LDL testing is central to modern preventive care, yet several commonly used estimation methods can understate LDL levels—especially when triglycerides are high. This systematic gap can delay or prevent the initiation of therapies designed to reduce the risk of heart attacks and strokes.

The study highlights the “stress test” nature of the lipid profile: low LDL coupled with elevated triglycerides is precisely where calculation errors become clinically consequential. Even differences of a few milligrams per deciliter may shift a patient’s treatment eligibility, including eligibility for potent lipid-lowering drugs such as PCSK9 inhibitors.

To develop the simplified model, researchers trained a machine-learning approach using data from 4.9 million U.S. children and adults from the Very Large Database of Lipids. The median LDL level in this representative cohort was 114 mg/dL, providing wide coverage of real-world values.

The team then compared the machine-learning equation’s LDL estimates against the original Martin–Hopkins results and against other reference approaches, including the Sampson–NIH and Friedewald equations. To evaluate performance, calculated LDL values were benchmarked against measurements obtained through ultracentrifugation, a laboratory gold standard.

Across the full dataset, the simplified model closely tracked the original equation, differing by just 0.5 mg/dL on average. In treatment-category classification, the Martin–Hopkins formulations correctly sorted 90% of samples, outperforming the competing equations, particularly for higher-risk individuals with low LDL ranges.

Validation extended beyond the initial training data: the work used more than 3.2 million samples to train the model and 1.6 million to test it, plus additional datasets—including a reference laboratory set and a clinical trial cohort from the FOURIER trial—to confirm agreement with ultracentrifugation-based measurements.

A key design goal was transparency and portability. Laboratories can implement the update by substituting the triglyceride component used in the Friedewald workflow into the Martin–Hopkins structure, enabling broad adoption without complex retooling.

The authors emphasize that the equation carries no patent restrictions and is intended to improve implementation of the forthcoming 2026 national dyslipidemia guideline, which recommends prioritizing the Martin–Hopkins calculation and setting LDL goals that vary by cardiovascular risk.

Subject of Research: People
Article Title: Development and Validation of a Simplified Martin/Hopkins Low-Density Lipoprotein Cholesterol Equation Using Machine Learning
News Publication Date: 15-Jul-2026
Web References: https://jamanetwork.com/journals/jama/fullarticle/1779534
References: FOURIER trial; Very Large Database of Lipids; ultracentrifugation-based LDL measurement
Image Credits: Not provided

Keywords: LDL cholesterol, Martin–Hopkins equation, machine learning, triglycerides, cardiovascular risk, PCSK9 inhibitors, lipid profiling, preventive cardiology

The research highlights the importance of regular mammography screenings, particularly among middle-aged and older women, as recommended by the U.S. Centers for Disease Control and Prevention. Approximately 40 million mammograms are conducted annually in the United States alone. While radiologists can observe breast arterial calcifications on mammogram images, the existing protocols do not typically include an analysis or report of these findings that could be crucial for cardiovascular risk assessments. Leveraging a novel AI image analysis technique, researchers have developed a method to automatically quantify these calcifications and translate their findings into a cardiovascular risk score for patients.

Dr. Theo Dapamede, the study’s lead author and a postdoctoral fellow at Emory University in Atlanta, emphasizes the potential impact of this innovation. He notes that utilizing mammogram screenings to simultaneously assess and identify cardiovascular disease is a significant advancement in preventive medicine. The study found that breast arterial calcification serves as a reliable predictor of cardiovascular disease, particularly in women under the age of 60. Early identification through this method could facilitate timely referrals to cardiologists, allowing for proactive risk management and treatment options.

Heart disease remains the leading cause of death among women in the United States, yet it often goes underdiagnosed, due in part to a lack of awareness of its prevalence in women. The researchers argue that AI-driven mammogram tools could potentially bridge this awareness gap by identifying early indicators of cardiovascular disease in women who might otherwise overlook their heart health during routine cancer screenings. This innovative approach represents a significant shift in the clinical utility of mammograms.

The presence of calcium in the arteries is often indicative of cardiovascular damage and is associated with early-stage heart disease and aging. Previous research has shown that women with arterial calcium buildup have a 51% increased risk of experiencing heart disease or stroke compared to those without such buildup. By employing a deep-learning AI model to analyze mammogram images, the researchers were able to segment the calcified vessels—visible as bright pixels in the X-rays—and assess the patient’s future cardiovascular event risk using extensive electronic health data.

The AI model distinguishes itself from earlier iterations due to its capacity for precise segmentation of calcified structures in mammogram images. Researchers utilized a significant dataset that encompassed the images and health records of over 56,000 patients who underwent mammograms at Emory Healthcare from 2013 to 2020. This comprehensive dataset allowed for rigorous training and validation of the AI model, strengthening its capability to recognize and evaluate arterial calcifications linked to cardiovascular risk factors.

One of the standout findings of the study is the model’s effectiveness in categorizing patients’ cardiovascular risk as low, moderate, or severe based on analyzed mammogram images. By calculating the likelihood of experiencing life-threatening cardiovascular events, including heart attacks, strokes, or heart failure over two- and five-year periods, the model demonstrated a clear correlation between the level of breast arterial calcification and the severity of potential cardiovascular outcomes. This is particularly pertinent for younger women, who might benefit significantly from early intervention strategies.

The data revealed that women showing severe breast arterial calcification—more than 40 mm²—experienced markedly lower five-year rates of survival without significant events compared to those with minimal calcification, defined as below 10 mm². Specifically, the study reported that 86.4% of women with high calcification levels survived five years post-assessment, in contrast to a remarkable 95.3% survival rate among those with low calcification. This disparity suggests that women with severe calcification may face approximately 2.8 times the risk of mortality within the same timeframe.

Collaboration between Emory Healthcare and Mayo Clinic was key in the development of this AI model, which has not yet been made available for clinical use. Should it receive further validation and clearance from the U.S. Food and Drug Administration, there is a promising prospect for its adoption in routine mammography screenings across healthcare systems. The researchers additionally express interest in applying similar AI techniques to identify markers for other conditions, such as peripheral artery disease and kidney disease, potentially enhancing the diagnostic capabilities of mammograms beyond their current scope.

The innovative findings presented in this study call for a reevaluation of how mammograms are utilized within the healthcare system. The original purpose of these screenings should expand beyond merely detecting cancer, evolving into tools that provide holistic insights into women’s overall health, particularly their cardiovascular well-being. As the integration of AI in healthcare continues to evolve, developments like this could reshape preventive frameworks, leading to improved health outcomes for women through earlier detection and intervention.

By unlocking the potential for simultaneous cancer and cardiovascular screening using mammograms, researchers are paving the way for a new era in women’s healthcare, ensuring that heart disease does not remain an overlooked threat. Tailoring interventions based on the findings from mammogram screenings could revolutionize preventive health strategies, ultimately saving lives and fostering a greater understanding among women about the critical importance of cardiovascular health.

As the results from this study disseminate through the medical community, the hope is that healthcare providers will recognize the value of harnessing advanced technologies like AI to enhance the efficacy of routine screenings. This endeavor underscores a commitment to improving patient care and outcomes by taking advantage of existing medical technologies to address multiple health concerns simultaneously.

The upcoming presentation of these findings at ACC.25 further amplifies the potential for discussion and knowledge sharing within the cardiovascular field, fostering collaboration and innovation in pursuit of enhanced healthcare solutions. The integration of AI into mammography could signal a paradigm shift that prompts practitioners to think beyond conventional treatment models, encouraging a more comprehensive approach to women’s health.

As the field continues to evolve, ongoing research and clinical trials will be crucial in validating these initial findings and exploring the broader implications of AI in various medical imaging contexts. The commitment to advancing these medical technologies offers hope for broader applications of AI-Assisted diagnosis and risk stratification in other areas of medicine, ultimately contributing to a more integrated and effective healthcare system.

Subject of Research: AI in Mammography for Cardiovascular Screening
Article Title: Advanced AI in Mammography: A Dual Function for Cancer and Cardiovascular Health
News Publication Date: March 31, 2025
Web References: (Not provided)
References: (Not provided)
Image Credits: (Not provided)

Keywords: Mammography, Health care, Cancer screening, Risk factors, Breast cancer, Cardiovascular disease, Heart disease, Disease prevention.