A Conceptual Multimodal Wearable Platform for Acute Myocardial Infarction Risk Estimation Using Clinical, Electrocardiographic and Echocardiographic Data
Acute myocardial infarction (AMI) is a time-critical cardiovascular emergency in which early recognition of high-risk ischemic patterns can influence treatment decisions and access to timely reperfusion. Current assessment relies mainly on clinical evaluation, 12-lead electrocardiography (ECG), serial high-sensitivity cardiac troponin (hs-cTn) measurements, and, where indicated, cardiac imaging. While these approaches provide complementary diagnostic information, delays in obtaining and interpreting investigations, limited laboratory and specialist capacity, and difficulty recognizing subtle or initially non-diagnostic ischemic changes can delay appropriate care. These challenges are particularly relevant in pre-hospital and resource-constrained settings. This review examines clinical, technological, and engineering evidence relevant to the development of a conceptual multimodal wearable platform for early recognition and risk estimation of AMI, with emphasis on STEMI-pattern ischemia, suspected NSTEMI, and occlusion myocardial infarction (OMI). The review also develops a proposed system architecture, preliminary engineering requirements, a quality-aware approach to multimodal data fusion, and a staged pathway for development and validation. Evidence was synthesized from the supplied reference set, clinical guidelines, systematic reviews, studies of wearable ECG systems, AI-assisted ECG analysis, artificial-intelligence applications in echocardiography, and emerging transdermal biomarker technologies. Established clinical practices were considered separately from technologies that remain at an experimental or early-development stage. The reviewed evidence suggests that the proposed modalities could provide complementary information rather than replace existing diagnostic methods. Clinical variables can help establish pre-test probability, while ECG provides immediate information on cardiac electrical activity. Echocardiography can add mechanical evidence, including regional wall-motion abnormalities, whereas emerging optical and transdermal technologies may eventually provide biochemical information. Wearable ECG systems offer potential for continuous or on-demand monitoring, although differences in lead configuration, motion artifacts, signal-quality variation, and electrode placement limit their direct equivalence to standard 12-lead ECG. Portable echocardiography is more realistically suited to episodic, connected assessment than to integration into a conventional wrist-worn device. Similarly, although artificial intelligence may improve recognition of clinically relevant patterns, issues relating to generalizability, calibration, missing data, false alerts, uncertainty, and prospective clinical validation remain unresolved. Based on the current evidence, the most realistic near-term application is a modular multimodal decision-support platform that produces calibrated risk estimates and reports signal quality alongside the available physiological measurements. Such a system should complement, rather than replace, clinical assessment, standard 12-lead ECG, serial hs-cTn testing, and indicated cardiac imaging. The proposed framework therefore provides a practical foundation for prototype development, analytical performance testing, silent-mode prospective evaluation, external validation, and subsequent clinical outcome studies. It also avoids assuming a level of technological and clinical maturity that has not yet been demonstrated.