Theoretical Design and Cloud-Integrated Architecture of an Affordable Electrochemical Sensor Platform (AESP) for Cancer Screening in Resource-Limited Healthcare
Abstract
Cancer remains one of the leading causes of mortality worldwide, where early-stage detection significantly improves therapeutic outcomes and survival rates. However, conventional diagnostic modalities such as imaging systems and histopathological examination are often costly, infrastructure-intensive, and inaccessible in resource-constrained healthcare environments, limiting timely screening. To address this gap, this study proposes the Affordable Electrochemical Sensor Platform (AESP)—a cloud-integrated conceptual framework designed to enable rapid and point-of-care cancer biomarker screening in low-resource settings.The AESP architecture integrates a miniaturized electrochemical sensing module with a portable acquisition interface and cloud-based analytical pipeline for biomarker signal interpretation. Rather than representing a physically fabricated device, the platform is currently presented as an architectural and computational model that outlines theoretical signal acquisition, preprocessing, and cloud-assisted decision workflows. The system emphasizes affordability, scalability, and modularity for distributed healthcare deployment. Performance was assessed using public clinical datasets to simulate biomarker classification and signal discrimination, reflecting theoretical predictive capability rather than physical electrochemical testing. Claims on reproducibility, stability, and biological resistance are based on computational modeling, not wet-lab validation. The…
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Evidence Hub reads this as a theoretical / analytical, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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