New machine learning models to boost the diagnosis of women’s heart disease
Cardiovascular disease in women often goes underdiagnosed compared to men, but a new study suggests that machine learning models using sex-specific criteria could help change that. According to researchers, utilizing these new models could not only improve the diagnosis of heart disease in women but also enhance treatment outcomes.
One of the key issues highlighted in the study is that the current diagnostic criteria for certain heart diseases are the same for both men and women, despite the anatomical differences between male and female hearts. Women typically have smaller hearts with thinner walls, meaning that their hearts must work disproportionately harder than men’s before meeting the same risk criteria.
The researchers found that this sex-neutral approach to diagnosing heart disease leads to significant underdiagnosis in women. Conditions such as first-degree atrioventricular block and dilated cardiomyopathy are particularly affected, with women being diagnosed less frequently than men.
To address this disparity, the researchers developed new heart risk models that incorporate sex-specific criteria. By including additional metrics like cardiac magnetic resonance imaging, pulse wave analysis, electrocardiograms (EKGs), and carotid ultrasounds, the new models aim to provide more accurate assessments of cardiovascular health.
Among the various metrics tested, EKGs were identified as the most effective tool for detecting cardiovascular disease in both men and women. The researchers recommend a two-step screening process for higher-risk patients, starting with a survey of traditional risk factors followed by a more in-depth assessment using EKGs.
Overall, the study underscores the importance of considering sex-specific criteria in diagnosing heart disease, especially in women. By leveraging machine learning models and advanced metrics, healthcare professionals can improve the accuracy of cardiovascular assessments and enhance treatment outcomes for all patients.
