Health, Canada (Commonwealth Union) – Breast cancer remains a significant global health concern, affecting millions of lives each year. However, with ongoing research and technological advancements, the landscape of breast cancer detection is evolving rapidly. The improvements in technology and AI have been forecasted to play a crucial role in the future diagnosis of breast cancer.
Researchers at the University of Waterloo are leading the way in developing a technique for early detection of breast cancer in women, ensuring timely access to life-saving treatments. This cutting-edge technology not only strives for greater accuracy but also boasts a more cost-effective approach compared to prevalent diagnostic tools like X-ray mammography, ultrasound, and magnetic resonance imaging (MRI). With test runs completed in just two minutes and energy consumption lower than that of a smartphone, this innovative method prioritizes efficiency. Additionally, it offers a safer alternative to X-rays, eliminating the risk of exposing patients to high levels of radiation that can potentially damage DNA and lead to cancer.
“We are coming very close to providing a method for breast cancer detection at an early stage that is inexpensive and harmless for women,” explained Dr. Omar Ramahi, the lead researcher as well as a professor in the University of Waterloo, Department of Electrical and Computer Engineering. “We’re trying to make a serious contribution to women’s health and create an alternative that is clinically and commercially viable.”
Researchers of the study pointed out that in Canada, breast cancer ranks as the second most prevalent cancer and the second leading cause of cancer-related death among women. Detecting a malignant tumor at an early stage significantly improves a woman’s chances of survival. Ramahi embarked on the exploration of novel methods for early-stage breast cancer detection in 2001. Over the past five years, his research has focused on investigating the potential of low-frequency electromagnetic waves.
Ramahi and his research team, composed of current and former Waterloo Engineering students, recently unveiled a groundbreaking discovery in the realm of healthcare. Their research has unveiled that very low electromagnetic frequencies exhibit a unique characteristic of traveling in straight lines, marking them as pioneers in this revelation. This newfound understanding has led to the development of an innovative diagnostic device that introduces a paradigm shift in global and individual healthcare, consistent with Waterloo’s tradition of disruptive technological advancements.
The diagnostic tool created by the team bears similarities to X-ray mammography but eliminates its drawbacks. Instead of relying on X-rays, the device emits low-frequency electromagnetic energy from an antenna, resembling those found in smartphones. As this energy permeates a patient’s breasts, it is captured by a metasurface or circuit board, consisting of pixels arranged end-to-end, with each pixel serving as a receiver.
The artificial intelligence (AI) system interprets the images generated by the circuit board, eliminating the need for human technicians to review results. This innovative technology excels in locating the size and position of tumors, even in breasts with dense tissue, addressing a limitation in current diagnostic systems.
Ramahi has successfully tested the system on breast phantoms, synthetic structures designed to replicate the characteristics of a human female’s breast. Encouraging results have prompted him to assert that their technology stands unrivaled in its field.
Moving forward, the researchers are committed to developing a system that, upon receiving approval from Health Canada, can undergo testing on human subjects. Seeking funding from various institutions and companies, Ramahi aims to create a cost-effective device accessible globally, envisioning it as a tool as commonplace as in-pharmacy blood pressure testing for women.
For further details, the research paper titled “Mammography using low-frequency electromagnetic fields with deep learning” offers comprehensive insights and has been published in Nature Scientific Reports.
