Evaluating the Feasibility of AI-Enhanced Electrical Impedance Tomography for Neurocysticercosis Detection

This study evaluates the preliminary diagnostic feasibility of an AI-enhanced Electrical Impedance Tomography (EIT) framework to detect neurocysticercosis (NCC)-like anomalies under simulated low-resource constraints. Using synthetic phantom slices modeling the human brain, we evaluate an automated engineering pipeline that encompasses boundary simulation under 30 dB Gaussian noise, Jacobian-based linearized difference reconstruction, and deep convolutional neural network (CNN) optimization for structural image refinement and hard-mask semantic parsing. Quantitative evaluations demonstrate robust stability in the forward measurements and high probabilistic discrimination, yielding a receiver operating characteristic area under the curve (ROC AUC) of 0.861, highlighting the profound potential of affordable, non-ionizing triage tools in medical environments suffering from acute neuroimaging deficits.