Self-Explaining Neural Networks for Transparent Parkinson's Disease Screening.

Overview

1. Cybersecurity Department, Al-Zaytoonah University of Jordan, Amman 11733, Jordan
2. Faculty of Information Science & Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia

Abstract

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• physionet.org/content/gaitpdb, at PhysioNet; found in “Data Availability Statement”

Data Availability Statement

You can access the PhysioNet GRF dataset using the following link: https://physionet.org/content/gaitpdb/1.0.0/ (accessed on 3 January 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 10 keywords, 7 MeSH terms, 1 funder, 25 references.

Cite

This paper

Farfoura, M. E., Alkhatib, A. A. A., & Connie, T. (2026). Self-Explaining Neural Networks for Transparent Parkinson's Disease Screening. Sensors (Basel, Switzerland), 26(9), 2671. https://doi.org/10.3390/s26092671

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41598-026-47769-z [code]

A multimodal explainable artificial intelligence framework for interpretable Parkinson's disease prediction.

Journal: Scientific reports

In common: Parkinson's, 2 references

[2] doi:10.1038/s41598-026-61801-2 [code]

Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.

Journal: Scientific reports

In common: Parkinson's, other, clinical / translational

[3] doi:10.3390/bioengineering13070773 [code]

From API to Action: A Multi-Model Comparison of OpenAI, Anthropic, Google, and Meta LLMs for Clinical Trial Data Extraction.

Journal: Bioengineering (Basel, Switzerland)

In common: Parkinson's, other, clinical / translational

[4] doi:10.1371/journal.pone.0348957 [code]

Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention.

Journal: PloS one

In common: Parkinson's, other, clinical / translational

[5] doi:10.1038/s41531-026-01335-6 [code]

StimVision: smartphone video kinematics to optimize DBS programming in Parkinson's disease.

Journal: NPJ Parkinson's disease

In common: Parkinson's, other, clinical / translational

[6] doi:10.3390/bios16070394

Hybrid Edge-Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors.

Journal: Biosensors

In common: other, 1 reference

[7] doi:10.1038/s41598-026-47967-9

Interaction between systemic arterial reservoir behavior and cerebrovascular vasoreactivity during acute controlled hypocapnia.

Journal: Scientific reports

In common: other, 1 reference

[8] doi:10.7554/elife.100605 [code]

Age-related changes in ‘cortical’ 1/f dynamics are linked to cardiac activity

Journal: n/a

In common: other, 1 reference

[9] doi:10.1016/j.xgen.2026.101217 [code]

ProtoCloud: A prototypical self-explaining model for single-cell analysis.

Journal: Cell genomics

In common: 1 reference

[10] doi:10.1186/s40708-026-00320-2

Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.

Journal: Brain informatics

In common: clinical / translational, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.