“I wanted to know: can we collect more data to control water quality to further protect public health? And is there a business case for cities to adopt smarter water monitoring technology?” asks the Assistant Professor with CVIA School of Engineering. “I think the answer is yes.”

Dr. Peleato’s questions ultimately evolved into a partnership with fellow School of Engineering Assistant Professor Dr. Anas Chaaban, the Natural Sciences and Engineering Research Council of Canada, national communications technology company TELUS and the Regional District of North Okanagan. Their collective goal is to leverage advances in wireless technology and sensors to better understand the quality of water closer to people’s taps—a unique research endeavour in the traditionally conservative field of water quality monitoring.

Dr. Peleato and Dr. Chaaban are proposing the use of economically feasible sensors throughout the water distribution system, leading from the treatment plant to the final point of distribution; the sensors will send data instantaneously through TELUS’ 5G network. “Most big cities have 5G,” says Dr. Chaaban. “It’s special because it enables massive machine-type communication; it not only connects people but also machines, sensors in our cars and the water distribution system, transportation, traffic lights, electricity—the list goes on and on. One house alone could have hundreds of sensors for different applications, so we need to be able to connect everything and transmit that data quickly to a usable platform.”

The study, recently published in Modern Pathology, builds on the understanding that ovarian cancer is not a single disease, but several distinct subtypes, called histotypes.

Dr. Bashashati and his team compared ovarian cancer disease classifications made by an AI machine learning-based model against those of a team of expert gynecologic pathologists who specialize in the diagnosis of female reproductive cancers.

Using a cohort of 948 ovarian cancer tissue specimens from Vancouver General Hospital, Dr. Bashashati’s team developed a series of AI computer algorithms that can identify four histotypes of ovarian cancer with a high degree of accuracy.