{"id":"660a72907ccb","type":"article","url":"https://hartvaat.nl/2020/08/25/machine-learning-voor-lv-diastolische-functie-op-ecg/","title":"Machine learning voor LV diastolische functie op ECG","title_en":"Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.","category":"hartfalen","category_label":"Hartfalen","professions":["cardioloog"],"tags":["ai-ecg","elektrocardiografie","kunstmatige-intelligentie"],"journal":"Journal of the American College of Cardiology","doi":"10.1016/j.jacc.2020.06.061","source_url":"https://doi.org/10.1016/j.jacc.2020.06.061","authors":["Nobuyuki Kagiyama","Marco Piccirilli","Naveena Yanamala","Sirish Shrestha","Peter D Farjo","Grace Casaclang-Verzosa","Wadea M Tarhuni","Negin Nezarat","Matthew J Budoff","Jagat Narula","Partho P Sengupta"],"significance":6,"published":"2020-08-25","source_date":"2020-08-25","image":"","kennis":[],"congress":"","summary_en":"This study used machine learning applied to standard ECG features to assess left ventricular diastolic function, developing an AI-based screening tool for identifying diastolic dysfunction without echocardiography.","created":"2026-07-03T10:28:44Z","updated":"2026-07-03T13:27:53Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Studie die machine learning toepaste op ECG-kenmerken voor beoordeling van LV diastolische functie.","abstract_original":"BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise. OBJECTIVES: This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction. METHODS: A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability. RESULTS: Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e') measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated e' discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated e' allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively). CONCLUSIONS: A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients."}