Wearable and Artificial-Intelligence-Enabled Multimodal Systems for Early Detection and Classification of Acute Myocardial Infarction: A Review of the Integration of Clinical, Electrocardiographic, and Echocardiographic Data

Abstract

Acute myocardial infarction (AMI) — comprising ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) — remains the leading cause of cardiovascular death worldwide, and rapid, accurate differentiation between its subtypes is essential for time-critical treatment decisions. Conventional diagnosis relies on a combination of clinical assessment, 12-lead electrocardiography (ECG), cardiac biomarkers, and echocardiography, each of which carries recognized diagnostic limitations when used in isolation. Over the past decade, wearable electrocardiographic devices, point-of-care high-sensitivity troponin assays, handheld artificial-intelligence (AI)-assisted echocardiography, and multimodal deep-learning fusion architectures have matured sufficiently to be considered building blocks of an integrated, low-cost diagnostic pathway suitable for emergency, pre-hospital, and resource-constrained settings. This review synthesizes evidence from cardiology, biomedical engineering, and digital-health literature on (i) the epidemiological burden and diagnostic paradigm of AMI, (ii) the diagnostic performance of wearable ECG and point-of-care biomarker technologies, (iii) artificial-intelligence approaches to ECG- and echocardiography-based MI detection, (iv) multimodal data-fusion strategies, and (v) the specific opportunities and gaps relevant to low- and middle-income countries such as Uganda. Across the reviewed literature, deep-learning models applied to single- or multi-lead ECG achieve areas under the receiver-operating-characteristic curve generally exceeding 0.90 for MI detection, wearable smartwatch electrocardiograms achieve sensitivities of 83–100% and specificities of 79–100% for arrhythmia and ST-segment change detection under supervised conditions, and point-of-care high-sensitivity troponin assays achieve diagnostic accuracy comparable to central-laboratory assays with substantially shorter turnaround times. However, multimodal fusion of clinical, ECG, and echocardiographic streams into a single wearable STEMI/NSTEMI classifier remains largely unrealized in the peer-reviewed literature, and evidence from sub-Saharan African populations is markedly under-represented. We propose a conceptual framework for an integrated wearable multimodal diagnostic system and outline the technical, clinical, regulatory, and ethical considerations that must be addressed before such systems can be safely deployed in emergency and pre-hospital cardiac care in low-resource settings.