EndoAI diagnostics revolutionizing early detection and diagnostics of endometriosis

Abstract

Endometriosis is a chronic and debilitating gynaecological disorder affecting approximately 10% of women worldwide. Characterised by the abnormal growth of endometrial‐like tissue outside the uterus, the condition often leads to severe physical pain, emotional distress, and mental health challenges, significantly reducing patients’ quality of life. Despite its high prevalence, diagnosing endometriosis remains a major clinical challenge due to the heterogeneous nature of its symptoms, frequent misdiagnoses, and reliance on invasive procedures to confirm the diagnosis. Consequently, the average diagnostic delay extends up to eight years. This dissertation proposes a four‐stage solution that addresses these challenges by investigating the potential of Artificial Intelligence (AI) techniques to facilitate the early and accurate diagnosis of endometriosis. Specifically, the study develops a multi‐model AI‐driven diagnostic framework that leverages both self‐reported patient symptom data and laparoscopic medical images. Six Machine Learning (ML) algorithms were employed to predict the likelihood of endometriosis based on symptomatology, incorporating feature engineering techniques to optimise model performance. Additionally, eleven Deep Learning (DL) architectures underwent transfer learning to enhance the detection of endometrial lesions from laparoscopic images. The effectiveness of the proposed models was evaluated through a comparative analysis using key performance metrics, such as accuracy, precision, and recall. The results demonstrated that AI‐powered diagnostic tools significantly enhance the identification of endometriosis, with feature selection and hyperparameter tuning playing a crucial role in improving predictive accuracy. This study further identified high‐performing ML and DL models with strong clinical applicability, as well as key symptom‐based features essential for detecting the disease. These findings highlight the transformative potential of AI in medical diagnostics, particularly in addressing the persistent diagnostic delays associated with endometriosis. By integrating AI‐driven methodologies into clinical workflows, healthcare professionals can improve early detection rates, minimise misdiagnoses, and ultimately enhance patient outcomes. Furthermore, this study underscores the feasibility of a self‐diagnostic tool capable of predicting the likelihood of endometriosis, thereby increasing public awareness of the condition and empowering individuals to seek timely medical consultations. This research contributes to the advancement of AI in gynaecological healthcare, offering a pathway toward more efficient, accessible, and reliable diagnostic solutions for endometriosis.