Researchers at UC San Diego have developed a small, wearable ultrasound which uses artificial intelligence (AI) to assess the structure and function of the human heart for up to a 24 hour period. And most exciting of all, it is only the size of a postage stamp!
Due to its small size and the lack of bulky equipment, the device can be used at home and during vigorous exercise. This capability will allow for the collection of real life data and will simplify testing for patients.
Read the full paper published in Nature here and a short article in Cardiovascular Business here.
A new technology called “virtual native enhancement” (VNE) may soon eliminate the need for gadolinium as a contrast agent for patients with hypertrophic cardiomyopathy undergoing cardiac MRI. Gadolinium, a heavy metal contrast agent which is injected intravenously, has long been used in cardiac MRI scans to spot cardiac scar tissue in patients with HCM. In 2017, the FDA issued a safety communication relating to gadolinium because it was found that gadolinium remains in the body for months to years after the use of the drug.
The new VNE technology, recently described in the journal Circulation, uses artificial intelligence (AI) to virtually enhance the standard MRI image. The technology was developed using data taken from 1348 HCM patients and was validated in the HCM population, but the technology may have uses extending beyond HCM.
By avoiding the use of the contrast agent, this technology avoids side effects and long term consequences from the use of gadolinium. Additionally, it will make cardiac MRI available to patients who are allergic to gadolinium. VNE is also faster and cheaper that current technology used for cardiac MRI, which may make more frequent MRI monitoring of patients feasible.
To read more about VNE, see also this article in UVA Today, this article in ACM Tech News, this article in Engineering and Technology, and this article in Science Daily.
A study by researchers from Mayo Clinic recently published in the International Journal of Cardiology found that a deep learning artificial intelligence algorithm using a standard 12 lead electrocardiogram was able to detect hypertrophic cardiomyopathy in young people with impressive accuracy. This accuracy was particularly strong among adolescents aged 15 – 18.
Mayo has been looking at artificial intelligence for its potential to screen populations for HCM for some time now. Here is a previous HCMBeat story about Mayo’s work on artificial intelligence from February 2020.
A study by researchers from Mayo Clinic published this week in the Journal of the American College of Cardiology found that an artificial intelligence algorithm was able to detect hypertrophic cardiomyopathy, commonly known as HCM, from EKG results with impressive accuracy, particularly among younger patients.
In order to “teach” the computer, the researchers used digital 12-lead ECGs from 2,448 patients with HCM along with 51,153 age- and sex-matched controls. The technology was then tested on 612 HCM patients and 12,788 controls.
The findings showed that the technology was able to identify HCM in a high number of cases, even where the EKG appeared “normal” to the human eye.
The researchers believe that this technology, when refined, may prove to be an efficient tool for HCM screening in the future. The team plans to continue testing the technology in greater subject samples in order to further refine its performance.
Mayo Clinic News Network