Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review

被引：0

作者：

Mugahed A. Al-antari ^{[1
]}

Saied Salem ^{[2
]}

Mukhlis Raza ^{[2
]}

Ahmed S. Elbadawy ^{[2
]}

Ertan Bütün ^{[3
]}

Ahmet Arif Aydin ^{[4
]}

Murat Aydoğan ^{[5
]}

Bilal Ertuğrul ^{[6
]}

Muhammed Talo ^{[5
]}

Yeong Hyeon Gu ^{[7
]}

机构：

[1] Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul

[2] Department of Artificial Intelligence, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul

[3] Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazığ

[4] Department of Computer Engineering, Faculty of Engineering, Inonu University, Malatya

[5] Department of Software Engineering, Faculty of Technology, Fırat University, Elazığ

[6] Department of Neurosurgery, Faculty of Medicine, Fırat University, Elazığ

[7] Department of Computer Science & Engineering, University of North Texas, Denton, 76205, TX

来源：

Artificial Intelligence Review | / 58卷 / 8期

基金：

新加坡国家研究基金会;

关键词：

Explainable artificial intelligence (XAI); Harmonization; Large language models (LLMs); LSS prediction; Lumbar spinal stenosis (LSS); Spinal LSS indices measurements;

D O I：

10.1007/s10462-025-11185-y

中图分类号：

学科分类号：

摘要：

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of Science, ScienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs). © The Author(s) 2025.

引用

共 149 条

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