Artificial Intelligence: A Primer for the Radiologists

Harsimran Bhatia; Anmol Bhatia; Chirag K. Ahuja; Arnavjit Singh; Kushaljit S. Sodhi

doi:10.1055/s-0042-1759863

Indographics, Table of Contents

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

¹Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India

,

Anmol Bhatia

¹Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India

,

Chirag K. Ahuja

¹Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India

,

Arnavjit Singh

²Department of Electronics and Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

,

Kushaljit S. Sodhi

¹Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India

› Author Affiliations

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

Full Text

References

References
1 Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019; 28 (02) 73-81
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
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
5 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
6 European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging 2019; 10 (01) 44
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
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
10 Russel S. Norvig P Artificial Intelligence. A Modern Approach. New Jersey: Pearson Education, Inc.; 2010
11 Samuel AL. Some studies in machine learning using the game of checkers. IBM J 1959; 3: 211-229
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;
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
14 Castellino RA. Computer aided detection (CAD): an overview. Cancer Imaging 2005; 5: 17-19
15 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521 (7553): 436-444
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
17 Kevin Zhou S, Greenspan H, Shen D. Deep Learning for Medical Image Analysis. 1st ed.. Amsterdam: Elsevier; 2017. (Academic Press, 2017)
18 Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 2017; 19: 221-248
19 Veeraraghavan H. MO-A-207B–01: radiomics: segmentation & feature extraction techniques. Med Phys 2016; 43: 3694-3694
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
21 Cinteza M. Medicine with intelligence. be it an artificial one. Maedica (Buchar) 2022; 17 (02) 251-252
22 Burley SK, Arap W, Pasqualini R. Predicting proteome-scale protein structure with artificial intelligence. N Engl J Med 2021; 385 (23) 2191-2194
23 Stokes JM, Yang K, Swanson K. et al. A deep learning approach to antibiotic discovery. Cell 2020; 180 (04) 688-702.e13
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
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
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
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
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
29 Regge D, Halligan S. CAD: how it works, how to use it, performance. Eur J Radiol 2013; 82 (08) 1171-1176
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
31 Katzen J, Dodelzon K. A review of computer aided detection in mammography. Clin Imaging 2018; 52: 305-309
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
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
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
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
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
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
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
39 Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16 (05) 933-951
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
41 Sohn K, Shang W, Lee H. Advances in Neural Information Processing Systems 27 (NIPS 2014). Red Hook, NY: Curran Assoc; 2014: 2141-2149
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
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
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
45 Moeskops P, Wolterink JM, Van der Velden BH. et al. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2016: 478-486
46 Neri E, Regge D. Imaging biobanks in oncology: European perspective. Future Oncol 2017; 13 (05) 433-441
47 European Society of Radiology (ESR). ESR Position Paper on Imaging Biobanks. Insights Imaging 2015; 6 (04) 403-410
48 Champaign JL, Cederbom GJ. Advances in breast cancer detection with screening mammography. Ochsner J 2000; 2 (01) 33-35
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
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
51 Hricak H. 2016 New Horizons Lecture: beyond imaging-radiology of tomorrow. Radiology 2018; 286 (03) 764-775
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