Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study

Yelda Fırat; Meral Seferoğlu; Hakan Kılıçaslan; Ali Özhan Sıvacı; Murat Kaan Yılmaz; Yılmaz Kılıçaslan

doi:10.18621/eurj.1794309

Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study

Abstract

Objectives: Multiple sclerosis (MS) is a chronic central nervous system disorder that causes demyelination, inflammation, and axonal damage, leading to permanent disabilities in motor, sensory, visual, and balance functions. This study aimed to develop an artificial intelligence (AI)-based, non-invasive diagnostic approach for MS detection using handwriting analysis, leveraging deep learning methods to identify disease-specific handwriting patterns.

Methods: A classification model was designed using a convolutional neural network (CNN) based on the VGG16 architecture with transfer learning. The dataset consisted of 426 handwriting samples, including 213 from MS patients and 213 from healthy individuals. Data augmentation and early stopping techniques were employed to improve model generalization capability.

Results: The proposed model achieved a validation accuracy of 83.72% and a test accuracy of 85%, indicating its robustness in distinguishing MS patients from healthy subjects. The confusion matrix analysis demonstrated a sensitivity of 86% and a specificity of 84%, indicating moderate discriminatory performance.

Conclusions: The findings suggest that the developed AI-based model offers an effective, non-invasive diagnostic tool for MS detection. This approach provides a promising foundation for future research on monitoring disease progression and developing clinically applicable AI-supported diagnostic systems.

Keywords

Ethical Statement

This study was approved by the University of Health Sciences Bursa Yüksek Training and Research Hospital Medical Sciences Ethics Committee (Decision No: 2024-TBEK 2024/11-12; date: 06.11.2024). All procedures were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments. All participants provided informed consent before inclusion, confirming their understanding and willingness to participate under clearly defined conditions.

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Yelda Fırat ^*
0009-0003-8365-1000
Türkiye

Meral Seferoğlu
0000-0003-3858-0306
Türkiye

Hakan Kılıçaslan
0009-0003-8579-0442
Türkiye

Ali Özhan Sıvacı
0000-0002-9697-9510
Türkiye

Murat Kaan Yılmaz
0009-0008-4552-5253
Türkiye

Yılmaz Kılıçaslan
0000-0002-5020-6547
Türkiye

Early Pub Date

October 31, 2025

Publication Date

November 4, 2025

Submission Date

September 30, 2025

Acceptance Date

October 30, 2025

Published in Issue

Year 2025 Volume: 11 Number: 6

DOI

https://doi.org/10.18621/eurj.1794309

IZ

https://izlik.org/JA73JP56XU

Cite

RIS / Bibtex

APA

Fırat, Y., Seferoğlu, M., Kılıçaslan, H., Sıvacı, A. Ö., Yılmaz, M. K., & Kılıçaslan, Y. (2025). Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study. The European Research Journal, 11(6), 1213-1226. https://doi.org/10.18621/eurj.1794309

AMA

1.Fırat Y, Seferoğlu M, Kılıçaslan H, Sıvacı AÖ, Yılmaz MK, Kılıçaslan Y. Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study. Eur Res J. 2025;11(6):1213-1226. doi:10.18621/eurj.1794309

Chicago

Fırat, Yelda, Meral Seferoğlu, Hakan Kılıçaslan, Ali Özhan Sıvacı, Murat Kaan Yılmaz, and Yılmaz Kılıçaslan. 2025. “Artificial Intelligence-Based Handwriting Analysis for Non-Invasive Multiple Sclerosis Detection: A Preliminary Study”. The European Research Journal 11 (6): 1213-26. https://doi.org/10.18621/eurj.1794309.

EndNote

Fırat Y, Seferoğlu M, Kılıçaslan H, Sıvacı AÖ, Yılmaz MK, Kılıçaslan Y (November 1, 2025) Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study. The European Research Journal 11 6 1213–1226.

IEEE

[1]Y. Fırat, M. Seferoğlu, H. Kılıçaslan, A. Ö. Sıvacı, M. K. Yılmaz, and Y. Kılıçaslan, “Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study”, Eur Res J, vol. 11, no. 6, pp. 1213–1226, Nov. 2025, doi: 10.18621/eurj.1794309.

ISNAD

Fırat, Yelda - Seferoğlu, Meral - Kılıçaslan, Hakan - Sıvacı, Ali Özhan - Yılmaz, Murat Kaan - Kılıçaslan, Yılmaz. “Artificial Intelligence-Based Handwriting Analysis for Non-Invasive Multiple Sclerosis Detection: A Preliminary Study”. The European Research Journal 11/6 (November 1, 2025): 1213-1226. https://doi.org/10.18621/eurj.1794309.

JAMA

1.Fırat Y, Seferoğlu M, Kılıçaslan H, Sıvacı AÖ, Yılmaz MK, Kılıçaslan Y. Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study. Eur Res J. 2025;11:1213–1226.

MLA

Fırat, Yelda, et al. “Artificial Intelligence-Based Handwriting Analysis for Non-Invasive Multiple Sclerosis Detection: A Preliminary Study”. The European Research Journal, vol. 11, no. 6, Nov. 2025, pp. 1213-26, doi:10.18621/eurj.1794309.

Vancouver

1.Yelda Fırat, Meral Seferoğlu, Hakan Kılıçaslan, Ali Özhan Sıvacı, Murat Kaan Yılmaz, Yılmaz Kılıçaslan. Artificial intelligence-based handwriting analysis for non-invasive multiple sclerosis detection: A preliminary study. Eur Res J. 2025 Nov. 1;11(6):1213-26. doi:10.18621/eurj.1794309