New AI research could ‘shake up the field of cardiology’

It is traditionally believed that men and women experience angina—the pain associated with coronary artery disease—in different ways, with men feeling it in their chest and women feeling it in their arms and back. A new clinical trial out of the Massachusetts Institute of Technology (MIT) in Cambridge, however, suggests there are no differences in how angina impacts the two sexes.

Karthik Dinakar, a researcher from the MIT Media Lab, and colleagues presented the results of their trial at the European Society of Cardiology’s annual meeting back in September. The trial leveraged machine learning technology and included more than 600 patients from the United States and Canada who were referred for their first coronary angiogram. Recorded conversations between those patients and their physicians were then analyzed, with the team observing that chest pain was mentioned in 90% of all conversations.

“This work, showing no real differences between women and men in chest pain, goes against the dogma and will shake up the field of cardiology,” co-author Deepak L. Bhatt, executive director of Interventional Cardiovascular Programs at Brigham and Women’s Hospital in Boston and professor of medicine at Harvard Medical School, said in a news release. “It is also exciting to see an application of machine learning in healthcare that actually worked and isn’t just hype.”

“This sophisticated machine learning study suggests, alongside several other recent more conventional studies, that there may be fewer if any differences in symptomatic presentation of heart attacks in women compared to men,” Philippe Gabriel Steg, a professor of cardiology at Université Paris- Diderot and director of the Coronary Care Unit of Hôpital Bichat in Paris, France, said in the same news release. Steg did not participate in the research. “This has important consequences in the organization of care for patients with suspected heart attacks, in whom diagnostic strategies probably need to be similar in women and men.”

Dinakar noted that symptoms aren’t always fully understood following clinical trials because researchers treat “symptoms as check boxes.”

“The result is to isolate one symptom from another, and you don’t capture the entire patient symptomatology presentation—you begin to treat each symptom as if it’s the same across all patients,” Dinakar said in the news release. “Further, when analyzing symptoms as check boxes, you rarely see the complete picture of the constellation of symptoms that patients actually report. Often this important fact is compensated for poorly in traditional statistical analysis.”