Application of artificial intelligence to the electrocardiogram.
Ontology highlight
ABSTRACT:
SUBMITTER: Attia ZI
PROVIDER: S-EPMC8500024 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAutomated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence-enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists.MethodsWe developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist's final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients ≥18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n = 1,749,654), validation (n = 249,951), and testing (n = 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm's performance to the cardiologist's interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves.ResultsThe model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of ≥0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity ≥95% for all codes.ConclusionsAn AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.
| S-EPMC8890098 | biostudies-literature
Project description:ObjectiveTo rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).MethodsA global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.ResultsThe area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.ConclusionInfection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.
| S-EPMC8327278 | biostudies-literature
Project description:AimsAn artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown.Methods and resultsThe AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate-severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 ± 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group (P < 0.001). The AI-ECG was correlated with aortic valve area (ρ = -0.48, R2 = 0.20), peak velocity (ρ = 0.22, R2 = 0.08), and mean pressure gradient (ρ = 0.35, R2 = 0.08). The AI-ECG also correlated with left ventricular (LV) mass index (ρ = 0.36, R2 = 0.13), E/e' (ρ = 0.36, R2 = 0.12), and left atrium volume index (ρ = 0.42, R2 = 0.12). Neither LV ejection fraction nor stroke volume index had a significant correlation with the AI-ECG. Age correlated with the AI-ECG (ρ = 0.46, R2 = 0.22) and its correlation with echocardiography parameters was similar to that of the AI-ECG.ConclusionA combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical/functional features in the model and its identification process of AS is multifactorial.
| S-EPMC10232245 | biostudies-literature
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (≤0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness.
2012-12-10 | GSE42804 | GEO
Project description:Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
| S-EPMC6465941 | biostudies-literature
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (M-bM-^IM-$0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness. Female striped bass were sorted into groups (N=8 each) producing M-bM-^@M-^Xhigh qualityM-bM-^@M-^Y or M-bM-^@M-^Xlow qualityM-bM-^@M-^Y eggs (spawns) based upon the percentage of eggs bearing viable 4 h embryos. Spawns with >50% of eggs producing 4 h embryos were considered to be of high quality and spawns with <30% of eggs producing 4 h embryos were considered to be of low quality.
2012-12-10 | E-GEOD-42804 | biostudies-arrayexpress
Project description:AimsAn artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE).Methods and resultsWe included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02-1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93-0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG.ConclusionThe AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.
| S-EPMC8245143 | biostudies-literature
Project description:ObjectiveTo validate an artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort.MethodsWe identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment.ResultsFor detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004).ConclusionArtificial intelligence-augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.
| S-EPMC9904428 | biostudies-literature
Project description:AimsAccurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.Methods and resultsWe trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77-0.95) compared to median expert electrophysiologist accuracy of 79% (range 70-84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.ConclusionWe describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.
| S-EPMC9708023 | biostudies-literature
Project description:BackgroundPost-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown.AimsThe aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant.MethodsA retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation.ResultsThe performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction.ConclusionsA positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.
| S-EPMC10077316 | biostudies-literature