190 Million People Suffer from This Mysterious Disease, Doctors Still Struggle to Diagnose It, But This New Tool Could Change That

Endometriosis remains one of the most painful and poorly understood chronic conditions, often described by patients as a relentless internal assault. It can lead to infertility, severe pelvic pain, and significant disruption to daily life, yet diagnosis is frequently delayed or missed altogether.

The condition affects roughly 10% of people with a uterus during reproductive years, and according to Popular Mechanics, about 190 million people globally live with some form of endometriosis. Among them, a smaller subset suffers from deep infiltrating endometriosis, a more severe form that spreads beyond the uterus and can bind organs together through adhesions.

A Disease That Hides in Plain Sight

Endometriosis occurs when tissue similar to the uterine lining grows outside the uterus, triggering inflammation and intense pain. In its deep infiltrating form, this tissue extends into surrounding organs such as the bowel or bladder, sometimes causing them to stick together.

Despite its severity, diagnosis remains a major challenge. Imaging tools like MRIs, CT scans, and ultrasounds are commonly used, yet few clinicians are adequately trained to interpret these results for endometriosis. According to Popular Mechanics, this gap in expertise has made invasive exploratory surgery the long-standing gold standard for diagnosis.

This reality forces patients into a difficult position, choosing between undergoing surgery or continuing without clear answers.

A Simulator Designed to Train the Eye and the Hand

Swedish company Surgical Science has introduced an ultrasound-based simulation module aimed at addressing this training gap. The system allows clinicians to practice identifying endometriosis using real ultrasound images, without requiring a patient to undergo examination.

The module focuses on a technique known as the “sliding sign.” By moving an ultrasound probe across pelvic organs, clinicians can detect whether tissues glide smoothly or encounter resistance. Smooth movement suggests the absence of adhesions, while stiffness may indicate their presence.

According to Surgical Science, inconsistent training standards and limited access to structured education have long slowed progress in diagnosis. The simulator aims to standardize learning and accelerate skill development, offering repeated practice in a controlled environment.

Measurable Gains but Not a Complete Solution

Early testing of the simulation module has produced notable results. 92% of clinicians trained with the system were able to recognize signs of deep infiltrating endometriosis using ultrasound. Confidence levels in identifying the condition increased by 150%.

Still, the technology has limitations. Ultrasound imaging lacks the resolution of MRI or CT scans and cannot detect inflamed lesions that are not linked to adhesions. Some affected areas may also remain inaccessible to the probe.

Even so, the improvement is significant. While many patients with deep infiltrating endometriosis will still require surgery to remove adhesions, better diagnostic training could reduce the number of unnecessary exploratory procedures.

Toward Earlier Answers for Millions of Patients

For most patients, endometriosis presents in a less severe, superficial form that may be managed without surgery, often through hormonal treatments. Yet the current reliance on surgical diagnosis complicates care decisions and delays relief.

According to statements , Surgical Science believes simulation will play a central role in future care by embedding ultrasound training into medical education. The goal is to shorten diagnostic delays and improve outcomes for patients who often wait years for answers.