The University of South Australia is conducting the world’s first study that will use artificial intelligence to identify the risks of the most lethal ovarian cancer in women globally, permitting it to be discovered and treated sooner.

The study’s funding is granted by the Federal Government’s Medical Research Future Fund (MRFF). The Medical Research Future Fund is a $20 billion long-term investment in Australia for medical and health research.

The federal government has granted $1.2 million to Professor Elina Hypponen, a world-famous nutritional epidemiologist, and a group from UniSA’s Australian Centre for Precision Health and SAHMRI to explore the genetic and physical hazards of ovarian cancer using the health records of 273,000 women from the UK Biobank data.

The UK Biobank is a large-scale biomedical record that provides detailed genetic and health data from 500,000 UK individuals from 2010 to 2016. It is available to approved experts worldwide conducting critical research into the most widespread and life-threatening diseases.

According to research by Annals of Oncology, ovarian cancer is the eighth most widespread cancer in women globally, with approximately 300,000 new cases diagnosed in 2018. 

According to Cancer Australia, 1532 women were diagnosed with ovarian cancer in 2020, while 1068 died from the condition in the same year. That corresponds to three women dying from ovarian cancer in Australia every day. Sixty percent of people diagnosed are over the age of 60, with women accounting for 33% of those diagnosed between 40 and 59.

This data implies that it is necessary to find ways on how to overcome Ovarian Cancer risks. Hence, the need for this study is justifiable. 

A machine learning model that analyses data automatically to find risk factors is expected to effectively forecast which women will develop ovarian cancer in the next 15 years.

Due to uncertain symptoms and few recognized causes, ovarian cancer is typically discovered late, with a five-year survival rate of less than 30% for women with advanced-stage cancer.

Genes, diet, and lifestyle all play a role in detecting ovarian cancer. The researchers believe that this computational approach will help them identify those women most vulnerable to the disease.

Prof Hypponen said:

“With an early diagnosis, we can notably improve survival rates from ovarian cancer” and “If we can identify women who are at greatest risk, we can triage them for more intense screening, improving early detection and prognosis.”

This four-year initiative will determine which factors increase or decrease the risk of ovarian cancer, emphasizing metabolomics, the tiny molecules engaged in the breakdown of fats for energy.

Scientists think that alterations in lipid metabolism are indicators for ovarian cancer, and they plan to investigate hormones and blood indicators to forecast the risks better.

Professor Elina Hypponen hints that some research also suggests that they might be able to reduce the risk of ovarian cancer through diet.

UniSA’s first AI study will help medical staff guide women on overcoming ovarian cancer hazards. At least the study will help to reduce the mortality rate of women with ovarian cancer.