Download PDF
CC BY-NC-ND 4.0 · Indographics 2022; 01(02): 215-221
DOI: 10.1055/s-0042-1759863
Review Article

Artificial Intelligence: A Primer for the Radiologists

Harsimran Bhatia
1   Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
,
Anmol Bhatia
1   Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
,
Chirag K. Ahuja 
1   Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
,
Arnavjit Singh
2   Department of Electronics and Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
,
Kushaljit S. Sodhi
1   Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
› Author Affiliations Funding None.
› Further Information
Also available at
   Permissions and Reprints

Abstract

Artificial intelligence (AI) has revolutionized almost every sphere of life today by providing cutting-edge tools aimed at improving the quality of life. The term AI refers to any operating system or a software that mimics human intelligence and performs functions like the human mind with minimal human intervention. The present review article focuses on the basics of AI and the terminology used in the field of AI. Flowcharts and figures to facilitate easy understanding of its impact and its potential applications have also been provided. It is meant to serve as a primer for the beginner.

Keywords

artificial intelligence - learning - machine - deep


Publication History

Article published online:
19 September 2023

© 2022. Indographics. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India

 

  • References

  • 1 Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019; 28 (02) 73-81
    CrossrefPubMedGoogle Scholar
  • 2 McCarthy J, Minsky ML, Rochester N, Shannon CE. Proposal for the Dartmouth summer research project on artificial intelligence. Tech rep, Dartmouth College; 1955. Accessed October 13, 2018, at: http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html
    Google Scholar
  • 3 cbinsights.com [Internet]. Healthcare remains the hottest AI category for deals; 2017. Accessed April 12, 2016, at: https://www.cbinsights.com/research/artificial-intelligence-healthcare-startups-investors/
  • 4 Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69S: S36-S40
    CrossrefPubMedGoogle Scholar
  • 5 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
    CrossrefPubMedGoogle Scholar
  • 6 European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging 2019; 10 (01) 44
    CrossrefPubMedGoogle Scholar
  • 7 ubs.com[Internet]. Accessed September 29, 2016, at: https://www.ubs.com/microsites/artificial-intelligence/en/new-dawn.html;c2016
  • 8 McCarthy J, Minsky ML, Rochester N, Shannon CE. Proposal for the Dartmouth summer research project on artificial intelligence. AI Mag 2006; 27: 4
    Google Scholar
  • 9 Brink JA, Arenson RL, Grist TM, Lewin JS, Enzmann D. Bits and bytes: the future of radiology lies in informatics and information technology. Eur Radiol 2017; 27 (09) 3647-3651
    CrossrefPubMedGoogle Scholar
  • 10 Russel S. Norvig P Artificial Intelligence. A Modern Approach. New Jersey: Pearson Education, Inc.; 2010
    PubMed
  • 11 Samuel AL. Some studies in machine learning using the game of checkers. IBM J 1959; 3: 211-229
    CrossrefGoogle Scholar
  • 12 Langlais ÉL, Thériault-Lauzier P, Marquis-Gravel G. et al. Novel artificial intelligence applications in cardiology: current landscape, limitations, and the road to real-world applications. J Cardiovasc Transl Res 2022; DOI: 10.1007/s12265-022-10260-x.
    CrossrefPubMedGoogle Scholar
  • 13 Theofilatos K, Pavlopoulou N, Papasavvas C. et al. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: evolutionary enhanced Markov clustering. Artif Intell Med 2015; 63 (03) 181-189
    CrossrefPubMedGoogle Scholar
  • 14 Castellino RA. Computer aided detection (CAD): an overview. Cancer Imaging 2005; 5: 17-19
    CrossrefPubMedGoogle Scholar
  • 15 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521 (7553): 436-444
    CrossrefPubMedGoogle Scholar
  • 16 Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19 (06) 1236-1246
    CrossrefPubMedGoogle Scholar
  • 17 Kevin Zhou S, Greenspan H, Shen D. Deep Learning for Medical Image Analysis. 1st ed.. Amsterdam: Elsevier; 2017. (Academic Press, 2017)
    Google Scholar
  • 18 Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 2017; 19: 221-248
    CrossrefPubMedGoogle Scholar
  • 19 Veeraraghavan H. MO-A-207B–01: radiomics: segmentation & feature extraction techniques. Med Phys 2016; 43: 3694-3694
    Google Scholar
  • 20 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
    CrossrefPubMedGoogle Scholar
  • 21 Cinteza M. Medicine with intelligence. be it an artificial one. Maedica (Buchar) 2022; 17 (02) 251-252
    PubMedGoogle Scholar
  • 22 Burley SK, Arap W, Pasqualini R. Predicting proteome-scale protein structure with artificial intelligence. N Engl J Med 2021; 385 (23) 2191-2194
    CrossrefPubMedGoogle Scholar
  • 23 Stokes JM, Yang K, Swanson K. et al. A deep learning approach to antibiotic discovery. Cell 2020; 180 (04) 688-702.e13
    CrossrefPubMedGoogle Scholar
  • 24 Maegerlein C, Fischer J, Mönch S. et al. Automated calculation of the Alberta Stroke Program Early CT Score: feasibility and reliability. Radiology 2019; 291 (01) 141-148
    CrossrefPubMedGoogle Scholar
  • 25 Amukotuwa SA, Straka M, Smith H. et al. Automated detection of intracranial large vessel occlusions on computed tomography angiography: a single center experience. Stroke 2019; 50 (10) 2790-2798
    CrossrefPubMedGoogle Scholar
  • 26 Jo T, Nho K, Saykin AJ. Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 2019; 11: 220
    CrossrefPubMedGoogle Scholar
  • 27 Alhasan AS. Clinical applications of artificial intelligence, machine learning, and deep learning in the imaging of gliomas: a systematic review. Cureus 2021; 13 (11) e19580
    PubMedGoogle Scholar
  • 28 Adeoye J, Akinshipo A, Thomson P, Su YX. Artificial intelligence-based prediction for cancer-related outcomes in Africa: status and potential refinements. J Glob Health 2022; 12: 03017
    CrossrefPubMedGoogle Scholar
  • 29 Regge D, Halligan S. CAD: how it works, how to use it, performance. Eur J Radiol 2013; 82 (08) 1171-1176
    CrossrefPubMedGoogle Scholar
  • 30 Al Mohammad B, Brennan PC, Mello-Thoms C. A review of lung cancer screening and the role of computer-aided detection. Clin Radiol 2017; 72 (06) 433-442
    CrossrefPubMedGoogle Scholar
  • 31 Katzen J, Dodelzon K. A review of computer aided detection in mammography. Clin Imaging 2018; 52: 305-309
    CrossrefPubMedGoogle Scholar
  • 32 Huang X, Shan J, Vaidya V. Lung nodule detection in CT using 3D convolutional neural networks. IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); 2017:379–383
    Google Scholar
  • 33 Tsehay YK, Lay NS, Roth HR, Wang X, Kwak JT, Turkbey BI. et al. Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images. In: Medical Imaging 2017: Computer-Aided Diagnosis. Washington, USA: SPIE publications; 2017: 1013405
    Google Scholar
  • 34 Fartaria MJ, Bonnier G, Roche A. et al. Automated detection of white matter and cortical lesions in early stages of multiple sclerosis. J Magn Reson Imaging 2016; 43 (06) 1445-1454
    CrossrefPubMedGoogle Scholar
  • 35 Zhang X, Yan LF, Hu YC. et al. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget 2017; 8 (29) 47816-47830
    CrossrefPubMedGoogle Scholar
  • 36 Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 2016; 30: 108-119
    CrossrefPubMedGoogle Scholar
  • 37 Cheng R, Roth HR, Lay N. et al. Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J Med Imaging (Bellingham) 2017; 4 (04) 041302
    PubMedGoogle Scholar
  • 38 Roth HR, Lu L, Lay N. et al. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med Image Anal 2018; 45: 94-107
    CrossrefPubMedGoogle Scholar
  • 39 Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16 (05) 933-951
    CrossrefPubMedGoogle Scholar
  • 40 Mirsadraee S, Oswal D, Alizadeh Y, Caulo A, van Beek Jr E. The 7th lung cancer TNM classification and staging system: review of the changes and implications. World J Radiol 2012; 4 (04) 128-134
    CrossrefPubMedGoogle Scholar
  • 41 Sohn K, Shang W, Lee H. Advances in Neural Information Processing Systems 27 (NIPS 2014). Red Hook, NY: Curran Assoc; 2014: 2141-2149
    Google Scholar
  • 42 Lecointre L, Dana J, Lodi M, Akladios C, Gallix B. Artificial intelligence-based radiomics models in endometrial cancer: a systematic review. Eur J Surg Oncol 2021; 47 (11) 2734-2741
    CrossrefPubMedGoogle Scholar
  • 43 Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 2017; 208 (04) 754-760
    CrossrefPubMedGoogle Scholar
  • 44 Ronneberger O, Fischer P, Brox TU. Convolutional networks for biomedical image segmentation. 2015 Accessed May 18, 2015, at: https://arxiv.org/abs/1505.04597
    PubMedGoogle Scholar
  • 45 Moeskops P, Wolterink JM, Van der Velden BH. et al. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2016: 478-486
    PubMedGoogle Scholar
  • 46 Neri E, Regge D. Imaging biobanks in oncology: European perspective. Future Oncol 2017; 13 (05) 433-441
    CrossrefPubMedGoogle Scholar
  • 47 European Society of Radiology (ESR). ESR Position Paper on Imaging Biobanks. Insights Imaging 2015; 6 (04) 403-410
    CrossrefPubMedGoogle Scholar
  • 48 Champaign JL, Cederbom GJ. Advances in breast cancer detection with screening mammography. Ochsner J 2000; 2 (01) 33-35
    PubMedGoogle Scholar
  • 49 Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med 2011; 41 (06) 449-462
    CrossrefPubMedGoogle Scholar
  • 50 Choy G, Khalilzadeh O, Michalski M. et al. Current applications and future impact of machine learning in radiology. Radiology 2018; 288 (02) 318-328
    CrossrefPubMedGoogle Scholar
  • 51 Hricak H. 2016 New Horizons Lecture: beyond imaging-radiology of tomorrow. Radiology 2018; 286 (03) 764-775
    CrossrefPubMedGoogle Scholar
  • 52 Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018; 2 (01) 35
    CrossrefPubMedGoogle Scholar