![](https://static.wixstatic.com/media/5d69fc_c7a71fea436b4cd49cbad60c890d9662f000.jpg/v1/fill/w_288,h_162,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/5d69fc_c7a71fea436b4cd49cbad60c890d9662f000.jpg)
![](https://static.wixstatic.com/media/5d69fc_1ccc5bd46a654dd292cd6d26b862a4eaf000.jpg/v1/fill/w_68,h_38,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/5d69fc_1ccc5bd46a654dd292cd6d26b862a4eaf000.jpg)
AI Echo
![human-body-6133439_1920.jpg](https://static.wixstatic.com/media/5d69fc_d48e4d56292b4bf1a7148a458cfdb5ec~mv2.jpg/v1/fill/w_270,h_450,al_c,q_80,usm_0.66_1.00_0.01,enc_auto/human-body-6133439_1920.jpg)
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.
![ekg-158177_1280.png](https://static.wixstatic.com/media/5d69fc_1a37aa230e1b4525aff48768da5fda19~mv2.png/v1/fill/w_88,h_92,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/ekg-158177_1280.png)
![heart-1767352_1280.jpg](https://static.wixstatic.com/media/5d69fc_1ca4bcfab5644222bb68879b8b396e95~mv2.jpg/v1/fill/w_104,h_58,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/heart-1767352_1280.jpg)
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.