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AkhavanMahdavi, AliAsghar, Mahdipour, Elham, Nahayati, MohammadAli. (1405). A Deep Learning–Driven Framework for Remote Monitoring and Severity Classification in Multiple Sclerosis Using Non-Motor Symptom Questionnaires. پژوهشنامه مدیریت اجرایی, 2(2), 73-85. doi: 10.22080/frai.2026.30959.1045
AliAsghar AkhavanMahdavi; Elham Mahdipour; MohammadAli Nahayati. "A Deep Learning–Driven Framework for Remote Monitoring and Severity Classification in Multiple Sclerosis Using Non-Motor Symptom Questionnaires". پژوهشنامه مدیریت اجرایی, 2, 2, 1405, 73-85. doi: 10.22080/frai.2026.30959.1045
AkhavanMahdavi, AliAsghar, Mahdipour, Elham, Nahayati, MohammadAli. (1405). 'A Deep Learning–Driven Framework for Remote Monitoring and Severity Classification in Multiple Sclerosis Using Non-Motor Symptom Questionnaires', پژوهشنامه مدیریت اجرایی, 2(2), pp. 73-85. doi: 10.22080/frai.2026.30959.1045
AkhavanMahdavi, AliAsghar, Mahdipour, Elham, Nahayati, MohammadAli. A Deep Learning–Driven Framework for Remote Monitoring and Severity Classification in Multiple Sclerosis Using Non-Motor Symptom Questionnaires. پژوهشنامه مدیریت اجرایی, 1405; 2(2): 73-85. doi: 10.22080/frai.2026.30959.1045

A Deep Learning–Driven Framework for Remote Monitoring and Severity Classification in Multiple Sclerosis Using Non-Motor Symptom Questionnaires

Future Research on AI and IoT
مقاله 6، دوره 2، شماره 2، دی 2026، صفحه 73-85    اصل مقاله (2.11 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22080/frai.2026.30959.1045
نویسندگان
AliAsghar AkhavanMahdavi1؛ Elham Mahdipour* 2؛ MohammadAli Nahayati3
1Computer Engineering Department, Khavaran Institute of Higher Education, Mashhad, Iran.
2Computer Engineering Department, Khavaran Institute of Higher Education
3Faculty of Medicine, University of Medical Sciences, Mashhad, Iran.
تاریخ دریافت: 09 دی 1404،  تاریخ بازنگری: 23 خرداد 1405،  تاریخ پذیرش: 07 بهمن 1404 
چکیده
Multiple sclerosis (MS) is a chronic neurodegenerative disorder with unpredictable progression. Despite advancements in imaging and clinical assessment, access to regular neurological evaluations remains limited, particularly in regions with centralized healthcare services. This study proposes a novel deep learning–based approach for assessing MS severity using non-motor symptom questionnaires. Sixteen validated patient-reported outcome measures (PROMs) were administered through a web-based platform, enabling remote monitoring of emotional, behavioral, urinary, fatigue, and functional symptoms. Data from 152 fully completed questionnaires were used to train and evaluate the proposed convolutional neural network (CNN) model. The model demonstrated superior performance compared with traditional machine learning classifiers, achieving 99.5% accuracy on the test dataset while incorporating regularization to mitigate overfitting. The proposed framework demonstrates strong potential as a decision-support tool for assisting remote monitoring and early identification of symptom deterioration in patients with multiple sclerosis. The proposed approach offers an affordable, non-invasive, and scalable solution for MS monitoring, particularly beneficial where MRI and specialist access are limited. Future work will validate the model on larger and multi-center cohorts.
کلیدواژه‌ها
Multiple Sclerosis؛ Deep Learning؛ Patient-Reported Outcomes؛ Remote Monitoring؛ Telemedicine
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