Auditory Transduction of Protein Misfolding for Lightweight Alzheimer’s Detection

Early identification of neurodegenerative disorders such as Alzheimer’s disease remains a critical clinical challenge, heavily constrained by the extreme computational burden of analyzing subtle, early-stage structural changes in amyloid-β (Aβ) protein misfolding. Existing geometric deep-learning archi- tectures, such as 3D Graph Neural Networks and Equivariant GNNs, operate directly on three-dimensional atomic coordinates and require quadratic spatial attention overhead, making high- throughput population screening computationally intractable. To solve this scalability bottleneck, we propose Diagnostic Sonifica- tion, a novel cross-modal machine learning framework that con- verts protein structural dynamics into a continuous microtonal auditory representation prior to classification. Given molecular- dynamics trajectories of Aβ1−42, a cross-modal Transformer encoder extracts latent topological features from spatial atomic coordinates and backbone dihedral angles (ϕ,ψ). A neural decoder maps these latent representations to continuous audio signals structured by microtonal scales, where toxic oligomeric misfolding manifests as distinct patterns of harmonic acoustic dissonance. A Short-Time Fourier Transform (STFT) generates two-dimensional time-frequency spectrograms, which are subse- quently processed by a lightweight 2D Convolutional Neural Net- work (CNN) to detect anomalous structural folding. Preliminary experiments on simulated Aβ1−42 monomeric and oligomeric trajectories achieve an accuracy of 94.6% and an F1-score of 0.94. Crucially, our audio transduction framework requires only 4.3 GFLOPs and 35.8 ms inference latency, yielding a 4.3× reduction in computational complexity and 15% lower latency compared to 3D-GCN baselines. These results demonstrate that converting spatial geometry into time-frequency acoustics pre- serves diagnostically vital structural information while drastically lowering computational overhead, offering a scalable foundation for decentralized Alzheimer’s screening.