Luca Saba

Active 2002–2026

379
Papers
18,747
Citations
72
h-index
325
i10-index

Citations

Citations per year for Luca Saba1972: 1 citations1989: 1 citations1991: 3 citations1994: 1 citations2002: 22 citations2003: 1 citations2007: 1 citations2009: 22 citations2010: 10 citations2011: 7 citations2012: 18 citations2013: 30 citations2014: 35 citations2015: 74 citations2016: 107 citations2017: 120 citations2018: 132 citations2019: 276 citations2020: 761 citations2021: 1,193 citations2022: 1,722 citations2023: 1,159 citations2024: 1,656 citations2025: 1,143 citations2026: 182 citations1973–1988: no citations, so these years are not shown1990: no citations, so this year is not shown1992–1993: no citations, so these years are not shown1995–2001: no citations, so these years are not shown2004–2006: no citations, so these years are not shown2008: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 1,152 citing papers, 13.8% of this breakdownChina: 906 citing papers, 10.9% of this breakdownItaly: 755 citing papers, 9.1% of this breakdownIndia: 720 citing papers, 8.7% of this breakdownUnited Kingdom: 451 citing papers, 5.4% of this breakdownGermany: 276 citing papers, 3.3% of this breakdownCanada: 265 citing papers, 3.2% of this breakdownFrance: 187 citing papers, 2.2% of this breakdownAustralia: 171 citing papers, 2.1% of this breakdownSpain: 169 citing papers, 2% of this breakdownNetherlands: 167 citing papers, 2% of this breakdownSaudi Arabia: 165 citing papers, 2% of this breakdown
0%13.8%Other 35.3%

Fields

  • Medicine63.8%
  • Computer Science11.6%
  • Neuroscience10.5%
  • Engineering3.9%
  • Biochemistry, Genetics and Molecular Biology3%
  • Health Professions2.7%
  • Other4.5%

Topics

  • Cerebrovascular and Carotid Artery Diseases4.9%
  • Radiomics and Machine Learning in Medical Imaging4.8%
  • Brain Tumor Detection and Classification3.5%
  • Artificial Intelligence in Healthcare and Education3.2%
  • COVID-19 diagnosis using AI3.1%
  • Cardiovascular Health and Disease Prevention3.1%
  • Other77.4%

Coauthors

All papers

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  1. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , George D. Kitas, Mostafa M. Fouda, Seemant Chaturvedi, Mannudeep K. Kalra, Jasjit S. Suri - Healthcare 2022 cited by 324

  2. A deep look into radiomics

    Authors: , , , , , - La radiologia medica 2021 cited by 416

  3. A Review on a Deep Learning Perspective in Brain Cancer Classification

    Authors: , , , , , , , , , , , , - Cancers 2019 cited by 460

  4. Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications

    Authors: , , , , , , , , - The Lancet Neurology 2019 cited by 487

  5. Current applications and challenges in large language models for patient care: a systematic review

    Authors: , , , , , , , , , , , , , - Communications Medicine 2025 cited by 215

  6. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm

    Authors: , , , , , - Computers in Biology and Medicine, Comput. Biol. Medicine 2020 cited by 250

  7. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review

    Authors: , , , , , - Computers in Biology and Medicine, Comput. Biol. Medicine 2022 cited by 132

  8. Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data

    Authors: , , , , , - Diagnostics 2023 cited by 87

  9. The present and future of deep learning in radiology

    Authors: , , , , , , , , , , , , , , , , - European Journal of Radiology 2019 cited by 345

  10. Carotid Plaque-RADS

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Maura Griffin, Bruce A. Wasserman, M. Eline Kooi, Jonathan H. Gillard, Giuseppe Lanzino, Dimitri P. Mikhailidis, Daniel M. Mandell, John C. Benson, Dianne H.K. van Dam-Nolen, Anna Kopczak, Jae W. Song, Ajay Gupta, J. Kevin DeMarco, Seemant Chaturvedi, Renu Virmani, Thomas S. Hatsukami, Martin M. Brown, Alan R. Moody, Peter Libby, Andreas Schindler, Tobias Saam - JACC. Cardiovascular imaging 2023 cited by 185

  11. Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review

    Authors: , , , , , - Computers in Biology and Medicine, Comput. Biol. Medicine 2021 cited by 185

  12. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Jasjit S. Suri - EClinicalMedicine 2024 cited by 141

  13. Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound

    Authors: , , , , , - Computers in Biology and Medicine, Comput. Biol. Medicine 2021 cited by 125

  14. Coronary Artery Calcification: Current Concepts and Clinical Implications

    Authors: , , , , , , , , , , , - Circulation 2024 cited by 228

  15. Large language models for structured reporting in radiology: past, present, and future

    Authors: , , , , , , , , , , , , - European Radiology 2024 cited by 104

  16. Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

    Authors: , , , , , , - Diagnostics 2022 cited by 95

  17. Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

    Authors: , , , , , , , , , , , , - Cancers 2022 cited by 121

  18. Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review

    Authors: , , , , , , , , , , , , , , , - Computers in Biology and Medicine, Comput. Biol. Medicine 2022 cited by 82

  19. UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

    Authors: , , , , , , , , , , , , , , - IEEE Access 2022 cited by 85

  20. Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

    Authors: , , , , , , , , , , , , - Journal of Stroke 2018 cited by 290

  21. Blockchain, artificial intelligence, and healthcare: the tripod of future - a narrative review

    Authors: , , , , , , , , , , - Artificial Intelligence Review, Artif. Intell. Rev. 2024 cited by 71

  22. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, F. Nagy, Zoltán Ruzsa, Mostafa M. Fouda, Subbaram Naidu, Klaudija Višković, Mannudeep K. Kalra - Diagnostics 2022 cited by 58

  23. Carotid Artery Wall Imaging: Perspective and Guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology

    Authors: , , , , , , , , , , , , , , , , , , - American Journal of Neuroradiology 2018 cited by 315

  24. Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

    Authors: , , , , , , , - Diagnostics 2022 cited by 72