Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular ejection fraction after anthracycline-based cancer therapy.

AIMS: Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (EF). Early recognition and treatment have been advocated, but robust, convenient, and cost-effective alternatives to cardiac imaging are missing. Recent developments in artificial intelligence (AI) techniques applied to electrocardiograms (ECGs) may fill this gap, but no study so far has demonstrated its merit for the detection of an abnormal EF after anthracycline therapy. METHODS AND RESULTS: Single centre consecutive cohort study of all breast cancer patients with ECG and transthoracic echocardiography (TTE) evaluation before and after (neo)adjuvant anthracycline chemotherapy. Patients with HER2-directed therapy, metastatic disease, second primary malignancy, or pre-existing cardiovascular disease were excluded from the analyses as were patients with EF decline for reasons other than anthracycline-induced cardiotoxicity. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for EFs < 50%. Of 989 consecutive female breast cancer patients, 22 developed a decline in EF attributed to anthracycline therapy over a follow-up time of 9.8 ± 4.2 years. After exclusion of patients who did not have ECGs within 90 days of a TTE, 20 cases and 683 controls remained. The AI-ECG model detected an EF < 50% and ≤ 35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively. CONCLUSION: These data support the use of AI-ECG for cardiotoxicity screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly cardiac imaging and could enable patients to monitor themselves over long periods of time.

Artificial intelligence electrocardiogram can be used to screen for an abnormal heart function after anthracycline chemotherapy, opening the door to new ways of cost-effective screening of cancer survivors at risk of cardiotoxicity over long periods of time.

Creating Ion Channel Kinetic Models Using Cloud Computing.

Computational modeling of ion channels provides key insight into experimental electrophysiology results and can be used to connect channel dynamics to emergent phenomena observed at the tissue and organ levels. However, creation of these models requires substantial mathematical and computational background. This tutorial seeks to lower the barrier to creating these models by providing an automated pipeline for creating Markov models of an ion channel kinetics dataset. We start by detailing how to encode sample voltage-clamp protocols and experimental data into the program and its implementation in a cloud computing environment. We guide the reader on how to build a containerized instance, push the machine image, and finally run the routine on cluster nodes. While providing open-source code has become more standard in computational studies, this tutorial provides unprecedented detail on the use of the program and the creation of channel models, starting from inputting the raw experimental data. © 2022 Wiley Periodicals LLC. Basic Protocol: Creation of ion channel kinetic models with a cloud computing environment Alternate Protocol: Instructions for use in a standard high-performance compute cluster.

Identification of structures for ion channel kinetic models.

Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined limited sets of various topologies for Markov models of channel dynamics. We present a systematic method for identification of all possible Markov model topologies using experimental data for two types of native voltage-gated ion channel currents: mouse atrial sodium currents and human left ventricular fast transient outward potassium currents. Successful models identified with this approach have certain characteristics in common, suggesting that aspects of the model topology are determined by the experimental data. Incorporating these channel models into cell and tissue simulations to assess model performance within protocols that were not used for training provided validation and further narrowing of the number of acceptable models. The success of this approach suggests a channel model creation pipeline may be feasible where the structure of the model is not specified a priori.

Mechanisms and models of cardiac sodium channel inactivation.

Shortly after cardiac Na+ channels activate and initiate the action potential, inactivation ensues within milliseconds, attenuating the peak Na+ current, INa, and allowing the cell membrane to repolarize. A very limited number of Na+ channels that do not inactivate carry a persistent INa, or late INa. While late INa is only a small fraction of peak magnitude, it significantly prolongs ventricular action potential duration, which predisposes patients to arrhythmia. Here, we review our current understanding of inactivation mechanisms, their regulation, and how they have been modeled computationally. Based on this body of work, we conclude that inactivation and its connection to late INa would be best modeled with a "feet-on-the-door" approach where multiple channel components participate in determining inactivation and late INa. This model reflects experimental findings showing that perturbation of many channel locations can destabilize inactivation and cause pathological late INa.

Quantifying functional group interactions that determine urea effects on nucleic acid helix formation.

Urea destabilizes helical and folded conformations of nucleic acids and proteins, as well as protein-nucleic acid complexes. To understand these effects, extend previous characterizations of interactions of urea with protein functional groups, and thereby develop urea as a probe of conformational changes in protein and nucleic acid processes, we obtain chemical potential derivatives (µ23 = dµ2/dm3) quantifying interactions of urea (component 3) with nucleic acid bases, base analogues, nucleosides, and nucleotide monophosphates (component 2) using osmometry and hexanol-water distribution assays. Dissection of these µ23 values yields interaction potentials quantifying interactions of urea with unit surface areas of nucleic acid functional groups (heterocyclic aromatic ring, ring methyl, carbonyl and phosphate O, amino N, sugar (C and O); urea interacts favorably with all these groups, relative to interactions with water. Interactions of urea with heterocyclic aromatic rings and attached methyl groups (as on thymine) are particularly favorable, as previously observed for urea-homocyclic aromatic ring interactions. Urea m-values determined for double helix formation by DNA dodecamers near 25 °C are in the range of 0.72-0.85 kcal mol(-1)m(-1) and exhibit little systematic dependence on nucleobase composition (17-42% GC). Interpretation of these results using the urea interaction potentials indicates that extensive (60-90%) stacking of nucleobases in the separated strands in the transition region is required to explain the m-value. Results for RNA and DNA dodecamers obtained at higher temperatures, and literature data, are consistent with this conclusion. This demonstrates the utility of urea as a quantitative probe of changes in surface area (ΔASA) in nucleic acid processes.