of endometriosis. Traditionally, endometriosis was diagnosed by an exploratory laparoscopy with posterior histology analysis. Nowadays new diagnostic strategies are arising such as Transvaginal ultrasound (TVUS) due to its availability and low cost. Although ultrasound can diagnose most locations, its limited sensitivity for posterior lesions does not allow management decisions in all patients. MRI has shown high accuracies for anterior and posterior pelvic endometriosis and enables complete lesion mapping before surgery. Moreover, adding an artificial intelligence (AI) analysis system to the MRI description can provide the expert review that lacks in tertiary centres or other centres where there are no specialised radiologists in endometriosis. OBJECTIVE: To evaluate the accuracy of artificial intelligence analysis combined with MRI in comparison with the Gold Standard technique, exploratory laparoscopy, in the diagnosis and staging of endometriosis. METHODS: This protocol study is designed as a cross-sectional retrospective study. Pre-surgical MRIs of 30 female patients who have been diagnosed with endometriosis by laparoscopy in Girona during 2005 -2021 will be re-analysed by an AI analysis system in order to compare the AI + MRI with the Gold Standard, diagnostic laparoscopy