Research Article

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

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

Abstract

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.

Keywords

Supporting Institution

No specific funding was received for this study.

Ethical Statement

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.

References

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Details

Primary Language

English

Subjects

Bioengineering (Other)

Journal Section

Research Article

Publication Date

July 28, 2026

Submission Date

April 20, 2026

Acceptance Date

July 28, 2026

Published in Issue

Year 2026 Volume: 14

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 (July). https://doi.org/10.17694/bajece.1933652.
EndNote
Ertuğrul B (July 1, 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, vol. 14, July 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 (July 1, 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, vol. 14, July 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. 2026 Jul. 1;14. doi:10.17694/bajece.1933652

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