🚨 Can AI help adjudicate clinical trial endpoints? In this study, Auto-MACE used an LLM Clinical Longformer to adjudicate CV death, MI, and stroke from trial records. In confident cases, agreement with CEC was: CV death: 97% MI: 89% Stroke: 88% Treatment effect for MACE was nearly identical: HR 0.91 with AI vs 0.90 with CEC. Manuscript link: jacc.org/doi/abs/10.1016/j.j… #AIinMedicine #DigitalHealth #ClinicalTrials #CardioX #Cardiotwitter

Can digital twins help answer whether RCT results apply to real-world patients? New in npj Digital Medicine: RCT-Twin-GAN simulates how SPRINT/ACCORD treatment effects shift across populations. nature.com/articles/s41746-0… #AIinMedicine #DigitalHealth

Can artificial intelligence detect coronary plaque progression before it becomes clinically evident? This is the first deep-learning model designed to detect changes in plaque progression by quantifying frame-to-frame changes in plaque burden across adjacent IVUS images. eurointervention.pcronline.c…

🚨🫀 JACC: can a routine 12-lead ECG AI predict future HF? In 14,126 participants from Framingham, CHS, and MESA, a positive composite ECG-AI screen tracked with ~25x higher 1-year HF risk and still >10x higher 10-year risk. Even better, adding ECG-AI improved PREVENT-HF risk stratification. Congrats @akshaydesaimd @FarazAhmadMD @DrLopezJimenez @JavedButler1 @SvatiShah @HFpEF and team 👏 @JACCJournals #CardioTwitter #HeartFailure #ECG #AIinMedicine #PreventiveCardiology

🚨🫀 Wearable cardiology AI just got a lot more interesting. This EHJ Digital Health paper introduces Wearable-Echo-FM — a foundation model that learns from paired 1-lead ECGs echo reports to improve screening for structural heart disease. Big takeaway: With the full dataset, performance was similar to a standard CNN. But when labels were scarce, the pretrained model really separated itself. With only 0.5% of training data, it still achieved: ⚡ 0.86 vs 0.55 for low EF ⚡ 0.82 vs 0.58 for diastolic dysfunction ⚡ 0.86 vs 0.50 for composite structural heart disease That is what makes this exciting: contrastive pretraining may make 1-lead wearable ECG models far more label-efficient. Great work by @rohan_khera @ekoikonomou @AryaAminorroaya @af_pedroso and team 👏 #CardioX #AIinCardiology #DigitalHealth #Wearables #ECG #Echo

We've released a #new State-of-the-Art Review, "Left Atrial Strain in Pediatric Cardiology: Evidence to Date and Future Directions." Read our @JournalASEcho article: bit.ly/3OvhqHi

🚨 Introducing Cardio amyloid-AI — a multimodal AI pipeline for early detection of ATTR-CM (transthyretin cardiac amyloidosis) in severe aortic stenosis patients. Using routine CT scans, echocardiograms, ECGs & demographics — no extra tests needed. 🫀 Our multimodal model outperforms every single-modality baseline (CT alone, Echo alone, ECG alone) on AUC — validated against PYP imaging (gold standard) in TAVR patients. Trend data from 2004–2016 shows rising detection probability over time. The model catches what humans miss. ATTR-CM is routinely missed until it’s too late. Opportunistic AI screening using already-collected clinical data = earlier diagnosis, timely treatment, better outcomes. No added cost. No extra scans. Just smarter use of existing data. #CardioAI #AmyloidHeart #AIinMedicine #TAVR #Cardiology

🫀✨ Really exciting AI-echo work in congenital heart disease. EchoFocus-CHD is a multitask model built from ~3.6 million echo videos across ~58,000 studies, with data spanning 58 countries and 6 continents 🌍 The model showed: 🔹 Excellent internal performance for critical CHD 🔹 Important real-world testing across outside referral studies 🔹 Better international performance after retraining on broader data @sgcard @jmayour @JohnTriedmanMD @FraSperotto @BostonChildrens #CardioX #PedsCardiology #Echocardiography #AIinMedicine #DigitalHealth #CongenitalHeartDisease #ASE #ACHD #Congenitalheartdisease

🫀⚡ EchoNext-Mini is a big deal for open-source cardiology AI ✅ 100k ECGs ✅ 36,286 patients ✅ Echo-confirmed structural heart disease labels ✅ Public baseline model This is the kind of dataset that can accelerate external validation, benchmarking, and future model training 🔓📈🚀 @jwestonhughes @timpotsMD @PierreEliasMD #CardioX #AIinCardiology #OpenScience #DigitalCardiology @NEJM_AI

PRO-TAVI trial: Deferring PCI was non-inferior to routine PCI before TAVI for the 1-year composite of all-cause mortality, MI, stroke, and major bleeding, suggesting its appropriate role in selected CAD patients. #ACC26 View slides here: clinicaltrialresults.org/wp-…

ALERT trial: AI-driven automated electronic clinician notifications for severe valvular heart disease accelerated and improved rates of cardiac specialty referrals and interventions vs. usual care. #ACC26 View the slides here: clinicaltrialresults.org/wp-…

Another LV unloading trial presented at #ACC26, this time in elective high-risk PCI. CHIP-BCIS3: elective LV unloading for complex PCI in severe LV dysfunction 🧠 Design: • N=300 randomized • Severe LV dysfunction (LVEF ≤35%) • STEMI & cardiogenic shock excluded • Very complex CAD: BCIS-JS 12, SYNTAX ~38 • High-risk PCI: LM (~72%), calcium modification (~81%), CTO (~27%) • Micro-axial flow pump vs standard care 📌 Primary outcome (hierarchical composite: death, disabling stroke, spontaneous MI, CV hospitalization, periprocedural myocardial injury): ❌ No benefit (WR 0.85, p=0.30) 📌 Mortality: ⚠️ Higher CV death with unloading (26.7% vs 14.5%; HR 1.91) All-cause death numerically ↑ ⚖️ Takeaway: Routine use of LV unloading in elective stable high-risk PCI is not advised nejm.org/doi/full/10.1056/NE… #CardioX #PCI @ACCinTouch

Can we get physiologic lesion assessment without a pressure wire? ALL-RISE: FFRangio vs pressure-wire for intermediate lesions 🧠 Design: • N=1930 randomized • Intermediate coronary stenosis (50–90%) • FFRangio = @CathWorks software that uses AI computational modeling to derive physiologic lesion assessment from routine angiograms, without adenosine or invasive pressure wires • Compared with standard pressure-wire physiology • Primary endpoint = 1-yr MACE (death, MI, or unplanned revascularization) 📌 Primary outcome: ✅ Noninferior (6.9% vs 7.1%; HR 0.98, 95% CI 0.70–1.39; P<0.001 for NI) 📌 Components: • Death: 2.3% vs 2.1% • MI: 1.6% vs 2.5% • Revasc: 4.1% vs 4.6% 📌 Workflow advantage: ⏱️ Faster physiology assessment 💉 Less contrast ☢️ Less fluoro ⚖️ Takeaway: #AI-enabled angiography-guided physiology assessment achieved similar 1-year composite end point of death, myocardial infarction, or unplanned clinically indicated coronary revascularization, with a simpler cath lab workflow. nejm.org/doi/full/10.1056/NE… #CardioX #ACC26 #PCI #FFR @ACCinTouch #AIinMedicine

STEMI Door-to-Unload (DTU): LV unloading prior to PCI in anterior STEMI 🧠 Design: • STEMI, no shock • Impella CP ~30-min delay to PCI vs immediate PCI • Strategy: reduce infarct size via LV unloading 📌 Primary endpoint (infarct size %LVM): ❌ No difference (31.8% vs 33.7%; Δ −1.9%, p=0.28) 📌 Key secondary (30d mortality): ↘️ Numerically lower early mortality, NS (1.2% vs 3.5%) 📌 Primary safety (BARC 3–5 bleeding / vascular complications): ⚠️ Higher with Impella strategy (~31% vs ~6%) 📌 Takeaway: No infarct size reduction with LV unloading strategy ↑ Bleeding/vascular complications remain a concern #CardioX #STEMI #ACC26 @ACCinTouch

One of the most awaited trials at #ACC26 CHAMPION-AF tests LAAO vs NOACs as a first-line strategy in AF patients eligible for anticoagulation. 🧠 Design: • N=3000, open-label RCT • Mean CHADS VASC 3.5, HAS-BLED ~1 📌 Primary efficacy (3y): CV death, stroke, systemic embolism → noninferiority met (5.7% vs 4.8%) 📌 Primary safety (non-procedure-related bleeding): Lower with LAAO (10.9% vs 19.0%; HR 0.55) 📌 Net clinical benefit (primary efficacy non-procedure-related bleeding): Favors LAAO (HR ~0.66) ⚖️ CHAMPION-AF, among patients with AF, left atrial appendage closure was noninferior to NOACs for CV death, stroke, or systemic embolism, and superior for non–procedure-related bleeding. @ACCinTouch nejm.org/doi/full/10.1056/NE…

🚨 Late-Breaking @ACCinTouch #ACC26 HI-PEITHO trial: In intermediate-risk PE, ultrasound-assisted catheter-directed thrombolysis (USCDT) anticoagulation significantly reduced early clinical deterioration vs anticoagulation alone (4.0% vs 10.3%, RR 0.39, p=0.005). 💥 No increase in major bleeding 🧠 No intracranial hemorrhage A potential shift in how we manage submassive PE. #CardioX #PERT #PulmonaryEmbolism

AMIE, a conversational medical #AI tool for clinical reasoning and dialogue, moves diagnostic AI from simulation → real patients 🏥 A prospective single-arm study showed results comparable to PCP reasoning 👩‍⚕️ Very interesting feasibility study. Looking forward to what comes next. research.google/blog/amie-a-… @AdamRodmanMD @GoogleDeepMind @BIDMChealth @DemisHassabis #CardioTechX #AIinMedicine

What happens when you train a foundation model on data from Epic Cosmos, one of the world’s largest de-identified longitudinal EHR datasets? In our latest CardioTechx Journal Club, we break down CoMET: a generative medical event model trained on 118M patients and 115B medical events, with signals in CKD progression, readmissions, and disease trajectories. Turn doom scrolling into microlearning. #EPIC #AIinMedicine #Cardiology #DigitalHealth #MedTwitter @HeyEpic @shanewaxler @PaulJBlazek

Can AI read an ECG like a cardiologist - from just an image? We built ECG-GPT, a vision-text transformer that generates complete diagnostic reports directly from photos of 12-lead ECGs Now out in @ESC_Journals #EHJDigitalHealth Kudos to @aakhunte & @Veer_Sangha_ for leading this @cards_lab 🧵