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Interpretable machine learning for endometriosis classification: a rule-based approach.

Endometriosis is a chronic gynecological disease characterized by the growth of endometrial-like tissue outside the uterus, leading to pelvic pain, infertility, and other major health complications. Though some studies have …

Published: June 10, 2026, midnight
Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.

Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive procedure marked by considerable inter-observer disagreement and diagnostic delays. Existing non-invasive approaches, …

Published: April 9, 2026, midnight
An ultrasound-based machine learning model for predicting pelvic adhesions: A SHAP-enhanced XGBoost approach.

This study is the first to develop and evaluate a machine learning (ML) model for predicting pelvic adhesions based on ultrasound features, utilizing the SHapley Additive Explanations (SHAP) framework for …

Published: Jan. 19, 2026, midnight
In Situ Characterization and Deep Profiling of Engineered Multispecific Nanoparticle Metabolite Coronas for Precise Serum Diagnostics.

Upon exposure to biofluids, engineered nanoparticles (NPs) spontaneously form reproducible biomolecular coronas via selective diverse biomolecule adsorption. The corona characterization of metabolites poses greater analytical challenges than proteins due to …

Published: Dec. 26, 2025, midnight
Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision.

Clinical limitations due to poverty significantly impact the lives and health of many individuals globally. Nevertheless, this challenge can be addressed with modern technologies, particularly through robotics and artificial intelligence. …

Published: Oct. 26, 2024, midnight
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