Tag: Cardiovascular Diseases

Artificial intelligence bias in the prediction and detection of cardiovascular disease

AI can help identify people at risk of cardiovascular disease, detect it early and allow timely intervention. However, bias can arise at any step in the development, validation and use of these algorithms, causing them to perform poorly for historically marginalised groups. This article discusses the sources and consequences of AI bias in predicting and detecting cardiovascular disease, and presents strategies to reduce it.

Bias can come from training data that does not represent the population a tool is meant for, from measurements or devices that work less well for some groups, and from leaving out important factors such as social determinants of health. It can also reflect inequalities in care: because women and ethnic minority patients are more likely to have their cardiovascular disease missed or diagnosed late, algorithms trained on this data may be less accurate at detecting it in these groups. Unequal access to digital technologies can further limit how well some groups are represented in the data. The authors give several examples of AI models that performed worse for women, Black patients or older adults.

To reduce bias, the authors propose an AI health equity framework covering the whole lifecycle of an algorithm: diverse research teams, early involvement of patients, representative and balanced data, the inclusion of social determinants of health, input from clinicians, validation across different groups, transparent reporting of results and monitoring after implementation. They conclude that, without these measures, AI bias could worsen existing healthcare inequities and miss opportunities to predict and detect cardiovascular disease.

Learn more about this article here: https://doi.org/10.1038/s44325-024-00031-9

Reference:

Mihan, A., Pandey, A., & Van Spall, H.G.C. (2024). Artificial intelligence bias in the prediction and detection of cardiovascular disease. npj Cardiovascular Health, 1, 31.
Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases: A Systematic Review

Cardiovascular disease is a leading cause of death for both women and men, but there is growing evidence that women are not always diagnosed and treated in the same way. This systematic review analysed 19 studies to examine gender bias in the diagnosis, prevention and treatment of cardiovascular diseases.

The review found that women’s cardiovascular disease was less often recorded when their symptoms were milder than men’s, or when they were misdiagnosed as digestive or anxiety-related problems. Women were offered fewer diagnostic tests, such as electrocardiograms and coronary angiography, were less often referred to cardiologists or admitted to hospital, and, once in hospital, were less likely to receive coronary interventions. They were also prescribed cardiovascular medicines less often than men, except for medicines for high blood pressure and angina, and saw themselves as being at lower risk of cardiovascular disease.

The authors conclude that women receive fewer diagnostic tests and medicines than men, and link these differences to limited knowledge of how cardiovascular disease presents differently in women and men.

Learn more about this review here: https://doi.org/10.7759/cureus.54264

Reference:

Al Hamid, A., Beckett, R., Wilson, M., Jalal, Z., Cheema, E., Al-Jumeily, D., Coombs, T., Ralebitso-Senior, K., & Assi, S. (2024). Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases: A Systematic Review. Cureus, 16(2), e54264.
Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases

AI models are increasingly used in cardiovascular medicine, but there are concerns that they may not work equally well for people of different racial and ethnic backgrounds. This systematic review examined how accurate and clinically applicable AI models for diagnosing heart disease and predicting outcomes are across racial and ethnic groups.

Of 1,704 references screened, 11 studies were included, covering diagnosis, prognosis and the analysis of heart images. Nine of them (82%) found racial or ethnic bias, while two found no differences between groups. For example, an AI model predicting heart failure from electrocardiograms was less accurate for young Black patients than for young Asian or White patients, and AI tools analysing cardiac MRI scans were more accurate for White people than for people from minority ethnic groups.

The authors conclude that AI models can perform well for some groups and less accurately for others, and that bias should be addressed at every stage, from data collection to clinical use. They recommend methods to reduce bias, standardised ways of measuring fairness, considering social determinants of health rather than race alone, and greater transparency through the sharing of code and data.

Learn more about this review here: https://doi.org/10.1016/j.ejrad.2024.111867

Reference:

Cau, R., Pisu, F., Suri, J.S., & Saba, L. (2024). Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases. European Journal of Radiology, 111867.
Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity

AI has been seen as a tool that could help detect and reduce the racial and ethnic inequities that persist in cardiovascular care. However, AI can also carry and spread biases of its own, which means it must be approached with care. This review looks at the role of AI in cardiology in identifying and addressing these inequities, and in promoting diversity in research, as well as the concerns its use raises and strategies for using it fairly.

The authors describe how AI has already helped reduce some inequities. In one study, an AI tool identified patients with severe heart valve disease and alerted their doctors, improving follow-up care for patients of all races. Even so, Black patients still faced greater delays in receiving appropriate care than White patients, showing that AI is not a “silver bullet” for eliminating inequities. AI can also perpetuate existing biases, due to limited diversity in training data, biases within the datasets themselves, and inadequate mechanisms for detecting and monitoring bias. For example, an AI model trained mostly on data from White patients to predict heart failure from electrocardiograms performed worse for Black patients, and particularly for Black women.

To use AI in cardiovascular care in a way that addresses racial and ethnic inequities, the authors stress the need for careful design, starting with meticulous data collection and a thorough review of training data. They also call for rigorous testing and continuous monitoring of algorithms to ensure they perform equally well for all groups, as well as greater diversity in the AI workforce and the involvement of stakeholders.

Learn more about this review here: https://doi.org/10.1007/s12170-024-00745-6

Reference:

Amponsah, D., Thamman, R., Brandt, E., James, C., Spector-Bagdady, K., & Yong, C.M. (2024). Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity. Current Cardiovascular Risk Reports, 18(11), 153–162.
Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis

AI models developed using data from very similar populations may not work as well for other groups, and could reflect or even worsen existing racial and ethnic disparities in health care. This study tested whether this was the case for an AI model that uses electrocardiograms (ECGs) to detect a weakened heart pumping function, known as low left ventricular ejection fraction.

The model had been developed with data from nearly 45,000 patients, 96.2% of whom were non-Hispanic White. When tested on a separate group of almost 53,000 patients, it performed consistently well across all the racial and ethnic groups analysed: non-Hispanic White, Asian, Black/African American, Hispanic/Latino and American Indian/Native Alaskan. A separate AI model was able to identify patients’ racial group from the ECG to some extent, showing that ECG characteristics do vary by race, but this did not affect the first model’s performance.

The authors conclude that, in this case, racial differences in the ECG did not affect the model’s ability to detect the condition. Even so, they recommend that the performance of all new AI tools in medicine be reported across different ethnic, racial, age and sex groups, to ensure they are used responsibly.

Learn more about this study here: https://doi.org/10.1161/CIRCEP.119.007988

Reference:

Noseworthy, P.A., Attia, Z.I., Brewer, L.C., Hayes, S.N., Yao, X., Kapa, S., Friedman, P.A., & Lopez-Jimenez, F. (2020). Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Circulation: Arrhythmia and Electrophysiology, 13(3), e007988.
Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction

Machine learning models are increasingly used to estimate a person’s risk of cardiovascular disease, but bias can be introduced when health data is collected and prepared, affecting how well these models work for certain groups. This study used electronic health records from 109,490 people treated at Vanderbilt University Medical Center, in the US, to test whether several machine learning models performed equally well across race and gender. It also compared them with a widely used risk equation from the American Heart Association that does not use machine learning.

Most of the machine learning models were less biased than the American Heart Association equation. Differences between racial groups were small, but differences between women and men were considerably larger. According to the authors, both the risk equation and the machine learning models were biased against women.

The researchers also tested three ways of reducing this bias. Removing race and gender from the data did not significantly reduce bias in most models, and balancing the number of people from each group in the training data did not consistently help either. Balancing the proportion of people with cardiovascular disease in each group reduced the gender bias, but not the racial bias, and slightly reduced accuracy in many cases. The authors conclude that gender disparities in cardiovascular risk prediction need to be investigated and corrected.

Learn more about this study here: https://doi.org/10.1016/j.jbi.2023.104294

Reference:

Li, F., Wu, P., Ong, H.H., Peterson, J.F., Wei, W.-Q., & Zhao, J. (2023). Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction. Journal of Biomedical Informatics, 138, 104294.
Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

Stroke is a leading cause of death and serious long-term disability in the US, with a particularly high risk among Black individuals. Risk prediction tools help identify who would benefit most from prevention, but they need to work equally well for everyone. This study compared several of these tools, including two stroke-specific models, a general cardiovascular risk model and two new machine learning models, using data from 62,482 Black and White adults aged 45 and over from four large US studies.

Overall, the models performed similarly, and the new machine learning models did not improve their accuracy. However, all of them were worse at distinguishing between people who would and would not have a stroke when applied to Black individuals. Among women, the models’ accuracy score was 0.76 for White women and 0.69 for Black women. Among men, it ranged from 0.71 to 0.72 for White men and from 0.64 to 0.66 for Black men. The models also performed better in adults under 60 than in older adults.

The authors conclude that current approaches are not enough to close this gap, and that the range of risk factors considered and the modelling techniques used need to be improved to address these racial disparities.

Learn more about this study here: https://doi.org/10.1001/jama.2022.24683

Reference:

Hong, C., Pencina, M.J., Wojdyla, D.M., Hall, J.L., Judd, S.E., Cary, M., Engelhard, M.M., Berchuck, S., Xian, Y., D’Agostino, R., Howard, G., Kissela, B., & Henao, R. (2023). Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups. JAMA, 329(4), 306–317.
Age, sex and race bias in automated arrhythmia detectors

Algorithms that read electrocardiograms (ECGs) automatically are increasingly used to help identify heart conditions, but few studies have examined whether they perform equally well for different groups of patients. This study compared 56 open-source algorithms, developed by participants in the 2021 PhysioNet Challenge, on more than 130,000 ECGs, grouping the results by sex, age and race.

Several performance measures were similar across groups, but others showed clear differences. Accuracy was significantly lower for Black patients and higher for Asian patients, with a gap of 39% on the test set. Accuracy also fell with age, by around 12% per decade, and results for male and female patients differed depending on the measure used.

The researchers then retrained one of the best-performing models with an additional constraint designed to reduce performance differences across sex, race and age. This led to a small loss in overall performance but a significant reduction in bias. The authors conclude that bias depends on the model’s design, the population it is trained on and how its performance is measured, and that all of these should be closely examined.

Learn more about this study here: https://doi.org/10.1016/j.jelectrocard.2022.07.007

Reference:

Perez Alday, E.A., Rad, A.B., Reyna, M.A., Sadr, N., Gu, A., Li, Q., Dumitru, M., Xue, J., Albert, D., Sameni, R., & Clifford, G.D. (2022). Age, sex and race bias in automated arrhythmia detectors. Journal of Electrocardiology, 74, 5–9.
Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases

Electrocardiography (ECG) enhanced by artificial intelligence is emerging as a promising tool for diagnosing and managing cardiovascular disease. This review explains how AI can detect subtle patterns in the ECG that human interpreters may miss, helping to identify problems such as arrhythmias and heart failure more quickly and consistently.

However, the authors also highlight important limitations, including systematic bias. When training datasets do not adequately represent different ages, sexes and racial groups, models can perform worse for those who are underrepresented, since ECG patterns vary with age, physiological differences between the sexes and genetic factors. To make these tools safe and fair in clinical practice, the review calls for more diverse datasets, bias mitigation strategies and continuous validation across populations.

Learn more about this review here: https://doi.org/10.1016/j.jelectrocard.2024.01.006

Reference:

Muzammil, M.A., Javid, S., Afridi, A.K., Siddineni, R., Shahabi, M., Haseeb, M., Fariha, F., Kumar, S., Zaveri, S., & Nashwan, A.J. (2024). Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. Journal of Electrocardiology, 83, 30–4