SpineAutoCAD: Multimodal CAD System for Lumbar Spine MRI Analysis and Structured Report Generation

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dc.contributor.authorSalem, Saied
dc.contributor.authorHabib, Afnan
dc.contributor.authorRaza, Mukhlis
dc.contributor.authorAydin, Ahmet Arif
dc.contributor.authorGu, Yeong Hyeon
dc.contributor.authorAl-Antari, Mugahed A.
dc.date.accessioned2026-04-04T13:18:59Z
dc.date.available2026-04-04T13:18:59Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
dc.description.abstractAutomated analysis of lumbar spine MRI is essential for improving diagnostic consistency and enhancing clinical workflow efficiency in the evaluation of chronic low back pain (CLBP). In this study, we present a computer-aided diagnosis (CAD) framework designed to automate both the analysis of lumbar spine MRI scans and the generation of structured diagnostic reports. The system processes 3D DICOM MRI volumes by extracting mid-sagittal slices for the segmentation of vertebrae and intervertebral discs (IVDs), followed by a 3D cross-projection method to localize the corresponding axial slices. The SegResNet architecture is employed as segmentation model to delineate anatomical structures in both sagittal and axial views. From these segmentations, quantitative measurements of key spinal anatomy are extracted, enabling automated anatomical indices measurements, disorders detection, spinal stenosis severity grading, and evaluation of spinal alignment abnormalities. These assessments serve as the diagnostics information feed into the input prompt to large language model (LLM) for report generation. The proposed system leverages a novel retrievalaugmented generation (RAG) approach that integrates semantic retrieval and knowledge graph-based reasoning to generate detailed, level-specific diagnostic report. The system demonstrates high segmentation performance (Dice: 97.79% sagittal, 93.52% axial) and generates clinically coherent reports using AgenticRAG, achieving a BERT F1-score of 83.58%. These results highlight its effectiveness for accurate, level-specific diagnosis and streamlined clinical reporting. © 2025 IEEE.
dc.description.sponsorshipNational Research Foundation of Korea, NRF; Institute for Information and Communications Technology Promotion, IITP; Ministry of Science and ICT, South Korea, MSIT, (RS-2023-00256517, IITP-2025-RS-2024-00437191); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK, (123N325)
dc.identifier.doi10.1109/IDAP68205.2025.11222171
dc.identifier.isbn979-833158990-5
dc.identifier.scopus2-s2.0-105024999895
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP68205.2025.11222171
dc.identifier.urihttps://hdl.handle.net/11616/108066
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectAdvanced Agentic RAG
dc.subjectComputer-aided Diagnosis (CAD)
dc.subjectLarge Language Model (LLM)
dc.subjectLumbar spinal stenosis (LSS)
dc.subjectStructured Medical Report Generation (sMRG)
dc.titleSpineAutoCAD: Multimodal CAD System for Lumbar Spine MRI Analysis and Structured Report Generation
dc.typeConference Object

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