Artificial Intelligence

Cedars-Sinai Detects AFib with Echo AI

A Cedars-Sinai-led team developed an echocardiography AI model that accurately detects patients with atrial fibrillation – including asymptomatic patients – potentially revealing a new “opportunistic screening” method to drive earlier AFib detection and treatment.

  • Transthoracic echo (TTE) is the most common cardiovascular imaging exam, and is increasingly being combined with AI to support image acquisition, interpretation, and reporting.
  • AFib detection has historically relied on ECG exams, and although ECG AI has shown promise, many patients who undergo ECGs were already either suspected to have AFib or already experienced a major cardiac event.

The researchers trained their deep learning algorithm using 111k TTE videos, including 39k exams from patients who were in AFib and 72k exams from patients with normal sinus rhythm at the time of their echos… although 6,654 of those “normal” exams were from patients who actually had paroxysmal AFib.

  • When tested against an internal Cedars-Sinai TTE dataset, the model identified patients who were in AFib with “high accuracy” (AUC: 0.96) and patients with normal sinus rhythm but paroxysmal AFib “moderately well” (AUC: 0.74).
  • When tested against 10k TTEs from an external Stanford dataset, the model achieved a decent 0.69 AUC identifying patients with a history of AFib.

That performance was consistent across genders, older patients, and patients with higher AFib risks, and perhaps more notably, it outperformed AFib detection based on clinical risk factors, TTE measurements, LA size, and CHA2DS2VASc Scores (AUCs: 0.64, 0.64, 0.62, 0.61).

They then combined the TTE AI model with an ECG-based deep learning model, finding that the combined TTE/ECG system detected patients with AFib better than the ECG model on its own (AUC 0.81 vs. 0.79), potentially because structural information from TTEs might complement ECG-based analysis. 

The Takeaway

There are about 6 million people in the United States living with AFib, and nearly a quarter of them are currently undiagnosed. Although Cedars-Sinai’s echo AI AFib model is still in its early stages, this study makes a solid case for how AI could be used to proactively analyze the 7 million echo exams that are performed in the US each year to help identify more undiagnosed AFib patients while their treatment would be most effective.

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