Excited that #PanEcho is now published in @JAMA_current
Kudos to @giholste@ekoikonomou for leading this incredible effort!
Now also validated on ED POCUS studies + RVNet+ (Thx Attila Kovacs & Marton Tokodi for joining us)
See 🧵 below for details!
https://t.co/4dzfzczyg8
#OpenSource #AI
Awesome to see PanEcho highlighted in a special podcast episode from @JAMA_current!
"This is the sort of thing that if 10 or 15 years ago you said we'd be able to do it, I suspect there would have been a lot of skepticism."
@rohan_khera@ekoikonomou@cards_lab@VITAGroupUT
Will 2026 be the year of real-world evidence that #AI can actually improve patient care?
@JAMAplusAI EIC Roy Perlis, MD, MSc, and Associate Editor Yulin Hswen, ScD, MPH, discuss in this special episode marking 1 year since the launch of JAMA+ AI:
https://t.co/ol21UYExUV
This Monday Gregory Holste from The University of Texas at Austin will be joining us to talk about their work on AI-enabled echocardiography interpretation. Catch it at 1-2pm PT this Monday on Zoom! Subscribe to https://t.co/Tr8mrytDbz #ML#AI#medicine#healthcare
Thanks for highlighting our work @DeryaTR_!
#PanEcho is publicly available for research use as well: https://t.co/4jcG5UwoD2.
We hope this can accelerate research on AI for echocardiography and medical AI more broadly
Important advance in medical video imaging interpretation through AI:
Echocardiography is a cornerstone of cardiovascular care, but relies on expert interpretation and manual reporting from a series of videos. An AI system, called PanEcho, has been proposed to automate echocardiogram interpretation with multitask deep learning.
This AI system can, potentially accelerate workflows and enable rapid cardiovascular health screening in point-of-care settings with limited access to trained experts.
AI Echo System Has Automated Screening Potential
The #PanEcho deep-learning system offers AI-enabled echo interpretation and may accurately assess the structure and function of the heart
@giholste@utexasece#AI#echocardiography
https://t.co/Rs8DmF9YUO
Thank you @EricTopol for highlighting our work! What an honor.
This is the product of deep interdisciplinary collaboration between engineers & clinicians. Grateful to teammates & mentors @ekoikonomou@rohan_khera
+ Model is open-source for researchers! https://t.co/4jcG5UwWsA
A new study in @JAMA_current led by Yale researchers finds that an AI-enabled tool can interpret echocardiograms with a high degree of accuracy in just a few minutes: https://t.co/wGJBkHMTNZ
@rohan_khera@ekoikonomou@giholste
An #AI system that automatically interprets echocardiograms maintained high accuracy across geography and time from complete and limited studies.
@rohan_khera
https://t.co/kltl6Ahs4I
Vibe coded a new personal website: https://t.co/XrCKww3drV
Do I know how Next.js works? Not in the slightest. Am I using it in my website? Apparently!
Have to say I'm very impressed with @v0, saved me hours and hours
An experimental new screening test that pairs AI with portable cardiac ultrasounds could ensure more people receive care for two types of cardiomyopathy, a deadly heart condition.
Learn more from @ekoikonomou and @rohan_khera.
@YaleCardiology@cards_lab
https://t.co/L44CHHsaSO
(1/n) Can #AI-enhanced #POCUS detect under-diagnosed cardiomyopathies?
In @LancetDigitalH, we report our findings using #AI to screen for ATTR-CM and HCM using >90k POCUS videos across the EDs of 2 health systems.
Link: https://t.co/1o1cj0bPm7
@cards_lab@rohan_khera
🧵⬇️
NEW Research: #AI-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography. @ekoikonomou@rohan_khera
Read it here: https://t.co/Xc2N7G7g3D
Working on #AI for #echofirst? Use PanEcho!
Why PanEcho? The model is trained on >1M echos from all views on 39 diverse tasks, meaning...
- PanEcho is a powerful transfer learner
- It takes *one line* of Python code
See https://t.co/4jcG5UwoD2 for details + reach out with Qs
After 2 months of embargo for #AHA24 Late Breaking presentation, we are pleased to announce PanEcho - Complete AI-enabled echocardiography interpretation with multi-task deep learning
Led by @giholste & @ekoikonomou@cards_lab
Preprint, code, model here: https://t.co/lxBuu8NkTD