Reducing Bias in AI Models

AI SYSTEMS ARE most robust when they are built on broad data sets drawn from a diverse array of patients. A Cedars-Sinai team published a study in the European Journal of Nuclear Medicine and Molecular Imaging that describes how to train an AI system to perform well in all applicable populations—not just the specific population the system was built on.

Some AI systems are trained using high-risk patients, which can cause overestimation of disease probability. To ensure that the AI model works accurately for all patients and to reduce bias, Piotr Slomka, PhD, and his team trained their AI system using simulated variations of patients to scan images to predict heart disease.

The team found that models trained with a balanced mix of cases more accurately predicted the prob-

A Better Path to Cardiac Disease Prediction

ability of coronary artery disease in women and low-risk patients, which can potentially lead to less invasive testing and more accurate diagnosis.

The models also led to fewer false positives, suggesting that the system may reduce the number of tests the patient undergoes to rule out the disease.

“The results suggest that enhancing training data is critical to ensuring that AI predictions more closely reflect the population that they will be applied to in the future,” says Dr. Slomka, director of Innovation in Imaging at Cedars-Sinai and a research scientist in the Division of Artificial intelligence in Medicine and the Smidt Heart Institute.

Predicting and diagnosing heart conditions can be greatly improved by applying artificial intelligence tools, according to rigorous Cedars-Sinai studies.

In the first blinded randomized clinical trial of artificial intelligence in cardiology, Smidt Heart Institute and Artificial Intelligence in Medicine researchers led by cardiologist David Ouyang, MD, found that AI is more successful in assessing cardiac function than echocardiogram assessments made by sonographers.

Investigators led by Damini Dey, PhD, professor of Biomedical Sciences, developed an AI-based tool that measures plaque buildup in the coronary arteries from a standard CT test. They also matched results with images taken by two invasive tests considered to be highly accurate in assessing coronary artery plaque and narrowing: intravascular ultrasound and catheter-based coronary angiography. The investigators discovered that measurements made by the AI algorithm from CTA images accurately predicted heart attack risk within five years.

Sumeet Chugh, MD, has spent much of his career studying the most lethal of heart disease problems: sudden cardiac arrest. Dr. Chugh—director of the Center for Cardiac Arrest Prevention, the Pauline and Harold Price Chair in Cardiac Electrophysiology Research, and director of Artificial Intelligence in Medicine—and his team working in the community for more than two decades discovered a novel scoring system that sums up a person’s risk of ventricular fibrillation. They have now embarked on the Observational Study of Cardiac Arrest Risk, or OSCAR, that will study nearly 400,000 Los Angeles County residents. These large collections of clinical data are being analyzed with AI tools to validate and improve the ability to predict who is at risk of a fatal cardiac arrest.

“By the time someone collapses from cardiac arrest and 911 has been dialed, it is too late for 90% of people,” Dr. Chugh says. “The way we predict and prevent cardiac arrest now is not sustainable. AI can help us build a better prediction model that will quickly get interventions to the people who really need them and save lives.”