Ateneo-led team develops AI model for heart monitoring

MANILA, Philippines — An international team of scientists has developed an artificial intelligence (AI) model that accurately predicts cardiac index (CI), a measure of how effectively the heart pumps blood.

The team, led by Patricia Angela Abu of the Department of Information Systems and Computer Science at Ateneo de Manila University (ADMU), developed the model using physiological indicators such as heart rate, stroke volume index and cardiac output. The data are collected through noninvasive sensor stickers placed on patients’ skin.

“With a classification accuracy of 97.78%, the system demonstrates the potential for a simpler and more accessible approach to monitoring heart health,” the ADMU Research Communications Section said.

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The section said the model could offer an alternative to conventional monitoring procedures that require specialized hemodynamic analyzers, controlled clinical settings and specialized health care professionals.

Clinicians use CI “to evaluate heart function and guide treatment decisions,” the section said.

Cardiovascular disease is becoming increasingly common among people ages 20 to 29, a trend the World Health Organization attributed to rising rates of metabolic conditions such as obesity, hypertension, hyperlipidemia and diabetes among young populations worldwide.

Ischemic heart diseases were the leading cause of death in the Philippines last year, accounting for 100,958 deaths, or 19.8% of all deaths nationwide, according to data from the Philippine Statistics Authority.

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More than 1 in 10 Filipino adults, or 13%, had elevated blood pressure, the Department of Science and Technology’s Food and Nutrition Research Institute (DOST-FNRI) found in its 2023 National Nutrition Survey.

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About 27 million Filipinos are overweight or obese.

The International Diabetes Federation estimates that 4.7 million Filipino adults live with diabetes, up from 4 million in 2019. Studies have found that nearly 2.8 million more remain undiagnosed.

The ADMU Research Communications Section said advanced heart assessments that can aid early detection and treatment usually require high-tech diagnostic equipment and specialized care, which are often available only in major hospitals in large cities.

“This makes detailed yet noninvasive heart monitoring difficult, particularly for at-risk individuals who could benefit from such evaluations,” the section said, adding that the AI model could help overcome these barriers.

The section said the researchers use modern algorithms to analyze basic health data collected from noninvasive sensors, helping make advanced monitoring more practical and widely available even in health care settings that lack specialized equipment and expertise.

“These findings point to a future in which advanced cardiovascular assessment is no longer confined to the walls of large hospitals and specialized clinics,” it said. “They suggest that reliable cardiovascular assessment may not always require complex or resource-intensive procedures, as shown by the model’s strong performance with fewer inputs.”

The researchers published their study, “Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks,” in the April 2026 issue of the MDPI journal Bioengineering.

They plan to validate the approach in more diverse populations and are also exploring ways to further reduce the number of required measurements. /dm