ecg computer-based data philips dxl-16 algorithm (Philips Healthcare)
90
Structured Review
Philips Healthcare
ecg computer-based data philips dxl-16 algorithm
Ecg Computer Based Data Philips Dxl 16 Algorithm, 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/product/computer-based+algorithm/philips+dxl+16+algorithm/pmc11855451-28-24-27
Average 90 stars, based on 1 article reviews
Ecg Computer Based Data Philips Dxl 16 Algorithm, 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/product/computer-based+algorithm/philips+dxl+16+algorithm/pmc11855451-28-24-27
Average 90 stars, based on 1 article reviews
ecg computer-based data philips dxl-16 algorithm - by Bioz Stars,
2026-09
90/100 stars
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1) Product Images from "Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis"
Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis
Journal: Healthcare
doi: 10.3390/healthcare13040408
Figure Legend Snippet: Comparison of available AI applications analyzing ECG.
Techniques Used: Comparison, Biomarker Discovery, Diagnostic Assay
Figure Legend Snippet: Excluded studies.
Techniques Used:
Figure Legend Snippet: Included studies and their characteristics.
Techniques Used: Biomarker Discovery
Figure Legend Snippet: Comparison of AI models utilized for each selected study.
Techniques Used: Comparison, Activation Assay, Extraction, Biomarker Discovery, Variant Assay, Selection
Figure Legend Snippet: Comparison of AI models.
Techniques Used: Comparison
Related Articles
Selection:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Comparison:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Biomarker Discovery:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Diagnostic Assay:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Activation Assay:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Extraction:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Variant Assay:Article Title: Machine-learning computer-assisted ECG analysis to predict myocardial fibrosis in patients with hypertrophic cardiomyopathy. Article Snippet: Aims: The application of computer assisted techniques to the electrocardiogram (ECG) analysis is showing promising results.. Our main aim was to apply a machine learning approach to the ECG analysis in patients with hypertrophic cardiomyopathy (HCM), to identify predictors of macroscopic fibrosis, a marker of ventricular arrhythmias and sudden cardiac death.. Methods: 136 patients diagnosed with HCM were included. Article Title: PR interval prolongation and 1-year mortality among emergency department patients: a multicentre transnational cohort study Article Snippet: In addition to the Article Title: Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Diagnostic utility of 31 ECG criteria for predicting echocardiographic left ventricular geometry Article Snippet: The Article Title: Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram—Applicable in Clinical Practice?—Critical Literature Review with Meta-Analysis Article Snippet: Salazar, 2021 [ ] , Computer-based ECG model , 458 ECG standard and non-standard parameters; 25 mm/s velocity and 10 Article Title: Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction. Article Snippet: Introduction: Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI).. This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.. Material and methods: In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. |