. 2022 Jan 1;118(1):95–102. [Article in Portuguese] doi: 10.36660/abc.20200596

Table 2. Articles on the use of machine learning in cardiology.

Article	Main results
Can machine-learning improve cardiovascular risk prediction using routine clinical data?³⁸	The algorithm was able to predict 4998 of 7404 positive cases (sensitivity 67.5%, PPV 18.4%) and 53,458 of 75,585 negative cases (specificity 70.7% and NPV 95.7%), with a gain of 355 patients (+7.6%) who developed cardiovascular diseases, compared to the traditional method.
Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data⁴³	By means of isolated analysis of ECG using a ML algorithm, it was possible to predict 1-year all-cause mortality with AUC = 0.84 and p < 0.05.
Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction⁵⁶	A group of 1273 patients with hypertension was evaluated using ML techniques, using clinical, laboratory, and echocardiography data. It was possible to identify a group of patients at a higher risk of developing heart failure with preserved ejection fraction who were likely to benefit from more intensive medical treatment.
Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy⁵⁷	They used ML techniques to differentiate constrictive pericarditis from restrictive cardiomyopathy with a ROC curve of 96.2% and accuracy greater than 90%.
Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography⁵⁸	The ML algorithm was able to detect coronary lesions greater than or equal to 25% with 93% sensitivity, 95% specificity, and 94% accuracy in 42 coronary angiographies.
A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation⁵⁴	Automatic analysis using the ML method for reading ECG in an emergency department obtained sensitivity (88.7% versus 92.0%, p < 0.086), specificity (94% versus 84.7%, p < 0.0001), PPV (88.2% versus 75.4%, p < 0.0001), and accuracy (92.2% versus 87.2%, p < 0.0001), compared to the conventional automatic method.
Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network: the CODE Study⁵³	A trained neural network was able to detect 6 classes of electrocardiographic abnormalities with specificity greater than 99% and performance greater than 80%, compared to last-year cardiology residents.
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction⁵⁵	ML software was able to detect patients with atrial fibrillation, based on ECG in sinus rhythm, with a sensitivity of 79%, specificity of 79.5%, and accuracy of 79.4%.

ECG: electrocardiogram; ML: machine learning; NPV: negative predictive value; PPV: positive predictive value.