E-viri
Recenzirano
-
Demolder, Anthony; Nauwynck, Maxime; De Pauw, Michel; De Buyzere, Marc; Duytschaever, Mattias; Timmermans, Frank; De Pooter, Jan
Journal of electrocardiology, March-April 2024, 2024 Mar-Apr, 2024-03-00, 20240301, Letnik: 83Journal Article
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. 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. Sliding window-based approach improves ECG based prediction of age and sex and may aid in addressing the challenge of prediction certainty estimation. Sliding window-based artificial intelligence-enabled ECG provides an accurate and reliable estimate of prediction certainty. Display omitted
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.