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

Year 2025, Volume: 11 Issue: 6, 1213 - 1226, 04.11.2025

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

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

https://izlik.org/JA73JP56XU

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

Multiple sclerosis , handwriting , VGG16 , Transfer learning , convolutional neural network

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.

References

• 1. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv. 2014;1409.1556. doi: 10.48550/arXiv.1409.1556.
• 2. Shoeibi A, Khodatars M, Jafari M, et al. Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: a review. Comput Biol Med. 2021;136:104697. doi:10.1016/j.compbiomed.2021.104697.
• 3. Wahlig SG, Nedelec P, Weiss DA, Rudie JD, Sugrue LP, Rauschecker AM. 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies. Front Neurosci. 2023;17:1188336. doi: 10.3389/fnins.2023.1188336.
• 4. Ekmekyapar T, Taşcı B. Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis. Diagnostics (Basel). 2023;13(19):3030. doi: 10.3390/diagnostics13193030.
• 5. Ebers GC. Environmental factors and multiple sclerosis. Lancet Neurol. 2008;7(3):268-277. doi: 10.1016/S1474-4422(08)70042-5.
• 6. Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278-286. doi:10.1212/WNL.0000000000000560.
• 7. Walton C, King R, Rechtman L, et al. Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS, third edition. Mult Scler. 2020;26(14):1816-1821. doi:10.1177/1352458520970841.
• 8. Oh J, Vidal-Jordana A, Montalban X. Multiple sclerosis: clinical aspects. Curr Opin Neurol. 2018;31(6):752-759. doi:10.1097/WCO.0000000000000622.
• 9. Compston A, Coles A. Multiple sclerosis. Lancet. 2008;372(9648):1502-1517. doi:10.1016/S0140-6736(08)61620-7.
• 10. Miller AE, Vermersch P, Kappos L, et al; TOPIC study group. Long-term outcomes with teriflunomide in patients with clinically isolated syndrome: Results of the TOPIC extension study. Mult Scler Relat Disord. 2019;33:131-138. doi: 10.1016/j.msard.2019.05.014.
• 11. García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal. 2013;17(1):1-18. doi: 10.1016/j.media.2012.09.004.
• 12. Hartmann M, Fenton N, Dobson R. Current review and next steps for artificial intelligence in multiple sclerosis risk research. Comput Biol Med. 2021;132:104337. doi: 10.1016/j.compbiomed.2021.104337.
• 13. Harirchian MH, Fatehi F, Sarraf P, Honarvar NM, Bitarafan S. Worldwide prevalence of familial multiple sclerosis: A systematic review and meta-analysis. Mult Scler Relat Disord. 2018;20:43-47. doi: 10.1016/j.msard.2017.12.015.
• 14. Goldenberg MM. Multiple sclerosis review. P T. 2012r;37(3):175-814.
• 15. Kobelt G, Thompson A, Berg J, Gannedahl M, Eriksson J; MSCOI Study Group; European Multiple Sclerosis Platform. New insights into the burden and costs of multiple sclerosis in Europe. Mult Scler. 2017;23(8):1123-1136. doi: 10.1177/1352458517694432.
• 16. Kaisey M, Solomon AJ. Multiple Sclerosis Diagnostic Delay and Misdiagnosis. Neurol Clin. 2024;42(1):1-13. doi: 10.1016/j.ncl.2023.07.001.
• 17. Miller DH, Weinshenker BG, Filippi M, et al. Differential diagnosis of suspected multiple sclerosis: a consensus approach. Mult Scler. 2008;14(9):1157-1174. doi: 10.1177/1352458508096878.
• 18. Fangerau T, Schimrigk S, Haupts M, et al; Multiple Sclerosis Study Group. Diagnosis of multiple sclerosis: comparison of the Poser criteria and the new McDonald criteria. Acta Neurol Scand. 2004;109(6):385-389. doi: 10.1111/j.1600-0404.2004.00246.x.
• 19. Murray TJ. Diagnosis and treatment of multiple sclerosis. BMJ. 2006;332(7540):525-527. doi: 10.1136/bmj.332.7540.525.
• 20. Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162-173. doi:10.1016/S1474-4422(17)30470-2.
• 21. Gill S, Agarwal M. Multiple Sclerosis Part 1: Essentials and the McDonald Criteria. Magn Reson Imaging Clin N Am. 2024;32(2):207-220. doi: 10.1016/j.mric.2023.11.002.
• 22. Wildner P, Stasiołek M, Matysiak M. Differential diagnosis of multiple sclerosis and other inflammatory CNS diseases. Mult Scler Relat Disord. 2020;37:101452. doi: 10.1016/j.msard.2019.101452.
• 23. Matias-Guiu JA, Díaz-Álvarez J, Ayala JL, et al. Clustering Analysis of FDG-PET Imaging in Primary Progressive Aphasia. Front Aging Neurosci. 2018;10:230. doi: 10.3389/fnagi.2018.00230.
• 24. Pontillo G, Cocozza S, Lanzillo R, et al. Determinants of Deep Gray Matter Atrophy in Multiple Sclerosis: A Multimodal MRI Study. AJNR Am J Neuroradiol. 2019;40(1):99-106. doi: 10.3174/ajnr.A5915.
• 25. Brownlee WJ, Hardy TA, Fazekas F, Miller DH. Diagnosis of multiple sclerosis: progress and challenges. Lancet. 2017;389(10076):1336-1346. doi: 10.1016/S0140-6736(16)30959-X.
• 26. Moetesum M, Siddiqi I, Vincent N, Cloppet F. Assessing visual attributes of handwriting for prediction of neurological disorders - A case study on Parkinson’s disease. Pattern Recognit Lett. 2018;121:19-27. doi:10.1016/j.patrec.2018.04.008.
• 27. Mohammadpoor M, Shoeibi A, Zare H, Shojaee H. A Hierarchical Classification Method for Breast Tumor Detection. Iran J Med Phys. 2016;13(4):261-268. doi:10.22038/ijmp.2016.8453.
• 28. Alizadehsani R, Khosravi A, Roshanzamir M, et al. Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020. Comput Biol Med. 2021;128:104095. doi: 10.1016/j.compbiomed.2020.104095.
• 29. Amin M, Martínez-Heras E, Ontaneda D, Prados Carrasco F. Artificial Intelligence and Multiple Sclerosis. Curr Neurol Neurosci Rep. 2024;24(8):233-243. doi: 10.1007/s11910-024-01354-x.
• 30. Bonacchi R, Filippi M, Rocca MA. Role of artificial intelligence in MS clinical practice. Neuroimage Clin. 2022;35:103065. doi: 10.1016/j.nicl.2022.103065.
• 31. Sadeghi D, Shoeibi A, Ghassemi N, et al. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med. 2022;146:105554. doi: 10.1016/j.compbiomed.2022.105554.
• 32. Martín-Noguerol T, Paulano-Godino F, López-Ortega R, Górriz JM, Riascos RF, Luna A. Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team. Clin Radiol. 2021;76(5):317-324. doi: 10.1016/j.crad.2020.11.113.
• 33. Vandewinckele L, Claessens M, Dinkla A, et al. Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance. Radiother Oncol. 2020;153:55-66. doi:10.1016/j.radonc.2020.09.008.
• 34. Liu J, Pan Y, Li M, et al. Applications of deep learning to MRI images: a survey. Big Data Min Anal. 2018;1(1):1-18. doi:10.26599/BDMA.2018.9020001.
• 35. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017;30(4):449-459. doi: 10.1007/s10278-017-9983-4.
• 36. Huseyn E. Deep Learning Based Early Diagnostics of Parkinsons Disease. arXiv. 2020;2008.01792. doi:10.48550/arXiv.2008.01792.
• 37. Shaban M. Deep Learning for Parkinson’s Disease Diagnosis: A Short Survey. Computers. 2023;12(3):58. doi:10.3390/computers12030058.
• 38. Tanveer M, Richhariya B, Khan RU, et al. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A review. ACM Trans Multimed Comput Commun Appl. 2020;16(1):1-35. doi:10.1145/3344998.
• 39. Zhou Z, Kanwal A, Chaturvedi K, Agrawal A, Nayan AA, Huang Y. Deep learning-based classification of neurodegenerative diseases using gait dataset: a comparative study. In: Proc Int Conf Robot Control Vis Eng. Tokyo, Japan; July 21-23, 2023:59-64. doi:10.1145/3608143.3608154.
• 40. Chandaran SR, Muthusamy G, Sevalaiappan LR, Senthilkumaran N. Deep Learning-based Transfer Learning Model in Diagnosis of Diseases with Brain Magnetic Resonance Imaging. Acta Polytech Hung. 2022;19(5):127-147. doi:10.12700/APH.19.5.2022.5.7.
• 41. Suganthe RC, Latha RS, Geetha M, Sreekanth GR. Diagnosis of Alzheimer’s disease from brain magnetic resonance imaging images using deep learning algorithms. Adv Electr Comput Eng. 2020;20(3):57-64. doi:10.4316/AECE.2020.03007.
• 42. Swati ZNK, Zhao Q, Kabir M, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34-46. doi:10.1016/j.compmedimag.2019.05.001.
• 43. Sivaranjini S, Sujatha CM. Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed Tools Appl. 2020;79:15467-15479. doi:10.1007/s11042-019-7469-8.
• 44. Lua S, Lua Z, Zhang YD. Pathological brain detection based on AlexNet and transfer learning. J Comput Sci. 2019;30:41-47. doi:10.1016/j.jocs.2018.11.008.
• 45. Oh K, Chung YC, Kim KW, Kim WS, Oh IS. Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep. 2019;9(1):18150. doi: 10.1038/s41598-019-54548-6.
• 46. Kim CM, Lee W. Classification of Alzheimer’s disease using ensemble convolutional neural network with lfa algorithm. IEEE Access. 2023;11:143004-15. doi:10.1109/ACCESS.2023.3342917.
• 47. Myszczynska MA, Ojamies PN, Lacoste AMB, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol. 2020;16(8):440-456. doi: 10.1038/s41582-020-0377-8.
• 48. Yakkundi A, Gupta R, Ramesh K, Verma A, Khan U, Ansari MA. Implications of Convolutional Neural Network for Brain MRI Image Classification to Identify Alzheimer's Disease. Parkinsons Dis. 2024;2024:6111483. doi: 10.1155/2024/6111483.
• 49. Chai J, Wu R, Li A, et al. Classification of mild cognitive impairment based on handwriting dynamics and qEEG. Comput Biol Med. 2023;152:106418. doi:10.1016/j.compbiomed.2022.106418.
• 50. Daqqaq TS, Alhasan AS, Ghunaim HA. Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis. Neurosciences (Riyadh). 2024;29(2):77-89. doi:10.17712/nsj.2024.2.20230103.
• 51. Lopatina A, Ropele S, Sibgatulin R, Reichenbach JR, Güllmar D. Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis. Front Neurosci. 2020;14:609468. doi:10.3389/fnins.2020.609468.
• 52. Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia (Engl Ed). 2023;38(8):577-590. doi:10.1016/j.nrleng.2020.10.013.
• 53. Montolío A, Martín-Gallego A, Cegonino J, et al. Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography. Comput Biol Med. 2021;133:104416. doi:10.1016/j.compbiomed.2021.104416.
• 54. Jannat SA, Hoque T, Supti NA, Alam MA. Detection of multiple sclerosis using deep learning. In: Proc Asian Conf Innov Technol (ASIANCON). Pune, India; Aug 27–29, 2021. doi:10.1109/ASIANCON51346.2021.9544601.
• 55. Cilia ND, De Gregorio G, De Stefano C, Fontanella F, Marcelli A, Parziale A. Diagnosing Alzheimer’s disease from on-line handwriting: A novel dataset and performance benchmarking. Eng Appl Artif Intell. 2022;111:104822. doi:10.1016/j.engappai.2022.104822.
• 56. Sarin K, Bardamova M, Svetlakov M, et al. A three-stage fuzzy classifier method for Parkinson’s disease diagnosis using dynamic handwriting analysis. Decis Anal J. 2023;8:100274. doi:10.1016/j.dajour.2023.100274.
• 57. Öcal H. A novel approach to detection of Alzheimer’s disease from handwriting: triple ensemble learning model. Gazi Univ J Sci Part C Des Technol. 2024;12(1):214-223. doi:10.29109/gujsc.1386416.
• 58. Aouraghe I, Khaissidi G, Mrabti M. A literature review of online handwriting analysis to detect Parkinson’s disease at an early stage. Multimed Tools Appl. 2023;82:11923-11948. doi:10.1007/s11042-022-13759-2.
• 59. Huang Y, Chaturvedi K, Nayan A-A, Hesamian MH, Braytee A, Prasad M. Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques. Information. 2024; 15(4):220. doi: 10.3390/info15040220.