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

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dc.contributor.authorAl-Antari, Mugahed A.
dc.contributor.authorSalem, Saied
dc.contributor.authorRaza, Mukhlis
dc.contributor.authorElbadawy, Ahmed S.
dc.contributor.authorButun, Ertan
dc.contributor.authorAydin, Ahmet Arif
dc.contributor.authorAydogan, Murat
dc.date.accessioned2026-04-04T13:37:29Z
dc.date.available2026-04-04T13:37:29Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractLumbar 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).
dc.description.sponsorshipNational Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [RS-2022-00166402, RS-2023-00256517]; National Research Foundation of Korea (NRF) - Korea government (MSIT) [123N325]; TUBITAK (The Scientific and Technological Research Council of Turkey) [IITP-2024-RS-2024-00437191]; MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program; National Research Foundation (NRF); Scientific and Technological Research Council of Turkey (TUBITAK)
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2022-00166402 and RS-2023-00256517) and by the TUBITAK (The Scientific and Technological Research Council of Turkey) under Grant Number: 123N325. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-RS-2024-00437191) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). The authors express their deep gratitude to the National Research Foundation (NRF) and the Scientific and Technological Research Council of Turkey (TUBITAK) for their substantial and invaluable support in facilitating this significant international collaborative research.
dc.identifier.doi10.1007/s10462-025-11185-y
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.issue8
dc.identifier.orcid0009-0002-9824-7951
dc.identifier.orcid0009-0002-8948-2167
dc.identifier.orcid0000-0002-1595-5681
dc.identifier.orcid0000-0002-4124-7275
dc.identifier.orcid0000-0002-6876-6454
dc.identifier.orcid0009-0001-7704-5149
dc.identifier.orcid0000-0002-4457-4407
dc.identifier.scopus2-s2.0-105004182232
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s10462-025-11185-y
dc.identifier.urihttps://hdl.handle.net/11616/109867
dc.identifier.volume58
dc.identifier.wosWOS:001480703000003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofArtificial Intelligence Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectLumbar spinal stenosis (LSS)
dc.subjectLSS prediction
dc.subjectHarmonization
dc.subjectSpinal LSS indices measurements
dc.subjectExplainable artificial intelligence (XAI)
dc.subjectLarge language models (LLMs)
dc.titleEvaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review
dc.typeArticle

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