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automatic ecg interpretation  (Philips Healthcare)

 
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    Structured Review

    Philips Healthcare automatic ecg interpretation
    Summary of model performance as the area under the receiver operating characteristic curve for predicting pericarditis. The ROC curves were made by the predictions of the deep learning model (DLM) using raw <t>ECG</t> signals and the XGB model integrating ECG measures (8 numerical values and 31 diagnostic labels), respectively. Each point represents the performance of humans and Philips automatic ECG interpretation. The cut points of the DLM and XGB model were based on Youden’s index in the tuning set.
    Automatic Ecg Interpretation, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/automatic ecg interpretation/product/Philips Healthcare
    Average 90 stars, based on 1 article reviews
    automatic ecg interpretation - by Bioz Stars, 2026-06
    90/100 stars

    Images

    1) Product Images from "A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram"

    Article Title: A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

    Journal: Journal of Personalized Medicine

    doi: 10.3390/jpm12071150

    Summary of model performance as the area under the receiver operating characteristic curve for predicting pericarditis. The ROC curves were made by the predictions of the deep learning model (DLM) using raw ECG signals and the XGB model integrating ECG measures (8 numerical values and 31 diagnostic labels), respectively. Each point represents the performance of humans and Philips automatic ECG interpretation. The cut points of the DLM and XGB model were based on Youden’s index in the tuning set.
    Figure Legend Snippet: Summary of model performance as the area under the receiver operating characteristic curve for predicting pericarditis. The ROC curves were made by the predictions of the deep learning model (DLM) using raw ECG signals and the XGB model integrating ECG measures (8 numerical values and 31 diagnostic labels), respectively. Each point represents the performance of humans and Philips automatic ECG interpretation. The cut points of the DLM and XGB model were based on Youden’s index in the tuning set.

    Techniques Used: Diagnostic Assay

    3-day CV- and non-CV-caused hospitalization in non-pericarditis cases stratified by DLM classification. DLM identification was defined as the intersection of DLM-pericarditis and DLM-STEMI. A higher risk of 3-day CV-caused hospitalization was present when the DLM defined the ECG as abnormal compared with those who were classified as having a normal ECG by DLM. The numbers reported in the legend are the hazard ratios.
    Figure Legend Snippet: 3-day CV- and non-CV-caused hospitalization in non-pericarditis cases stratified by DLM classification. DLM identification was defined as the intersection of DLM-pericarditis and DLM-STEMI. A higher risk of 3-day CV-caused hospitalization was present when the DLM defined the ECG as abnormal compared with those who were classified as having a normal ECG by DLM. The numbers reported in the legend are the hazard ratios.

    Techniques Used:



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    automatic ecg interpretation  (Philips Healthcare)
    90
    Philips Healthcare automatic ecg interpretation
    Summary of model performance as the area under the receiver operating characteristic curve for predicting pericarditis. The ROC curves were made by the predictions of the deep learning model (DLM) using raw <t>ECG</t> signals and the XGB model integrating ECG measures (8 numerical values and 31 diagnostic labels), respectively. Each point represents the performance of humans and Philips automatic ECG interpretation. The cut points of the DLM and XGB model were based on Youden’s index in the tuning set.
    Automatic Ecg Interpretation, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/automatic ecg interpretation/product/Philips Healthcare
    Average 90 stars, based on 1 article reviews
    automatic ecg interpretation - by Bioz Stars, 2026-06
    90/100 stars
    		  Buy from Supplier

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    Summary of model performance as the area under the receiver operating characteristic curve for predicting pericarditis. The ROC curves were made by the predictions of the deep learning model (DLM) using raw ECG signals and the XGB model integrating ECG measures (8 numerical values and 31 diagnostic labels), respectively. Each point represents the performance of humans and Philips automatic ECG interpretation. The cut points of the DLM and XGB model were based on Youden’s index in the tuning set.

    Journal: Journal of Personalized Medicine

    Article Title: A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

    doi: 10.3390/jpm12071150

    Figure Lengend Snippet: Summary of model performance as the area under the receiver operating characteristic curve for predicting pericarditis. The ROC curves were made by the predictions of the deep learning model (DLM) using raw ECG signals and the XGB model integrating ECG measures (8 numerical values and 31 diagnostic labels), respectively. Each point represents the performance of humans and Philips automatic ECG interpretation. The cut points of the DLM and XGB model were based on Youden’s index in the tuning set.

    Article Snippet: The AUCs were 0.954 and 0.952 in the validation dataset and chest pain subset, respectively, with the same sensitivities of 78.9% and similar specificities of 97.7% and 97.6%, which were significantly better than the XGB model and Philips automatic ECG interpretation.

    Techniques: Diagnostic Assay

    3-day CV- and non-CV-caused hospitalization in non-pericarditis cases stratified by DLM classification. DLM identification was defined as the intersection of DLM-pericarditis and DLM-STEMI. A higher risk of 3-day CV-caused hospitalization was present when the DLM defined the ECG as abnormal compared with those who were classified as having a normal ECG by DLM. The numbers reported in the legend are the hazard ratios.

    Journal: Journal of Personalized Medicine

    Article Title: A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

    doi: 10.3390/jpm12071150

    Figure Lengend Snippet: 3-day CV- and non-CV-caused hospitalization in non-pericarditis cases stratified by DLM classification. DLM identification was defined as the intersection of DLM-pericarditis and DLM-STEMI. A higher risk of 3-day CV-caused hospitalization was present when the DLM defined the ECG as abnormal compared with those who were classified as having a normal ECG by DLM. The numbers reported in the legend are the hazard ratios.

    Article Snippet: The AUCs were 0.954 and 0.952 in the validation dataset and chest pain subset, respectively, with the same sensitivities of 78.9% and similar specificities of 97.7% and 97.6%, which were significantly better than the XGB model and Philips automatic ECG interpretation.

    Techniques: