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@article{188890,
        author = {AADIL RASHID MALIK and Mr. Mudasir Mohi Ud Din and Mr Jasar Hassan and Ms Aimun Manzoor and Ms Misba Shapoo},
        title = {Artificial Intelligence in Coronary CT Angiography: Applications and Future Prospects},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3877-3885},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188890},
        abstract = {Coronary computed tomography angiography (CCTA) has evolved into a first-line non-invasive modality for the diagnosis and management of coronary artery disease (CAD). Yet, increasing image complexity and expanding clinical indications have amplified the need for automation, reproducibility and enhanced diagnostic accuracy. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has emerged as a transformative tool in CCTA. AI now plays an essential role in image acquisition, reconstruction, segmentation, stenosis detection, plaque quantification, calcium scoring, ischemia assessment (CT-derived fractional flow reserve, CT-FFR), workflow optimization and clinical prognostication. This review presents an in-depth exploration of AI methodologies in CCTA, summarizes major clinical applications with detailed evidence, discusses regulatory and reimbursement developments, evaluates current limitations and provides a forward-looking perspective on future innovations such as multimodal learning, real-time decision support and federated model training. The growing convergence of AI and CCTA is expected to set new standards in precision cardiovascular imaging and risk prediction},
        keywords = {Artificial intelligence, machine learning, deep learning, coronary CT angiography, CAD-RADS, plaque analysis, stenosis quantification, radiomics},
        month = {December},
        }