Results from the SPEC-AI trial found AI-guided screening using a digital stethoscope to detect pregnancy-related cardiomyopathy among nearly 1,200 pregnant and postpartum individuals in Nigeria was effective and resulted in double the number of cases identified, suggesting that up to half are likely missed with usual care, according to researchers.

"This research can change current clinical practice from one that is reactive and symptom-driven to a more proactive approach of identifying pregnancy-related cardiac dysfunction using a simple, low-cost and effective screening tool," said lead study author Demilade A. Adedinsewo, MD, MPH, FACC. "Earlier diagnosis would facilitate prompt and appropriate management of cardiomyopathy and reduce associated disease and death."

In the SPEECH trial involving more than 400 adults with HF in Israel, a smartphone app leveraging novel speech analysis technology to detect early signs of worsening HF, including impending decompensation, was 71% accurate in detecting HF events about three weeks in advance.

"Speech analysis is novel technology that may be a useful tool in remote monitoring of [HF] patients, providing early warning of worsening [HF] that frequently results in hospitalization," said lead study author William T. Abraham, MD, FACC. "This technology has the potential to improve patient outcomes, keeping patients well and out of the hospital, through the implementation of proactive, outpatient care in response to voice changes."

In the ARISE study of approximately 43,000 patients in a hospital in Taiwan, AI technology paired with electrocardiogram testing reduced the time to diagnose and transfer people with STEMI to the cardiac catheterization laboratory from 52.3 minutes to 43.3 minutes – about 10 minutes. Researchers noted that AI-enabled ECGs accurately diagnosed STEMI patients with positive predictive value of 88% and negative predictive value of 99.9%.

"Due to the recent AI revolution, the accuracy of clinical decision support systems has improved significantly and doctors are becoming more trusting of this technology," said lead author Chin-Sheng Lin, MD, PhD. "Using low-cost tech tools can be valuable in everyday medical work. In the future, we might see more of these tech tools being used in new ways, like in ambulances or on wearable devices, which could change how we care for patients with STEMI."

Findings from the ORFAN trial found AI-driven assessment of perivascular fat (FAI Score) to predict inflammatory risk as part of routine cardiac computed tomography angiography (CCTA) accurately predicted fatal and nonfatal cardiac events, independent from clinical risk scores and CT interpretation. "The new AI-driven risk classification (taking into account coronary inflammation, plaque and risk factors) reclassified about 30% of patients to a higher risk category and about 10% to a lower risk category," according to Charalambos Antoniades, MD, in presenting the results.

"The new AI-driven risk classification changes risk-driven management in 40% of the patients in a real-world evaluation in the UK health care system," he said. "Adding AI-driven assessment of inflammatory risk in routine CCTA, is a major step towards personalized medicine, customizing the use of preventive treatments to the patients who need them."

Clinical Topics: Acute Coronary Syndromes, Heart Failure and Cardiomyopathies, Acute Heart Failure, Noninvasive Imaging, Arrhythmias and Clinical EP

Keywords: American Heart Association, AHA23, Computed Tomography, Acute Coronary Syndrome, Artificial Intelligence, Advanced Heart Failure, Mechanical Circulatory Support, Heart Failure, Cardiomyopathies, Cardio-Obstetrics, Pregnancy Complications, Cardiovascular