Clinical use of artificial intelligence in endometriosis: a scoping review

Abstract

Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

Self-report symptom-based endometriosis prediction using machine learning

“…Endometriosis is a chronic gynecological condition that affects 5–10% of women of reproductive age 1 , 2 . Women with endometriosis have endometrial-type tissue outside of the uterus 1 , 3 . In exceptional cases, endometriosis lesions may reach organs distant from the pelvis such as the membranes of the lungs, heart, limbs, and brain.…”

“…Indeed, ML is promising because it facilitates the discovery of complex, non-linear relationships between a set of variables (such as patient characteristics or symptoms) and a target variable (such as the patient’s likelihood of having endometriosis). A recent review by Sivajohan et al 3 found 36 studies that applied ML in endometriosis prediction, diagnosis, and research. Only three of these studies 6 , 24 , 30 used self-report questionnaires to develop ML-based models for endometriosis prediction.…”

“…In recent years, machine learning (ML) has been used as a promising approach for patient classification, with excellent results in various medical fields [23][24][25][26][27] . ML has also been used for endometriosis prediction and diagnosis 3,14,15,24,28,29 . Indeed, ML is promising because it facilitates the discovery of complex, non-linear relationships between a set of variables (such as patient characteristics or symptoms) and a target variable (such as the patient's likelihood of having endometriosis).…”

Suffering in Silence. Population-Level Detection and Risk-Prediction of Women’s Reproductive Disease from Claims Data

“…AI methods have been used to develop models for infertility and to optimize IVF using genetic, imaging, or metabolic datasets (23). While these methods have high predictive power, these are considerably downstream, are not readily scalable, and are points where individuals have resigned themselves to invasive interventions.…”

Endometriosis in the era of precision medicine and impact on sexual and reproductive health across the lifespan and in diverse populations

“…A recent scoping review has highlighted artificial intelligence and machine learning algorithms to integrate complex metadata, omics data, diagnostic approaches, and therapeutic targets to improve endometriosis patient diagnosis, disease phenotyping, personalized therapies, prognostic indicators of responses to treatment, and risk of recurrence (Figure 7). 202 In this space, the future is now, and endometriosis warrants being in the front of the line to move this enigmatic disease forward for the benefit of those affected. We anticipate multidisciplinary approaches and leveraging clinical data across diverse patient cohorts will further inform endometriosis disease mechanisms underlying the known heterogeneous clinical manifestations and improve patient stratification and personalized clinical approaches to therapies.…”