Moving #AI in #CVImaging from opaque masks towards outputs that serve clinical, research or #AI workflows🫀Open Source = Open Invitation for Collaboration @ https://t.co/toXz7Ajubo
#EchoFirst
Stop asking the model for a picture of the border. Ask it for the coordinates 🫀
In this video, founder Dr. Shehab Anwer (@ShehabAnwer) introduces ATRIA-EchoTrace:
MedGemma 1.5, QLoRA-adapted so the LV endocardial border comes back as a JSON polygon.
Drag a vertex > Metrics update > The corrected points re-enter training in the same format!
Built for the workflows people actually run: clinical review and AI engineering — not a heatmap you can only accept or bin.
Research use only. Not a medical device. Every contour is a proposal.
Would you rather receive a mask, a polygon, or a spreadsheet?
#EchoFirst #AIinHealthcare
Opaque mask: model decides🫀Structured Polygon: you decide!
I recorded the workstation on a held-out CAMUS frame: best / median / worst of the 200 frames - are all public, including the 25 failures on our website!
Move a point. The metric moves. The correction is already in the corpus format.
Research use only. Not a device.
Would you ship the proposal, or only the revised trace?
🫀 Can serial ultrasound congestion scores predict outcomes better than a single assessment in acute heart failure?
A new prospective study introduces ΔVExPLUs, combining serial changes in Venous Excess Ultrasound (VExUS) and lung ultrasound (LUS) to improve risk stratification in patients hospitalized with acute decompensated heart failure (ADHF).
🔍 Key findings:
✅ 104 patients with ADHF underwent serial assessment of VExUS and lung ultrasound during hospitalization to evaluate changes in systemic and pulmonary congestion.
✅ The combined change (ΔVExPLUs) provided stronger prognostic information than either VExUS or lung ultrasound alone, highlighting the value of integrating venous and pulmonary congestion assessment.
✅ Patients with persistent or worsening ultrasound congestion experienced significantly worse clinical outcomes, emphasizing that successful decongestion should be assessed with imaging rather than symptoms alone.
✅ The study supports multiorgan ultrasound as a practical bedside tool to guide treatment response and identify patients at higher risk before discharge.
💡 Clinical takeaway: Heart failure congestion extends beyond the lungs. Integrating VExUS and lung ultrasound may provide a more complete picture of decongestion and improve risk stratification, paving the way for more personalized management of ADHF.
📖 Read the full article: https://t.co/eAUK9cZ2sa
#CardioX #HeartFailure #POCUS #LungUltrasound #VExUS #Echocardiography #CardiacImaging #AcuteHeartFailure #Cardiology #MedTwitter
The promise of medical AI is not simply better performance — it is better care grounded in evidence that can be trusted. On AI Grand Rounds, @DrXiaoLiu discusses what rigorous evaluation looks like as technologies move from publications into practice. 🎧 https://t.co/cHzTla7ZOh
⚡️ Healthcare AI moves faster when founders can pair breakthrough research with accelerated computing and production-ready infrastructure.
Join us alongside Google Cloud, Google Research, and leading healthcare VCs for a five-part virtual series built for digital health and biotech founders.
Check it out 👇
Train medical robots where anatomy, physics, perception and policy meet.
NVIDIA Isaac for Healthcare introduces GPU-native medical physics simulation with:
🫀 Simulation-ready anatomical digital twins
⚙️ Device–tissue interaction modeling
🩻 X-ray, ultrasound and camera data paired with exact ground truth
⚡ Thousands of parallel simulation environments
🤖 Policy training and evaluation before a real robot moves
Explore the framework for surgical robotics, endoscopy, endovascular intervention and robotic ultrasound.
👇 https://t.co/7VfbeCfKGA
My journey to develop AGI spans 25 yrs, including 10+ yrs thinking about technical & societal perspectives at Google DeepMind.
AGI is on the horizon - we need deeper understanding of its implications. To help, we've created the DeepMind Institute. https://t.co/dpc2y4IGT1
in the first chapter of healthcare AI, the battleground was access to longitudinal clinical data.
in this next phase, it will be access to biological specimens: tissue, blood, synovial fluid, and other samples containing 1,000× more information, linked to clinical context and longitudinal outcomes.
the winners will be those who can access, assay, and interpret these specimens at the lowest cost and highest resolution.
@morgancheatham Excellent insight, @morgancheatham: Longitudinal data spans to the seconds it takes the cardiac tissue to work - I'd love to hear your thoughts about:
https://t.co/XfdzOtMvtF
Opaque mask: model decides🫀Structured Polygon: you decide!
I recorded the workstation on a held-out CAMUS frame: best / median / worst of the 200 frames - are all public, including the 25 failures on our website!
Move a point. The metric moves. The correction is already in the corpus format.
Research use only. Not a device.
Would you ship the proposal, or only the revised trace?
@pridgenmd Amazing work, @pridgenmd ! What if a foundation model is tuned to output coordinates for actionable segmentation? Check this out :)
https://t.co/XfdzOtMvtF
Opaque mask: model decides🫀Structured Polygon: you decide!
I recorded the workstation on a held-out CAMUS frame: best / median / worst of the 200 frames - are all public, including the 25 failures on our website!
Move a point. The metric moves. The correction is already in the corpus format.
Research use only. Not a device.
Would you ship the proposal, or only the revised trace?
Opaque mask: model decides🫀Structured Polygon: you decide!
I recorded the workstation on a held-out CAMUS frame: best / median / worst of the 200 frames - are all public, including the 25 failures on our website!
Move a point. The metric moves. The correction is already in the corpus format.
Research use only. Not a device.
Would you ship the proposal, or only the revised trace?
Stop asking the model for a picture of the border. Ask it for the coordinates 🫀
In this video, founder Dr. Shehab Anwer (@ShehabAnwer) introduces ATRIA-EchoTrace:
MedGemma 1.5, QLoRA-adapted so the LV endocardial border comes back as a JSON polygon.
Drag a vertex > Metrics update > The corrected points re-enter training in the same format!
Built for the workflows people actually run: clinical review and AI engineering — not a heatmap you can only accept or bin.
Research use only. Not a medical device. Every contour is a proposal.
Would you rather receive a mask, a polygon, or a spreadsheet?
#EchoFirst #AIinHealthcare
The number I care about in this clip is not Dice!
It is that a disagreement becomes the next training row with no conversion step!
If you open the workstation, drag one vertex and watch the JSON + millimetres update. That is the whole thesis that bridges human ingenuity & machine intelligence!
Published in Nature Medicine: @Google researchers and collaborators share lessons from prospective studies of AMIE, highlighting why real-world clinical evidence is critical to evaluating conversational AI in health.
Read more: https://t.co/vAKMGHioXM
A cardiac imager cannot argue with a mask!
ATRIA-EchoTrace by The Adimension finetunes MedGemma 1.5 so an echo frame returns editable polygons, translating black box into interoperability!
Drag a vertex. Estimate Parameters. Integrate to your workflow: Still research work!
Built on open weight model & datasets. Open invitation to collaborate.
#EchoTrace #MedGemma