Concerned about the ability of artificial intelligence models trained on data from urban demographics to make the right medical diagnoses for rural populations, West Virginia University computer scientists have developed several AI models that can identify signs of heart failure in patients from Appalachia.

Prashnna Gyawali, assistant professor in the Lane Department of Computer Science and Electrical Engineering at the WVU Benjamin M. Statler College of Engineering and Mineral Resources, said heart failure—a chronic, persistent condition in which the heart cannot pump enough blood to meet the body’s need for oxygen—is one of the most pressing national and global health issues, and one that hits rural regions of the U.S. especially hard.

Despite the outsized impact of heart failure on rural populations, AI models are currently being trained to diagnose the disease using data representing patients from urban and suburban areas like Stanford, California, Gyawali said.

“Imagine Jane Doe, a 62-year-old woman living in a rural Appalachian community,” he suggested. “She has limited access to specialty care, relies on a small local clinic, and her lifestyle, diet and health history reflect the realities of her environment: high physical labor, minimal preventive care, and increased exposure to environmental risk factors like coal dust or poor air quality. Jane begins to experience fatigue and shortness of breath—symptoms that could point to heart failure.

“An AI system, trained primarily on data from urban hospitals in more affluent, coastal areas, evaluates Jane’s lab results. But because the system was not trained on patients who share Jane’s socioeconomic and environmental context, it fails to recognize her condition as urgent or abnormal,” Gyawali said. “This is why this work matters. By training AI models on data from West Virginia patients, we aim to ensure people like Jane receive accurate diagnoses, no matter where they live or how their lives differ from national averages.”

The researchers identified the AI models that were most accurate at diagnosing heart failure in an anonymized sample of more than 55,000 patients who received medical care in West Virginia. They also pinpointed the exact parameters for providing the AI models with data that most enhanced diagnostic accuracy. The findings appear in Scientific Reports, a Nature portfolio journal.

Doctoral student Alina Devkota emphasized they trained the AI models to work from patients’ electrocardiogram results, rather than the echocardiogram readings typical for patient data from urban areas.

Electrocardiograms rely on round electrodes stuck to the patient’s torso to record electrical signals from the heart. According to Devkota, they don’t require specialized equipment or specialized training to operate, but they still provide valuable insights into heart function.

“One of the criteria to diagnose heart failure is by measuring the ‘ejection fraction,’ or how much blood is pumped out of the heart with every beat, and the gold standard for doing that is with echocardiography, which uses sound waves to create images of the heart and the blood flowing through its valves,” she said.

“But echocardiography is expensive, time-consuming and often unavailable to patients in the very same rural Appalachian states that have the highest prevalence of heart failure across the nation. West Virginia, for example, ranks first in the U.S. for the prevalence of heart attack and coronary heart disease, but many West Virginians don’t have local access to high-tech echocardiograms. They do have access to inexpensive electrocardiograms, so we tested whether AI models could use electrocardiogram readings to predict a patient’s ejection fraction.”

Devkota, Gyawali and their colleagues trained several AI models on patient records from 28 hospitals across West Virginia. The AI models used either “deep learning,” which relies on multilayered neural networks, or “non-deep learning,” which relies on simpler algorithms, to analyze the patient records and draw conclusions.

The researchers found the deep-learning models, particularly one called ResNet, did best at correctly predicting a patient’s ejection fraction based on data from 12-lead electrocardiograms, with the results suggesting that a larger dataset for training would yield even better results. They also found that providing the AI models with specific “leads,” or combinations of data from different electrode pairs, affected how accurate the models’ ejection fraction predictions were.

Gyawali said while AI models are not yet being used in clinical practice due to reliability concerns, training an AI to successfully estimate ejection fraction from electrocardiogram signals could soon give clinicians an edge in protecting patients’ cardiac health.

“Heart failure affects more than six million Americans today, and factors like our aging population mean the risk is growing rapidly—approximately 1 in 4 people alive today will experience heart failure during their lifetimes. The prevalence is even higher in rural Appalachia, so it’s critical the people here do not continue to be overlooked.”

Additional WVU contributors to the research included Rukesh Prajapati, graduate research assistant; Amr El-Wakeel, assistant professor; Donald Adjeroh, professor and chair for computer science; and Brijesh Patel, assistant professor in the WVU Health Sciences School of Medicine.