BioMedInformatics

Journal Menu

Journal Browser

► Journal Browser

BioMedInformatics, Volume 6, Issue 4 (August 2026) – 2 articles

Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
You may sign up for e-mail alerts to receive table of contents of newly released issues.
PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.

19 pages, 1470 KB

Open AccessArticle

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

by Enmanuel Abilheira, Bruno Silva, Ljiljana Dukanovic, Afonso Pinheiro and Vitor Carvalho

BioMedInformatics 2026, 6(4), 39; https://doi.org/10.3390/biomedinformatics6040039 (registering DOI) - 24 Jun 2026

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 independent 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 deployment 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. Full article

► Show Figures

Figure 1

39 pages, 4730 KB

Open AccessArticle

EEG Slope Entropy and Affective Self-Report Fusion for Cognitive Workload Classification: A Multi-Stage Pipeline with Explainable AI Evaluation

by Mahdy Kouka and Bujar Raufi

BioMedInformatics 2026, 6(4), 38; https://doi.org/10.3390/biomedinformatics6040038 (registering DOI) - 23 Jun 2026

Abstract

Classifying cognitive workload (CWL) from neurophysiological signals remains a central challenge in affective computing. We present a multi-stage pipeline fusing EEG Slope Entropy (SlpEn;

M = 3

δ = 0.001

γ = 1.0

, 1-s window) on [...] Read more.

Classifying cognitive workload (CWL) from neurophysiological signals remains a central challenge in affective computing. We present a multi-stage pipeline fusing EEG Slope Entropy (SlpEn;

M = 3

δ = 0.001

γ = 1.0

, 1-s window) on the DEAP corpus, evaluating five affective dimensions (Valence, Arousal, Dominance, Liking, Familiarity) individually and across all ten pairwise combinations. Random Forest (RF) and XGBoost classifiers were assessed with 5-fold stratified cross-validation on a binary HIGH/LOW CWL task derived from a disjunctive threshold rule over Arousal and Dominance. Results are, therefore, reported separately for rule-constituentand non-constituent features. Arousal (RF: 81.48%, AUC: 0.896) and Dominance (71.64%, AUC: 0.811) attain the highest apparent accuracies but largely reconstruct the labelling rule. Among non-constituent dimensions, Valence is the strongest legitimate predictor (RF: 64.14%, AUC: 0.684), followed by Liking (58.75%) and Familiarity (57.93%). Slope entropy adds 3.6–4.1 pp over the strongest affective baselines and up to 23.4 pp over the SlpEn-alone baseline, with complete insensitivity to blend weighting. The Arousal + Dominance pair (RF: 99.84%, AUC: 1.000) fully reconstructs the rule and is excluded from substantive interpretation. Valence + Arousal reaches 87.27% but remains partially rule-inflated. All results are reported as mean with 95%. Full article

Journal Menu

Journal Browser

BioMedInformatics, Volume 6, Issue 4 (August 2026) – 2 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI