High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning

Despite rapidly advancing developments in targeted therapeutics and genetic sequencing,1,2 persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine. This conundrum is exemplified by current approaches to assessing morphologic alterations of the heart.3,4 If reliably identified, misdiagnoses of certain cardiac diseases (eg, cardiac amyloidosis and hypertrophic cardiomyopathy [HCM]) could be avoided, and specific targeted therapies could be initiated efficiently. Systematic screening paradigms, including through imaging and automated medical record feature review, have shown the opportunity to identify patients with underdiagnosed diseases that are increasingly recognized as more prevalent than was previously thought.59 The ability to reliably distinguish between cardiac disease types with similar morphologic features but different causes would also enhance specificity for linking genetic risk variants and determining mechanisms.

Click here to continue reading: jamanetwork.com

Παλαιότερα άρθρα