Artificial Intelligence tool forecasts potential heart risks in women via mammography exams
In a groundbreaking development, scientists in Australia have created an artificial intelligence (AI) tool that can predict women's heart health risks by analysing their mammograms. The tool, developed by researchers from Australian universities and The George Institute for Global Health, a renowned medical research institute, has the potential to revolutionise women's healthcare.
The AI tool, which was designed and tested using data from over 49,000 women, was published in the journal Heart on Wednesday. According to the researchers, the tool performs just as well as other models that require more patient data.
Dr. Jennifer Barraclough, one of the study's authors, suggests that the AI tool could help detect women's heart health risks in areas with uneven access to medical care. Barraclough cites rural Australia as an example where mobile mammography units offer free breast cancer screenings, making the AI tool potentially useful.
The AI tool uses women's ages and mammogram images to predict their risk of being hospitalized or dying over the next 10 years due to heart failure or another cardiac problem. The tool does not require additional history taking or medical record data, making it less resource-intensive to implement.
Clare Arnott, global director of The George Institute's cardiovascular programme, emphasises the key advantage of the model as its ability to accurately predict heart health risks without requiring additional data. Arnott also points out that it's a common misconception that heart disease predominantly affects men, resulting in underdiagnosis and undertreatment of the condition in women.
The new AI tool, when used in combination with breast cancer screenings, has the potential to identify and potentially prevent two major causes of illness and death. Many women are not aware of or offered screenings to identify their heart disease risks, despite the fact that globally, cardiovascular diseases are responsible for about 35% of deaths among women.
Researchers plan to test the model in additional, diverse populations to understand potential barriers to its implementation. The hope is that this tool will help bridge the gap in heart health screening and treatment for women worldwide.
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