首页|Ghent University Hospital Reports Findings in Artificial Intelligence (Prediction of certainty in artificial intelligence-enabled electrocardiography)
Ghent University Hospital Reports Findings in Artificial Intelligence (Prediction of certainty in artificial intelligence-enabled electrocardiography)
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New research on Artificial Intelligence is the subject of a report. According to news reporting from Ghent, Belgium, by NewsRx journalists, research stated, “The 12-lead ECG provides an excellent substrate for artificial intelligence (AI) enabled prediction of various cardiovascular diseases. However, a measure of prediction certainty is lacking.” The news correspondents obtained a quote from the research from Ghent University Hospital, “To assess a novel approach for estimating certainty of AI-ECG predictions. Two convolutional neural networks (CNN) were developed to predict patient age and sex. Model 1 applied a 5 s sliding time-window, allowing multiple CNN predictions. The consistency of the output values, expressed as interquartile range (IQR), was used to estimate prediction certainty. Model 2 was trained on the full 10s ECG signal, resulting in a single CNN point prediction value. Performance was evaluated on an internal test set and externally validated on the PTB-XL dataset. Both CNNs were trained on 269,979 standard 12-lead ECGs (82,477 patients). Model 1 showed higher accuracy for both age and sex prediction (mean absolute error, MAE 6.9 ± 6.3 years vs. 7.7 ± 6.3 years and AUC 0.946 vs. 0.916, respectively, P<0.001 for both). The IQR of multiple CNN output values allowed to differentiate between high and low accuracy of ECG based predictions (P <0.001 for both). Among 10% of patients with narrowest IQR, sex prediction accuracy increased from 65.4% to 99.2%, and MAE of age prediction decreased from 9.7 to 4.1 years compared to the 10% with widest IQR. Accuracy and estimation of prediction certainty of model 1 remained true in the external validation dataset.”
GhentBelgiumEuropeArtificial IntelligenceDiagnosisDiagnostic Techniques and ProceduresElectrocardiographyEmerging TechnologiesHealth and MedicineHeart Function TestsMachine Learning
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