Systematic, noninvasive endometriosis diagnosis in transvaginal sonography by the Swiss Society of Ultrasound in Medicine.

We present a new systematic, comprehensive, checklist-based sonographic assessment of endometriosis in the female true pelvis. Emphasis is placed on practical skills teaching. The newly introduced White Sliding Line (WSL) is the core structure.

Sustained Physiological Stretch Induces Abdominal Skin Growth in Pregnancy.

Supraphysiological stretches are exploited in skin expanders to induce tissue growth for autologous implants. As pregnancy is associated with large levels of sustained stretch, we investigated whether skin growth occurs in pregnancy. Therefore, we combined a mechanical model of skin and the observations from suction experiments on several body locations of five pregnant women at different gestational ages.

Judging Urgency in 343 Ectopic Pregnancies Prior to Surgery - The Importance of Transvaginal Sonographic Diagnosis of Intraabdominal Free Blood.

Objectives: Assessing urgency in ectopic pregnancies (ECP) remains controversial since the disorder covers a large clinical spectrum. Severe conditions such as acute abdomen or hemodynamic instability are mostly related to intra-abdominal blood loss diagnosed as free fluid (FF) on transvaginal sonography (TVS). The aims of the current study were to investigate the value of FF and to assess other potentially predictive parameters for judging urgency.

Expert opinion on the use of transvaginal sonography for presurgical staging and classification of endometriosis.

Gynecological ultrasonography plays a central role in the management of endometriosis. The rapid technical development as well as the currently increasing evidence for non-invasive diagnostic methods require an updated compilation of recommendations for the use of ultrasound in the management of endometriosis. The present work aims to highlight the accuracy of sonography for diagnosing and classifying endometriosis and will formulate the present list of key messages and recommendations.

Self-supervised representation learning for surgical activity recognition.

Purpose: Virtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential, they must be able to automatically recognize activities performed by users and assess those. Since annotations of trajectories by human experts are expensive, there is a need for methods that can learn to recognize surgical activities in a data-efficient way.