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    A Novel CAD Framework with Visual and Textual Interpretability: Multimodal Insights for Predicting Respiratory Diseases
    (Institute of Electrical and Electronics Engineers Inc., 2024) Mukhlis, Raza; Saleem, Saied; Kwon, Hyunwook; Hussain, Jamil; Aydin, Ahmet Arif; Al-Antari, Mugahed A.
    Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: Data Collection and Annotation, Data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, XAI LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The AI-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of 70 %, precision of 70 %, and F1-score of 0.60 %, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks. © 2024 IEEE.
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    Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review
    (Springer, 2025) Al-Antari, Mugahed A.; Salem, Saied; Raza, Mukhlis; Elbadawy, Ahmed S.; Butun, Ertan; Aydin, Ahmet Arif; Aydogan, Murat
    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).
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    SpineAutoCAD: Multimodal CAD System for Lumbar Spine MRI Analysis and Structured Report Generation
    (Institute of Electrical and Electronics Engineers Inc., 2025) Salem, Saied; Habib, Afnan; Raza, Mukhlis; Aydin, Ahmet Arif; Gu, Yeong Hyeon; Al-Antari, Mugahed A.
    Automated 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.