LAUSR

Our previous work demonstrated the diagnostic value of Artificial Intelligence (AI) -driven algorithms for ST-Elevation Myocardial Infarction (STEMI). In the present research, we explore the importance of demographic data inclusion, in order to achieve a more accurate diagnosis. To demonstrate that incorporation of demographic variables into the sample records will augment the accuracy of AI-based protocols for STEMI diagnosis. An observational, retrospective, case-control study. Demographic data (age and gender) male/female ratio 1.3, ages 98–18 years was added to the sample records. Sample: 8,511 EKG records, previously diagnosed as normal, abnormal (over 200 conditions) or STEMI. Records excluded other patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (90,592 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample was used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with Nvidia GTX 1070GPU, 8GB RAM. The model yielded an accuracy of 97.1%, a sensitivity of 96.8%, and a specificity of 97.5%. The ability of AI-guided algorithms to diagnose STEMI is increased by expanding the morphological variables with demographic data. This approach may be applied to improve the EKG diagnosis of other cardiovascular entities and improve clinical management.