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FDA Clears Pocket-Sized ECG System and AI Technology for Detection of Cardiac Conditions

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Using a reduced leadset and deep neural network algorithms trained on more than 175 million electrocardiograms, the KAI 12L technology reportedly detects up to 35 cardiac determinations, including acute myocardial infarction.

The Food and Drug Administration (FDA) has granted dual 510(k) clearances for the pocket-sized Kardia 12L electrocardiography (ECG) system and KAI 12L artificial intelligence (AI) technology, which may bolster the detection of life-threating cardiac conditions with a reduced leadset.

Trained on more than 175 million ECGs, the deep neural network algorithms with KAI 12L may facilitate the diagnosis of 35 cardiac determinations, including common forms of cardiac ischemia and acute myocardial infarction, according to AliveCor, the developer of the technology.

FDA Clears Pocket-Sized ECG System and AI Technology for Detection of Cardiac Conditions

Dual FDA clearances for the pocket-sized Kardia 12L ECG System (shown above) and the KAI 12L AI technology, trained on more than 175 million ECGs, may bolster the detection of cardiac conditions and facilitate use of the technology in resource-challenged health-care facilities, according to AliveCor, the developer of the modalities. (Photo courtesy of AliveCor.)

"Not only is it the first FDA-cleared AI that can detect a heart attack on a reduced leadset, but it also returns determinations for our broadest range of conditions yet," said Priya Abani, the CEO of AliveCor. "Paired with our pocket-sized Kardia 12L ECG System, this offering is poised to disrupt traditional care pathways and represents a leap forward in cardiac care."

In comparison to standard 12-lead ECG devices, the pocket-sized Kardia 12L ECG system enables clinicians to capture eight quality diagnostic bandwidth leads with five electrodes and a single cable. AliveCor added that the portability of the battery-operated, 0.3-lb. device may expand access to the technology for a variety of health-care facilities including resource-challenged centers or rural medical facilities.

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