Cardiac Condition Evaluation

New from me - the FDA has cleared an AI algorithm that, when used with a digital stethoscope from startup Eko Health, can detect a key risk factor for heart failure. The algorithm was originally developed by the Mayo Clinic for use with electrocardiograms, but as adapted by Eko, it can be used by primary care physicians during routine checkups. Which means that early signs of heart disease might be caught before any symptoms emerge. “These tools are incredibly powerful — they help us screen for conditions for which we have treatments," Mayo Clinic cardiovascular head Paul Friedman told me.

I post a lot about #AI mistakes, hallucinations, and manipulation, but AI is proving itself useful in significant ways. I wish brands would stop racing to unleash AI chatbots on customers and would instead consider how to use AI to improve processes and #CustomerExperience. Here is a study of implementing an artificial intelligence (AI)-enabled electrocardiogram (AI-ECG) to identify hospitalized patients with a high risk of mortality. The study involved 39 physicians, supporting them rather than trying to supersede them in the patient care process. The outcome was quite positive. Implementing the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% of patients in the intervention group died within 90 days, compared to 4.3% in the control group. The impact was even greater in a group of high-risk patients: For the high-risk group, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm.) Every brand is rushing to show how innovative it is by copying every other brand rushing to implement customer-facing chatbots. Rather than hurrying to deploy a chatbot that may harm customers or damage reputation, a smarter use of AI is to integrate it into your processes, supporting and enhancing employees rather than replacing them. https://lnkd.in/dPRt7hwz

“Researchers have developed a new artificial intelligence (AI) model capable of evaluating electrocardiogram (ECG) results and identifying signs of occlusion myocardial infarction faster than other modern techniques. The group shared its findings in Nature Medicine, noting that the model’s performance was much better than expected.[1] “When a patient comes into the hospital with chest pain, the first question we ask is whether the patient is having a heart attack or not. It seems like that should be straightforward, but when it’s not clear from the ECG, it can take up to 24 hours to complete additional tests,” lead author Salah Al-Zaiti, PhD, RN, an associate professor of emergency medicine and cardiology at the University of Pittsburgh, said in a prepared statement. “Our model helps address this major challenge by improving risk assessment so that patients can get appropriate care without delay.” Al-Zaiti et al. trained their algorithm using ECG data from more than 4,000 patients who presented with chest pain at one of three Pittsburgh hospitals. They validated the model using data from nearly 3,300 patients seen at a different health system. Researchers have developed a new artificial intelligence (AI) model capable of evaluating electrocardiogram (ECG) results and identifying signs of occlusion myocardial infarction faster than other modern techniques. The group shared its findings in Nature Medicine, noting that the model’s performance was much better than expected.[1] “When a patient comes into the hospital with chest pain, the first question we ask is whether the patient is having a heart attack or not. It seems like that should be straightforward, but when it’s not clear from the ECG, it can take up to 24 hours to complete additional tests,” lead author Salah Al-Zaiti, PhD, RN, an associate professor of emergency medicine and cardiology at the University of Pittsburgh, said in a prepared statement. “Our model helps address this major challenge by improving risk assessment so that patients can get appropriate care without delay.” Al-Zaiti et al. trained their algorithm using ECG data from more than 4,000 patients who presented with chest pain at one of three Pittsburgh hospitals. They validated the model using data from nearly 3,300 patients seen at a different health system.”

🔉 Now in Nature Portfolio's Nature Cardiovascular Research, our study reports the development of a novel #DeepLearning #AI model that detects Hypertrophic Cardiomyopathy (HCM) from photos of ECGs. HCM : 🏥 Is a leading cause of sudden cardiac death in young adults 🪢 Has a long asymptomatic course 📽️ Is Infeasible to screen as requires cardiac imaging for dx Our approach detects HCM: 📸 Using photos/images of electrocardiograms ⭐ Across layouts of images 🇺🇸 🇬🇧 🇳🇱 Validation across 3 multinational sites, with AUC >0.9 at all 👁️🗨️ Identifies patterns of LV thickness in test POSITIVES vs NEGATIVES Read more: https://lnkd.in/eY8qGaR5 Research tool at: https://lnkd.in/e7ytvrVN We are working to improve targeted screening approaches to drive up positive predictive value further Led by star members of the Cardiovascular Data Science (CarDS) Lab, Veer Sangha & Lovedeep Dhingra, along with Evangelos K. Oikonomou, Philip Croon & Arya Aminorroaya MD, MPH, and our collaborators, Harlan Krumholz, Folkert Asselbergs, Martin S. Maron, MD, & Matthew Martinez

Epigenetic age of carotid plaques predicts future cardiovascular events. Prior studies showed that blood's epigenetic age predicts cardiovascular events. This novel research stands out for examining human plaques. Kudos to the vascular biobank Athero-Express for tracking patients post-surgery. Ernest Diez Benavente , Robin Hartman, Hester den Ruijter (2024) Atherosclerotic Plaque Epigenetic AgeAcceleration Predicts a Poor Prognosis and Is Associated With Endothelial-to-Mesenchymal Transition in Humans. Arterioscler Thromb Vasc Biol. https://lnkd.in/daGec3HG #EpigeneticClocks #CardiovascularHealth #Methylation

"Forget the cloud. Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis. With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time data processing and near-instant diagnostics. To test the concept, engineers used the device to classify large amounts of information from publicly available electrocardiogram (ECG) datasets. Not only could the device efficiently and correctly identify an irregular heartbeat, it also was able to determine the arrhythmia subtype from among six different categories with near 95% accuracy." #ai #energyefficiency #cloudcomputing

We Have the 🛠️ Tools. The Potential 💡 Is Clear. Let’s Rethink ❤️🩹Cardiovascular Care ❤️🩹at Scale. A compelling review by Aline Pedroso, PhD and Rohan Khera in Nature Portfolio’s Cardiovascular Health. Great outline on how AI-powered wearables, PPG/ECG sensors, point-of-care ultrasound, and edge-AI models can and are transforming cardiovascular care—extending reach, reducing friction, and bringing precision to the front lines. 👉 Article: https://lnkd.in/eCNVj8_F Why this matters: ✅Community-based detection of arrhythmias and structural heart disease is feasible now. ✅Multimodal sensor + AI fusion improves prediction, risk stratification, and monitoring. ✅Cloud and edge tech enable privacy-preserving integration into clinical workflows. ✅Tools like AI-guided echocardiograms with GE HealthCare’s Caption Guidance (FDA-cleared for use by any medical professional) allow earlier, scalable echo screenings—no sonographer required. ✅These shifts are especially powerful in under-resourced or preventive care settings. Call to action for Health Systems, Payers, MedTech and Innovators: 1️⃣ Advance interoperability—connect consumer and bedside data with clinician workflows. 2️⃣ Fund pragmatic RCTs to validate outcomes, not just signal accuracy. 3️⃣ Build reimbursement models that reward early detection and smarter triage. 4️⃣ Design inclusively—this must close gaps, not widen them. 💡 We’re past proof of concept and evolve the platform. Time to implement boldly, equitably, and at scale. #DigitalHealth #AIinHealthcare #CardiovascularCare #HealthEquity #Wearables

Did you know your gut could impact your heart health after surgery? ❤️ This study in Nature Portfolio uncovers a strong link between gut microbiota metabolism and postoperative atrial fibrillation (POAF) after coronary artery bypass grafting (CABG). 🌟 Key Findings Patients who developed POAF had significantly different gut microbiota and metabolome profiles compared to those who didn't. The study identified five secondary bile acids as powerful predictors of POAF with an accuracy of over 90%. 🔍 Why It Matters #Diversity - POAF patients had lower gut microbiota diversity and richness. - Higher levels of harmful bacteria: Actinobacteria and Firmicutes. - Lower levels of beneficial bacteria: Roseburia and Coprococcus. #metabolites - Nine bile acids were elevated in POAF patients (e.g., deoxycholic acid, lithocholic acid). - Three short-chain fatty acids (SCFAs) increased: acetic acid, propionic acid, and isobutyric acid. - These metabolites influence inflammation and oxidative stress, crucial factors in heart rhythm disturbances. 🔑 Takeaway I’ve always been particularly interested in bile acids. They’re not frequently discussed, but they’re an untapped area of health with tremendous potential. Especially considering their roles in digestion, regulating cholesterol, and more. Their impact on inflammation and metabolic pathways also suggests they could be critical when it comes to predicting conditions like POAF and other cardiovascular issues as well. 🔗https://lnkd.in/eA8J5tMz #guthealth #microbiome #hearthealth #cardiacsurgery #atrialfibrillation #personalizedmedicine #injoy

This is wild. A simple ECG, transformed by AI, could become a powerful tool for early detection of structural heart disease. A recent study in Nature Portfolio introduces Echonext, an AI tool that analyzes ECGs to identify patients who may need an echocardiogram. By using a common, low-cost test to guide imaging decisions, it helps surface high-risk patients who might otherwise be missed. This technology improves performance and imaging stewardship, moving towards higher value for patients and systems. Appropriate use of echocardiograms has long been a challenge. Some patients don’t get the imaging they need, while others undergo unnecessary exams. Compared with standard of care, the AI achieved higher accuracy, and when applied retrospectively, it flagged thousands of high-risk patients who had been overlooked. This once again shows that human plus machine can be better than human alone. As a radiologist, I see the power of AI to help guide appropriate evaluation and use of imaging tools as truly transformational. That represents a new diagnostic opportunity at scale. Smarter referrals. Earlier detection. Improved outcomes. Clinical judgment will always be essential. But AI can sharpen that judgment when the signals are subtle or easy to miss. If we can turn 400 million ECGs each year into 400 million opportunities to detect disease earlier, we are improving care and changing lives. We are following this work closely at Ainsley Advisory Group. 🔍 https://lnkd.in/eXjw7Gvq

⚕️ AI in Cardiology: From Code to Clinic I am excited to share a groundbreaking study recently published in Nature Magazine, led by our colleague at Columbia University Irving Medical Center, Dr. Pierre Elias, MD. This research introduces "EchoNext," a deep learning model designed to detect structural heart disease (SHD) from electrocardiograms (ECGs). SHD encompasses pathologies that affect the heart's valves, walls, or chambers , and it is a growing epidemic that remains substantially underdiagnosed. Early detection is key, but widespread screening is limited by the cost and accessibility of definitive diagnostic tools like echocardiography. 🔬 The research team developed EchoNext by training it on an extensive and diverse dataset of over 1.2 million ECG-echocardiogram pairs from more than 230,000 patients. The model demonstrated high diagnostic accuracy in both internal and external validation (AUROC was 85.2% in the internal test set) 🧠 In a direct comparison based on 150 ECGs, the EchoNext model alone was more accurate at detecting SHD from an ECG than the 13 participating cardiologists. The AI model alone achieved 77.3% accuracy. This outperformed both cardiologists working without AI assistance (64.0% accuracy) and cardiologists who were assisted by the AI's prediction (69.2% accuracy). I think this demonstrates the immense potential for AI to augment our diagnostic capabilities, using a test that is already ubiquitous in medicine. 🩺 What I find particularly compelling is that this research didn't stop at retrospective validation. The team conducted a 100-patient prospective clinical trial, called DISCOVERY, to test an AI-ECG model's ability to find previously undiagnosed heart disease in a real-world setting. The trial successfully identified patients (n=37) with previously unknown, clinically significant SHD. 🌐 To foster further innovation and transparency, the authors have publicly released model weights and a large, annotated dataset of 100,000 ECGs from 36,286 unique patients, which is a commendable move to advance the entire field. Link to the paper: https://lnkd.in/ed7KT52g