Araştırma Makalesi

Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective

Cilt: 14 28 Temmuz 2026
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Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective

Öz

Abstract Background: Lumbar spinal stenosis (LSS) is a prevalent degenerative spinal disorder in neurosurgical practice, where magnetic resonance imaging (MRI) plays a central role in diagnosis and surgical planning. In recent years, artificial intelligence (AI), particularly deep learning–based models, has shown promising results in automated MRI analysis, including stenosis grading and segmentation. However, most existing AI systems rely heavily on predefined quantitative parameters, raising concerns regarding their clinical applicability. Objective: This study aims to critically evaluate the limitations of parameter-based AI models in the MRI assessment of LSS and to propose a conceptual framework for clinically aligned, explainable hybrid AI approaches. Methods: A narrative and conceptual analysis was conducted based on current literature and clinical experience. The limitations of parameter-based AI models were examined in terms of standardization, clinical decision-making complexity, clinicoradiological mismatch, multilevel disease evaluation, imposed parameterization, and labeling variability. Results: Parameter-based AI models demonstrate important limitations in reflecting real-world clinical decision-making. The lack of standardized thresholds, inability to integrate multidimensional clinical factors, and challenges in identifying symptomatic levels in multilevel disease reduce their clinical utility. Additionally, interobserver variability in labeling introduces noise that negatively affects model performance and generalizability. Conclusion: While parameter-based AI models contribute to the quantitative evaluation of LSS, they remain insufficient for comprehensive clinical decision support. Explainable hybrid AI approaches that integrate imaging data with clinical variables and structured knowledge representations may offer a more reliable and clinically meaningful framework for future applications.

Anahtar Kelimeler

Destekleyen Kurum

No specific funding was received for this study.

Etik Beyan

This study is a conceptual and narrative analysis based on previously published literature and does not involve human participants or animal subjects. Therefore, ethical approval was not required.

Kaynakça

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  3. [3] Alpert JN. Review of the diagnosis and management of lumbar spinal stenosis. JAMA. 2022;328(8):779–780. https://doi.org/10.1001/jama.2022.11384
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  6. [6] Abdulmahmod OF, Al-Antari MA, Kwon H, Habib A. Medical spine sagittal MRI dataset for segmentation and foraminal stenosis detection. Sci Data. In press.
  7. [7] Al-Antari MA, et al. Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review. Artif Intell Rev. In press.
  8. [8] Salem S, Habib A, Raza M, Al-Huda Z, Al-Maqtari O, Ertuğrul B, Yıldırım Ö. AutoSpineAI: lightweight multimodal CAD framework for lumbar spine MRI assessments. Proc IEEE EMBS Int Conf Biomed Health Inform (BHI). 2025.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Biyomühendislik (Diğer)

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

28 Temmuz 2026

Gönderilme Tarihi

20 Nisan 2026

Kabul Tarihi

28 Temmuz 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 14

Kaynak Göster

APA
Ertuğrul, B. (2026). Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective. Balkan Journal of Electrical and Computer Engineering, 14. https://doi.org/10.17694/bajece.1933652
AMA
1.Ertuğrul B. Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective. Balkan Journal of Electrical and Computer Engineering. 2026;14. doi:10.17694/bajece.1933652
Chicago
Ertuğrul, Bilal. 2026. “Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective”. Balkan Journal of Electrical and Computer Engineering 14 (Temmuz). https://doi.org/10.17694/bajece.1933652.
EndNote
Ertuğrul B (01 Temmuz 2026) Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective. Balkan Journal of Electrical and Computer Engineering 14
IEEE
[1]B. Ertuğrul, “Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective”, Balkan Journal of Electrical and Computer Engineering, c. 14, Tem. 2026, doi: 10.17694/bajece.1933652.
ISNAD
Ertuğrul, Bilal. “Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective”. Balkan Journal of Electrical and Computer Engineering 14 (01 Temmuz 2026). https://doi.org/10.17694/bajece.1933652.
JAMA
1.Ertuğrul B. Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective. Balkan Journal of Electrical and Computer Engineering. 2026;14. doi:10.17694/bajece.1933652.
MLA
Ertuğrul, Bilal. “Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective”. Balkan Journal of Electrical and Computer Engineering, c. 14, Temmuz 2026, doi:10.17694/bajece.1933652.
Vancouver
1.Bilal Ertuğrul. Limitations of Parameter-Based AI Models in the MRI Evaluation of Lumbar Spinal Stenosis: A Clinical Perspective. Balkan Journal of Electrical and Computer Engineering. 01 Temmuz 2026;14. doi:10.17694/bajece.1933652

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