An Overview on Diagnosis of Endometriosis Disease Based on Machine Learning Methods

Endometriosis is a complex gynecological condition characterized by the presence of endometrial-like tissue outside the uterus. Diagnosis and management of endometriosis are challenging due to diverse clinical manifestations and lack of definitive diagnostic methods. Medical imaging, particularly ultrasound and magnetic resonance imaging (MRI), has shown potential in visualizing and assessing endometriosis lesions. In this research paper, we review recent advances in machine learning algorithms applied to endometriosis diagnosis, focusing on the importance of training datasets in improving diagnostic accuracy. We analyze the contributions of various studies and discuss the challenges and future directions in this field.