Leonie Brumm, Elisabeth Rathgeb-Schnierer
The relationship between accuracy in numerosity estimation, math achievement, and math interest in primary school students
Frontiers in psychology
electrocardiogram ecg widely considered primary test evaluating cardiovascular diseases however use artificial intelligence ai advance medical practices learn new clinical insights ecgs remains largely unexplored hypothesize ai models specific design provide finegrained interpretation ecgs advance cardiovascular diagnosis stratify mortality risks identify new clinically useful information utilizing data set 2 322 513 ecgs collected 1 558 772 patients 7 years followup developed deeplearning model stateoftheart granularity interpretable diagnosis cardiac abnormalities gender identification hypertension screening solely ecgs used stratify risk mortality model achieved area receiver operating characteristic curve auc scores 0998 95 confidence interval ci 09950999 0964 95 ci 09630965 0839 95 ci 08370841 three diagnostic tasks separately using ecgpredicted results find high risks mortality subjects sinus tachycardia adjusted hazard ratio hr 224 196257 atrial fibrillation adjusted hr 222 199248 use salient morphologies produced deeplearning model identify key ecg leads achieved similar performance three diagnoses find v1 ecg lead important hypertension screening mortality risk stratification hypertensive cohorts auc 0816 08140818 univariate hr 170 161179 two tasks separately using ecgs alone developed model showed cardiologistlevel accuracy interpretable cardiac diagnosis advancement mortality risk stratification addition demonstrated potential facilitate clinical knowledge discovery gender hypertension detection readily available