Yonsei Med J.  2024 Mar;65(3):174-180. 10.3349/ymj.2023.0341.

Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis

• KMID: 2553358
• DOI: http://doi.org/10.3349/ymj.2023.0341

Abstract

Purpose
Prehospital telecardiology facilitates early ST-elevation myocardial infarction (STEMI) detection, yet its widespread implementation remains challenging. Extracting digital STEMI biomarkers from printed electrocardiograms (ECGs) using phone cameras could offer an affordable and scalable solution. This study assessed the feasibility of this approach with real-world prehospital ECGs.
Materials and Methods
Patients suspected of having STEMI by emergency medical technicians (EMTs) were identified from a policy research dataset. A deep learning-based ECG analyzer (QCG^TM analyzer) extracted a STEMI biomarker (qSTEMI) from prehospital ECGs. The biomarker was compared to a group of human experts, including five emergency medical service directors (boardcertified emergency physicians) and three interventional cardiologists based on their consensus score (number of participants answering “yes” for STEMI). Non-inferiority of the biomarker was tested using a 0.100 margin of difference in sensitivity and specificity.
Results
Among 53 analyzed patients (24 STEMI, 45.3%), the area under the receiver operating characteristic curve of qSTEMI and consensus score were 0.815 (0.691–0.938) and 0.736 (0.594–0.879), respectively (p=0.081). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of qSTEMI were 0.750 (0.583–0.917), 0.862 (0.690–0.966), 0.826 (0.679– 0.955), and 0.813 (0.714–0.929), respectively. For the consensus score, sensitivity, specificity, PPV, and NPV were 0.708 (0.500– 0.875), 0.793 (0.655–0.966), 0.750 (0.600–0.941), and 0.760 (0.655–0.880), respectively. The 95% confidence interval of sensitivity and specificity differences between qSTEMI and consensus score were 0.042 (-0.099–0.182) and 0.103 (-0.043–0.250), respectively, confirming qSTEMI’s non-inferiority.
Conclusion
The digital STEMI biomarker, derived from printed prehospital ECGs, demonstrated non-inferiority to expert consensus, indicating a promising approach for enhancing prehospital telecardiology.

Keyword

Prehospital emergency care; emergency medical service; STEMI; deep learning; ECG