AI Echo

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Congenital Heart Diseases
  • CHDs (congenital heart diseases) are the most common type of birth defect, affecting nearly 1% of births per year.

  • About 1 in 4 babies born with a heart defect has a critical CHD.

Our Data

 

  • Now that many of those algorithms don’t account for       heterogeneity or variability of imaging equipment, our algorithm mainly focuses on the data from echocardiography, a ultrasound technology which captures high temporal and spatial resolutions of the heart and surrounding structure and which serves as a primary equipment for the diagnosis and follow-ups of CHD patients with the combination of rapid acquisition of image and the lack of ionizing radiation.

  • To date, no autonomously functioning radiology AI algorithms have gone to market; in the current market, AI still serves as a clinical decision support tool designed to assist with specific components of an imaging specialist’s workflow

  • While other works applying machine learning to medical images requires re-annotation by human experts, the clinical workflow for echocardiography inherently includes many measurements and calculations and is often reported through structured reporting systems; its ability to use previous annotations and interpretations from clinical reports will contribute to the building of our algorithms and accelerate the adoption of it in clinical practice. 

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Our CHD Algorithm & Deep Learning 
 
  • Our deep learning models, which are supposed to rely on large volumes of annotated data to     automatically learn features for subsequent detection tasks, will still have such a “small data   challenge,” wherein the number of samples available for training a machine learning model is several orders lesser than the number of model parameters.