Innovations in Ecg Analysis Techniques

Recreating a full 12-lead ECG using only 3 leads as input! In our npj #DigitalMedicine paper, we developed an #AI algorithm, trained on over 600,000 ECGs, to reconstruct a 12-lead #ECG from just 2 limb leads and 1 precordial lead (V3): https://lnkd.in/gmWZdHUm . The algorithm accurately reconstructs the missing leads in terms of mean square error, enabling an automatic algorithm to recognize ECGs with ST-elevation myocardial infarction (#STEMI) with good accuracy. Most importantly, to demonstrate that the algorithm's output data can be interpreted, we showed cardiologists either the original 12-lead ECG or an AI-reconstructed ECG using data from the three selected leads. They recognized STEMI with 81.4% accuracy when presented with the reconstructed ECG, comparable to an accuracy of 84.6% when presented with the original ECG. After future prospective validation, this technology could allow patients to obtain high-quality, time-sensitive clinical data without traveling to a facility with a 12-lead ECG. This would increase access to ECG technology, reduce costs, and improve patient safety. Such algorithms may be utilized outside of clinical settings, enabling timely STEMI diagnoses and potentially facilitating prompt emergency procedures. Paper with" Federico Mason, Amitabh Pandey, MD, Matteo Gadaleta, Eric Topol, MD, and Evan David Muse, MD PhD Nature Portfolio, Scripps Research, Scripps Research Digital Trials Center, Scripps Research Translational Institute

🔥 NEW Study: Can you rely on conventional ECG algorithm readings of a "Normal ECG" to confidently rule out Acute Myocardial Infarction? Many assume clinicians ignore these automated ECG readings. Yet, evidence repeatedly shows frontline healthcare providers rely on conventional ECG algorithm outputs during initial patient triage and decision-making. A recently published quality improvement study, Shifa Karim analyzed 96 ECGs from 42 patients with angiographically confirmed STEMI, all initially classified as "Normal" by traditional ECG algorithms: ✅ 81% of these "normal" ECGs were correctly reclassified as positive by PMcardio's AI ECG. ✅ The most frequent feature leading to reclassification was reciprocal changes. ✅ The second most common feature was hyper-acute T-waves. Conventional ECG algorithms, humanly programmed to detect abnormalities based on fiducial points, frequently miss critical patterns of STEMI. AI-driven deep neural network models like PMcardio AI ECG offer significant potential in identifying these dangerous false negatives, reducing the risk of false reassurance, and enhancing clinical decision-making.

AI-Enhanced ECG for Rapid Myocardial Infarction Detection: The ROMIAE Study A recent European Heart Journal study by Lee et al. (2025) presents findings from the ROMIAE multicenter study, which evaluates the performance of an artificial intelligence–enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI) in the emergency department (ED) setting. Conducted across 18 university hospitals in South Korea, the study assessed 8,493 adult patients presenting with AMI symptoms within 24 hours of onset. The AI-ECG algorithm achieved an AUROC of 0.878 (95% CI, 0.868–0.888) for AMI detection, comparable to the HEART score (0.877) and superior to other risk stratification tools, including the GRACE 2.0 score and physician-estimated AMI probability. Notably, AI-ECG demonstrated a 99.1% negative predictive value (NPV) for ruling out AMI in low-risk patients, effectively identifying 8.2% of patients as low risk within minutes of arrival. In high-risk patients, AI-ECG achieved a 60.4% positive predictive value (PPV), enhancing early triage efficiency. The study also explored AI-ECG’s integration with high-sensitivity troponin and HEART scores, improving risk stratification and achieving a net reclassification improvement (NRI) of 19.6%. Importantly, AI-ECG was particularly effective in detecting ST-elevation myocardial infarction (STEMI), demonstrating 92.5% sensitivity and 99.2% NPV. This research supports the adoption of AI-ECG as a rapid, non-invasive, and highly scalable tool for early AMI detection in EDs, potentially reducing diagnostic delays and improving patient outcomes. Further real-world validation and integration into clinical workflows could revolutionize emergency cardiac care. 🔗 Read the full study: European Heart Journal

It was a pleasure to connect with Dr. David Krummen, who generously shared insights into their EP Lab and how they’re leveraging GEHC CardioLab alongside AI tools to advance care for arrhythmia patients. By unlocking the potential of ECG data, they are improving cardiac ablation outcomes, streamlining workflows, and boosting procedural efficiency.   Cardiac arrhythmias are responsible for 10% of global deaths, with over 25% of adults over 40 likely to develop a serious arrhythmia. Left untreated, arrhythmias significantly increase the risk of death and are linked to severe co-morbidities like stroke and dementia.   The role of AI in Electrophysiology (EP) labs is becoming increasingly vital in enhancing diagnostic accuracy, improving procedural success, and optimizing workflow efficiency. Here’s how AI is making an impact: Arrhythmia Detection and Classification: AI algorithms, particularly deep learning models, are now analyzing ECGs and intracardiac signals with high precision, enabling early and accurate detection of arrhythmias such as atrial fibrillation and ventricular tachycardia. Mapping and Ablation: AI-powered systems are aiding the creation of 3D electroanatomical heart maps, essential for guiding ablation procedures, by integrating and analyzing large datasets from various sources. Workflow Optimization: AI is streamlining EP lab operations by automating routine tasks like data entry and image processing, allowing clinicians to focus on patient care. It also helps predict procedure durations and optimize resource scheduling. Predictive Analytics: AI models are being used to predict procedural outcomes, assess patient risks, and support personalized treatment planning. Decision Support Systems: AI-based tools provide real-time guidance during procedures, helping clinicians make informed decisions by suggesting optimal ablation points or predicting procedure success. Research and Development: AI is accelerating electrophysiology research by analyzing large datasets, uncovering patterns, and generating new hypotheses for innovative treatments.   AI's integration into EP labs is transforming the field, driving greater precision, improving patient outcomes, and making procedures more efficient and accessible.

Many smartwatches & portable devices now have 1-lead ECG acquisition built in. We can use them for assessing rhythm problems (e.g. Afib). But can we leverage them as screening devices for structural heart diseases (SHDs) that even human experts cannot "see" on the ECG? In European Society of Cardiology's #EHJDigitalHealth, led by Arya Aminorroaya MD, MPH Cardiovascular Data Science (CarDS) Lab, we present #ADAPT_Heart - an AI-ECG tool for diagnosing a range of SHDs from 1-lead ECGs Why a multi-SHD tool? - Because in community settings, low prevalence will create a range of false positives for individual disease models, and - Each SHD component will still lead to the same final result - an Echo. The tool here is noise-adapted to account for the kind of ECGs we get from wearable devices - and is now being prospectively validated in the ID-SHD study: https://lnkd.in/ejKebReX The full text: https://lnkd.in/eJgkyXwy All kudos to our exceptional CarDS Lab team: Lovedeep Dhingra Evangelos K. Oikonomou Aline Pedroso, PhD Sumukh Vasisht Shankar Akshay Khunte Andreas Coppi Our collaborators: Harlan Krumholz Tom Ribeiro Luisa Brant Sandhi Barreto Murilo Foppa Yale School of Medicine Yale Department of Internal Medicine Universidade Federal de Minas Gerais

⭐ Deep Learning takes on heart failure: AI model offers a non-invasive future ⭐ Exciting developments on the horizon! Researchers from Massachusetts Institute of Technology and Harvard Medical School have introduced an innovative Deep Learning model, CHAIS, that could redefine how we monitor and prevent heart failure. Traditionally, invasive procedures like Right Heart Catheterization (RHC) have been the gold standard for assessing heart health. But CHAIS offers a groundbreaking alternative: a non-invasive approach using ECG signals to predict heart failure risk, with accuracy comparable to RHC. ✔️ Key Benefits: ➜ Noninvasive and convenient: Patients wear a simple patch on their chest, providing continuous monitoring without the need for hospital visits. ➜ Accurate and timely: Predicts heart health risks with impressive precision, allowing early intervention. ➜ Broad impact: Could significantly reduce hospital readmissions and ease pressure on healthcare workers. This AI-driven approach is poised to improve patient outcomes and make high-quality heart care accessible to everyone, regardless of location or socioeconomic status. 🌍❤️ #AIinHealthcare #Innovation #HeartHealth #MIT #HarvardMedicalSchool #DeepLearning #ArtificialIntelligence

Earlier this year, I witnessed how AI and machine learning can enhance patient care in cardiology in practical, impactful ways.   A speaker at the AI Cures conference at MIT shared how ML can be applied to data from minimally invasive home monitoring devices like ECGs.   A patient’s hemodynamic measures are incredibly useful in monitoring a patient, however given the equipment involved, can only be done in the hospital. With this new algorithm that was presented, the model can actually infer a patient's hemodynamic measurements, like pressures, fairly accurately from the ECG waveform data alone. I found that rather amazing. And useful!   This means patients could be monitored closely at home, with the ML model providing cardiologists with clinical indicators like pressure risks they wouldn't otherwise have without bringing the patient in.   Examples like this, where ML provides incremental advantages and empowers clinicians, excite me most about AI in healthcare.   The technology is maturing to the point where we can apply it to increase access to care, fill in gaps, and connect disparate data sources - rather than pursue AI applications for their own sake.   What other opportunities exist where AI/ML could provide an extra layer of insight to improve clinicians' abilities? I'd love to hear your ideas! #AI #artificialintelligence #codetocare

As wearable health devices grow more popular in the U.S., there’s a growing opportunity to track the broader adult population’s heart health. But it’s much harder for an Apple Watch to accurately detect the heart’s electrical signals and rhythm than a set of hospital-grade electrodes. Its readings are messier, often because of poor contact with the skin. A team of Yale researchers published a paper in Nature this month exploring a potential solution: an artificial intelligence algorithm trained on noisy electrocardiograms, electrical pulse recordings that illustrate heart function. Starting an algorithm off with this data could help it more easily adapt to the reality of imperfect wearable sensors. "You could say I’m going to find the clean, pristine-looking ECG that looks a lot more like the ECG done in a clinical setting, and only use those for diagnostics,” study author Rohan Khera told me. “But that’s not the reality. Clinical ECGs are not obtained on wristwatches.” #medicaldevices #ai #ecg #algorithms #heartdisease https://lnkd.in/eNWAtHey

Can #AI improve EKG interpretation? In this Scrubs and Software episode, I test out PMCardio by Powerful Medical, an app that analyzes EKG photos and predicts conditions like STEMI and AFib using machine learning. It’s not FDA-approved yet—but its contextual input and accuracy are worth watching. It's STEMI AI model has received FDA’s Breakthrough Device Designation, which accelerates its pathway toward regulatory approval by allowing prioritized review and closer collaboration with the agency. #AIinMedicine #EKG #HealthTech #ScrubsAndSoftware #DigitalHealth #medicine #publichealth #healthcare Simon Rovder Robert Herman, MD, PhD Felix Bauer Martin Herman Viktor Jurasek Lucia Bojkovska