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A.J. Rogers Profile
A.J. Rogers

@ajrogers_md

Followers
451
Following
612
Media
41
Statuses
279

Electrophysiologist at Stanford Arrhythmia Clinic

Stanford, CA
Joined March 2010
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@ajrogers_md
A.J. Rogers
8 hours
“I’m jealous of our agentic lab scientists because their meetings are very efficient!” -James Zou Join us at https://t.co/yCzfseIswV #EPeeps #StanfordEP25 @S_NarayanMD @Wanginnovate
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@S_NarayanMD
Sanjiv Narayan
4 days
#ep_peeps #StanfordEP25 FRIDAY OCT 24TH 8A-4P PT Register to discuss latest #AF #VT #innovations via #AI & case #efficiency with @EPrystowskyonEP Isabelle Diesenhofer @DrBradleyKnight @james_y_zou @netta_doc @TinaBaykaner @Wanginnovate In person/stream https://t.co/X1ZZ3pXziS
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@David_Ouyang
David Ouyang, MD
15 days
It was great to visit old friends and meet the current fellows! A great number of amazing physician scientists - I look forward to seeing what yall do next!
@Ron_Witteles
Ronald Witteles
15 days
Incredible Stanford Cardiology Grand Rounds by @David_Ouyang on AI in Cardiology. He’s a true pioneer of the field & we’re so proud he is a @StanfordMedRes & @StanCVFellows alum. And look at all those @StanfordMedRes #friendsoftheprogram alumni he’s collaborating with! 🙂
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@ajrogers_md
A.J. Rogers
5 months
Hi #EPeeps, check out the new calipers web app! No download required, just print screen and paste to measure. New features: Rhythm calipers and present an EGM PDF with live calipers. @StanfordCVI @StanCVFellows @EPeeps_Bot https://t.co/RnIpQkFCtQ
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@ElaineWanMD
Elaine Wan
5 months
👉🏻Just published in @NatureCVR most up to date review of “Cardiac Imaging Techniques for Electrophysiologists” - we cover everything CT/MRI/ICE/PET https://t.co/TzTYCwBF9J @ajrogers_md @OlgaReyn @DJ_Lakkireddy @drrakeshg1 @Hirad7 @NTrayanova @EP_mom1 #EPeeps #cardiotwitter
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@ajrogers_md
A.J. Rogers
6 months
Join us next year for the Biodesign New Arrhythmia Technologies Retreat at #HRS2026 in Chicago, IL! #EPeeps @StanfordCVI @Wanginnovate @S_NarayanMD https://t.co/FCeLkshV8R
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@EricTopol
Eric Topol
9 months
Getting your real-time heart rhythm monitored for 10 days now requires an ECG technician to review and notify your doctor of a critical arrhythmia. A new study showed A.I. did better than technicians (98.6% vs 80.3%) for identifying these abnormalities https://t.co/yhvLvRO09Q
Tweet card summary image
nature.com
Nature Medicine - In a large-scale analysis of ambulatory electrocardiographic recordings, a deep learning algorithm had a substantially higher sensitivity for the detection of critical arrhythmias...
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@S_NarayanMD
Sanjiv Narayan
9 months
Great to host the incomparable Steven Niederer giving rounds at ⁦⁦@Stanford⁩ ⁦@CEMRG_⁩ ⁦@Joseph_C_Wu⁩ @ Mark Mercola ⁦@joshuawknowles⁩ ⁦@Daniel_B_Ennis⁩ ⁦@EldrinL⁩ ⁦@JordiHeijman⁩ @ andrew McCulloch ⁦@MarsdenStanford
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@HanZhuMD
Han Zhu
9 months
Proud to share this new pub from my lab - “Novel Therapeutic Approach Targeting CXCR3 to Treat Immunotherapy Myocarditis” https://t.co/1R52DuooaC.... Congrats to first authors @yuhsinvhuang and @yinsun96. Our first foray into targeted therapy for this devastating condition
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@CirculationEP
CircEP
10 months
Prompt engineering enables ChatGPT to interpret #EHRs with near-expert accuracy without the need for expert knowledge, allowing analysis by non-experts #AHAJournals #Epeeps @mvperez92 @ajrogers_md https://t.co/Ckg6x2F2Cr
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@netta_doc
JK Han MD
1 year
Our latest in @CirculationEP ! “Women Trainees in #Electrophysiology & the Effect of #RoleModels” Thank you to @TinaBaykaner for leading this work & to the many leaders in EP for their collaboration & support of this project 🔗 https://t.co/TpzGI7RIH2
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@S_NarayanMD
Sanjiv Narayan
1 year
#EPeeps Please join us on Friday October 18, 2024, 8 AM to 4 PM PT for rapid practical talks 3 tracks: #Afib #VT #AI Advanced Practice Providers #StanEP24 Online: https://t.co/y2PKwsxRxn @atulverma_md @Drdevignair @JonPicciniSr @TinaBaykaner @wanginnovate @TJaredBunch @EldrinL
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@imin_chiu
I-Min Chiu, MD, PhD
1 year
Excited to share 'KardioNet' published in @JACCJournals! Our proof-of-concept #AI model to detect hyperkalemia from the #AppleWatch #ECG . A huge thanks to @David_Ouyang for all the advice and support! paper: https://t.co/4TzDRTVvpc code:
@imin_chiu
I-Min Chiu, MD, PhD
1 year
Excited to share our new work #KardioNet, an AI-enabled smartwatch ECG algorithm for serum potassium monitoring. Trained on ~300k ECG and potassium pairs at @CedarsSinai, Kardio-Net is designed to enhance potassium monitoring via smartwatch for ESRD patients. #digitalhealth
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@StanfordMedRes
StanfordMedRes
1 year
Meet the CHieVS, the 2024-2025 @StanfordChiefs! @CelesteWitting, @HayleyGalitzer, @VishnuNairMD, and #SulaimanSomani are thrilled to lead and supercharge the Residency experience! 💫
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@AHAScience
AHA Science
1 year
1/ This provides a framework for the periprocedural management of patients with CIEDs, encompassing the planning phase, procedure, & subsequent care coordinated with the primary device managing clinic. ✍🏽 @ajrogers_md @ElaineWanMD @MLavelleMD @DrMiguelLeal @sarah_a_stone
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@AHAScience
AHA Science
1 year
@ajrogers_md @ElaineWanMD @MLavelleMD @DrMiguelLeal @sarah_a_stone 2/ View the paper and supplemental resources here:
professional.heart.org
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@AHAScience
AHA Science
1 year
1/ Understanding & managing patients with CIEDs is important as interference with device function may result in hemodynamic disturbances or arrhythmias. 📷 The cycle of periprocedural management of CIEDs ✍🏽 @ajrogers_md @ElaineWanMD @MLavelleMD @DrMiguelLeal @sarah_a_stone
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@StanCVFellows
StanfordCVFellowship
1 year
@StanfordDeptMed @euanashley Tremendously excited to have one of our great leaders and mentors @euanashley helping to chart the path forward!
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@David_Ouyang
David Ouyang, MD
1 year
Great work by @Ray29750200 @CedarsSinaiMed for proof of concept smartwatch ECG to detect hyperkalemia in ESRD patients! External validation at @stanfordmed by @jwestonhughes @ajrogers_md @mvperez92 @james_y_zou and CGMH in Taiwan
@imin_chiu
I-Min Chiu, MD, PhD
1 year
Excited to share our new work #KardioNet, an AI-enabled smartwatch ECG algorithm for serum potassium monitoring. Trained on ~300k ECG and potassium pairs at @CedarsSinai, Kardio-Net is designed to enhance potassium monitoring via smartwatch for ESRD patients. #digitalhealth
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@CircHF
Circ: Heart Failure
2 years
Deep learning models for heart failure prediction from ECGs show bias against minority groups, namely young Black women @StanfordMed @mvperez92 @S_NarayanMD @ajrogers_md @jwestonhughes #AHAJournals https://t.co/rZCtLeqGrm
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