Automatic Interpretation of RPR Tests Using Lightweight Hybrid Architectures for Binary and Ternary Classification: A Preliminary, Single-Device Proof-of-Concept Study

Abstract

This study evaluates a lightweight, edge-deployable artificial intelligence pipeline to assist, not replace, trained human readers in the classification of RPR test reactions. Two separate and non-directly comparable experimental configurations were investigated: a binary task (Reactive vs. Non-Reactive) using 243 original images and a ternary task (Reactive, Minimally Reactive, Non-Reactive) using a distinct dataset of 293 original images. Because the datasets were acquired using a single device and laboratory protocol, and because deterministic augmentation generates highly correlated transformations rather than inde pendent clinical samples, the reported results should be interpreted as preliminary internal evidence of feasibility rather than proof of clinical generalizability. In the augmented internal test evaluation, the binary model achieved 99.98% accuracy (25,137/25,200), while the ternary model achieved 91.12% accuracy (14,417/15,822). In the original-image deploy ment evaluation, binary performance remained 100% (58/58) across FP32, FP16, and INT8; ternary performance was preserved under FP32/FP16 at 95.24% (80/84) but decreased to 76.19% (64/84) after INT8 quantization. An additional stochastic augmentation experiment for ternary INT8 deployment restored performance to 95.24% (80/84) and 0.9444 Macro-F1, but external validation remains mandatory before any clinical adoption.