How AI Is Transforming Detection of Congenital Heart Defects

Congenital heart defects (CHDs) are among the most common birth defects, affecting nearly 1 in 100 babies born in the U.S. Yet despite advances in prenatal imaging, CHD remains one of the most frequently missed diagnoses in obstetric care. As Congenital Heart Defect Awareness Week highlights this challenge, AI–enabled ultrasound is emerging as a powerful tool to combat the statistic by improving early detection and reducing care variation.

What early detection really means

Early diagnosis of a congenital heart defect is often misunderstood. Some assume prenatal screening is primarily about identifying pregnancies that may not continue, or parents don’t want to continue. In reality, the goal of early diagnosis is preparation to improve pregnancy and newborn outcomes.

A prenatal diagnosis allows clinicians to plan delivery at an appropriately equipped hospital, ensure pediatric specialists are available, and determine whether a newborn will need monitoring, medical therapy, or immediate intervention. In some cases, follow-up may not be needed for months. In others, surgery is required shortly after birth. Knowing this in advance helps avoid dangerous delays in care.

There are also significant benefits for families. Learning about a heart defect before birth gives parents time to process the diagnosis, learn about it, arrange childcare for siblings, plan work leave, and navigate insurance considerations. Managing these realities ahead of time is far less stressful than confronting them unexpectedly right after birth with a critically ill newborn. As in many areas of medicine, earlier recognition allows delivery planning and appropriate NICU preparedness, resulting in safer, more efficient care. Preventing emergencies benefits not only families, but also healthcare systems managing limited resources.

Why CHD is so hard to detect

Diagnosing fetal heart defects is technically challenging. Unlike imaging organs or bones that are still, to evaluate the fetal heart clinicians must evaluate a tiny, constantly beating heart inside a moving fetus that is inside the mother. Accurate interpretation requires extensive training — years beyond standard OB-GYN residency.

As a result, only a small subset of doctors, primarily maternal-fetal medicine specialists and pediatric cardiologists, are equipped to perform these assessments reliably. Most pregnancies, however, are cared for in community settings without routine access to that level of expertise.

Access gaps and the role of AI

The uneven distribution of trained specialists creates wide variation in detection rates, particularly in rural and underserved areas. While some families live near equipped medical centers, many must travel long distances or rely on providers with limited fetal cardiac imaging experience. AI-enabled ultrasound offers a bridge over that gap. Rather than replacing clinicians, AI augments their capabilities, analyzing cardiac images in real time, confirming whether views are adequate, and flagging images that may require further evaluation.

Even under ideal circumstances, studies suggest that 20% to 50% of CHDs go undetected prenatally. Referring every patient to a specialist is not feasible across the country. AI provides a practical alternative: empowering community-based providers to perform higher-quality screening while ensuring that cases are prioritized for referral. When AI indicates a normal heart, the likelihood of a significant defect is extremely low. When uncertainty or an abnormality is suspected, timely referrals can follow — improving efficiency for both patients and specialty centers.

Early experience in practice

Studies evaluating FDA-approved AI ultrasound platforms have demonstrated high sensitivity and specificity in identifying fetal heart defects. AI technology reliably flags abnormal or suspicious images while confidently recognizing normal ones — essential for effective screening.

We have also seen workflow improvements. Sonographers spend less time acquiring images because AI technology can denote when sufficient cardiac views have been obtained. This increases confidence among clinicians, improves patient experience, and allows patients to be seen more efficiently without sacrificing quality. While larger studies are needed to confirm long-term outcome improvements, early data suggest meaningful benefits for access, efficiency, and diagnostic consistency.

Looking ahead

AI adoption in prenatal care remains limited today, but that is changing quickly. As these tools become integrated into ultrasound equipment and reporting systems, they will likely become routine — much like digital imaging and electronic records did in earlier generations. The most important misconception to correct is that AI replaces clinicians. It does not. Clinical judgment, counseling, and shared decision-making remain strictly human. AI simply helps clinicians see more clearly and act more confidently.

In prenatal cardiology, that clarity can mean earlier diagnoses, safer deliveries, and better supported families who are prepared for what lies ahead. Consider how AI-enabled ultrasound could strengthen CHD screening in your network, improve workforce efficiency, and provide actionable insights to support patient care.

Photo credit: Blue Planet Studio, Getty Images

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