A new artificial intelligence tool is helping clinicians better detect and diagnose the stage of a range of serious health conditions.
The Supervised Contrastive Ordinal Learning algorithm, developed by researchers at ECU, uses routine and non-invasive medical images such as bone density scans and ultrasounds not only for the early detection of diseases, but also to highlight disease-specific changes that help in staging and clinical interpretation.
It can be used to detected conditions including cardiovascular disease (CVD), diabetic eye complications, and cancer.
ECU researcher Dr Afsah Saleem said there was an urgent need for non-invasive technologies to assist with the detection of medical issues such as CVD and diabetic retinopathy (DR).
“These chronic diseases are often difficult to detect in the early stages because they lack obvious symptoms. Current diagnostic methods frequently rely on manual assessments of medical scans, which is a time-consuming, expensive, and subjective process,” he said.
Globally, CVD affects more than 640 million people and in Australia the disease is responsible for one in every four deaths.
Similarly DR, a leading cause of blindness, currently impacts more than 103 million adults worldwide, a number projected to rise to 160 million by 2045.
“Being a machine learning scientist and working in medical imaging, our aim is to prevent or delay permanent health losses from chronic diseases,” Dr Saleem said.
This AI algorithm has already been successfully applied across multiple medical domains.
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“Using this algorithm, we achieved 85% accuracy and 79% sensitivity in identifying Abdominal Aortic Calcification, an early indicator of CVD,” Dr Saleem added.
“We also obtained 87% accuracy and 84% sensitivity in diagnosing DR, and 91% accuracy in identifying different stages of breast cancer.”
ECU senior lecturer Dr Syed Zulqarnain Gilani, who is also part of the research group behind the AI system, said the innovative aspect of the developed algorithm lies in its ability to capture and learn the distinctive characteristics of both healthy and unhealthy individuals.
“Subsequently, the algorithm effectively differentiates these traits to identify individuals afflicted with disease with remarkable precision,” Dr Gilani said.
Dr Saleem will be presenting her research into DR at the Medical Image Computing and Computer Assisted Intervention Conference, in Korea, later this year.
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