To systematically evaluate the methodological quality and diagnostic performance of artificial intelligence (AI) applications, specifically machine learning (ML) and deep learning (DL), in the diagnosis of endometriosis through imaging and …
BackgroundDespite many women learning about endometriosis on social media, posts about the condition often fail to reflect current evidence. With the content and credibility of online health messages being found …
Endometriosis is defined as endometrial tissue outside of the uterus. It is a debilitating, complex, and underdiagnosed condition, impacting approximately 10%-15% of reproductive-aged women worldwide. Endometriosis is associated with chronic …
Early diagnosis of endometriosis is crucial, yet limited literature exists on factors influencing women's decisions to seek diagnosis. This study explores the role of symptoms, health beliefs, and social influences …
Endometriosis diagnosis is often limited by the resolution of conventional imaging techniques (ultrasound/non-targeted MRI) as well as the invasiveness and recurrence risks associated with laparoscopy. To overcome these challenges, we …
Artificial intelligence (AI) is revolutionizing how we practice medicine. In areas where we have traditionally struggled, such as diagnosing endometriosis, AI has significant potential to improve the breadth and accuracy …
Endometriosis affects 1 in 10 women of reproductive age and is often diagnosed after years of symptom onset. Although population-wide screening is not recommended in asymptomatic women, targeted imaging-based assessment …
To study whether incident endometriosis diagnosis, staging, and typology are associated with concurrent elevated serum inflammatory markers interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor alpha (TNF- α …
The authors of this commentary published two peer-reviewed online articles in 2020 and 2022 on the U.S. Agency for Healthcare Research and Quality (AHRQ) PSNet that were removed by the …