Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Canadian Association of Radiologists Journal

Volumn 69, Issue 2, 2018, Pages 120-135

  Tang, An ^a,b   Tam, Roger ^c   Cadrin Chênevert, Alexandre ^d   Guest, Will ^c   Chong, Jaron ^e   Barfett, Joseph ^f   Chepelev, Leonid ^g   Cairns, Robyn ^c   Mitchell, J Ross ^h   Cicero, Mark D ^f   Poudrette, Manuel Gaudreau ⁱ   Jaremko, Jacob L ^j   Reinhold, Caroline ^e   Gallix, Benoit ^e   Gray, Bruce ^f   Geis, Raym ^k   O'Connell, Timothy ^l   Babyn, Paul ^l   Koff, David ^l   Ferguson, Darren ^l   Derkatch, Sheldon ^l   Bilbily, Alexander ^l   Shabana, Wael ^l   more..

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b CENTRE HOSPITALIER DE L UNIVERSITÉ DE MONTRÉAL   (Canada)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^d UNIVERSITÉ LAVAL   (Canada)

^e MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

^f ST MICHAEL'S HOSPITAL   (Canada)

^g UNIVERSITY OF OTTAWA   (Canada)

^h MAYO CLINIC   (United States)

ⁱ UNIVERSITÉ DE SHERBROOKE   (Canada)

^j UNIVERSITY OF ALBERTA   (Canada)

^k DEPARTMENT OF MEDICINE   (United States)

^l NONE

more..

Author keywords

Artificial intelligence; Deep learning; Healthcare; Imaging; Machine learning; Medicine; Quality improvement; Radiology

Indexed keywords

ADOPTION; ADULT; ARTICLE; ARTIFICIAL INTELLIGENCE; CANADA; HUMAN; LEARNING ALGORITHM; LIFELONG LEARNING; NOMENCLATURE; PATIENT CARE; POPULATION HEALTH; RADIOLOGIST; RADIOLOGY; SPEECH DISCRIMINATION; TOTAL QUALITY MANAGEMENT; WORKFLOW; MEDICAL SOCIETY; PROCEDURES;

ARTIFICIAL INTELLIGENCE; CANADA; HUMANS; RADIOLOGISTS; RADIOLOGY; SOCIETIES, MEDICAL;

EID: 85045189457     PISSN: 08465371     EISSN: 14882361     Source Type: Journal    
DOI: 10.1016/j.carj.2018.02.002     Document Type: Review

References (59)

1. Goodfellow, I., Bengio, Y., Courville, A., Deep Learning. 1st ed., 2016, MIT Press, Cambridge, MA.
2. Merriam-Webster definition of artificial intelligence. Available at: https://www.merriam-webster.com/dictionary/artificial intelligence. Accessed January 28, 2018.
3. Guidance for Industry and Food and Drug Administration Staff - Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data - Premarket Notification [510(k)] Submissions. 2017. Available at: https://www.fda.gov/MedicalDevices/ucm187249.htm. Accessed January 28, 2018.
4. Arthur, S., Some Studies in Machine Learning Using the Game of Checkers. IBM J Res Dev 3 (1959), 535–554.
5. Google. Machine learning. Glossary. Available at: https://developers.google.com/machine-learning/glossary/#r. Accessed January 28, 2018.
6. Minka T. A statistical learning/pattern recognition glossary. Available at: http://alumni.media.mit.edu/∼tpminka/statlearn/glossary/. Accessed January 28, 2018.
7. McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E., A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27, 2006, 12.
8. Faggella D. What is machine learning? Techemergence. 2017. Available at: https://www.techemergence.com/what-is-machine-learning/. Accessed January 22, 2018.
9. Bahnsen AC. Easy Solutions, Inc. Building AI applications using deep learning. Available at: http://blog.easysol.net/building-ai-applications. Accessed January 28, 2018.
10. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
11. Chartrand, G., Cheng, P.M., Vorontsov, E., et al. Deep learning: a primer for radiologists. Radiographics 37 (2017), 2113–2131.
12. Canadian Association of Radiologists. Resources on AI, machine learning, and deep learning. Available at: https://car.ca/innovation/artificial-intelligence/ai-resources/. Accessed January 28, 2018.
13. American College of Radiology Data Science Institute. Use case development. Available at: http://acrdsi.org/use-case-development.html. Accessed January 28, 2018.
14. Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48 (2012), 441–446.
15. Christ PF. LiTS: liver tumor segmentation challenge. 2017. Available at: https://competitions.codalab.org/competitions/17094. Accessed January 28, 2018.
16. Pickup LC, Gleeson F, Talwar A, Kadir T. Lung nodule risk stratification using CNNs: can we generalize from screening training data? Conference on Machine Intelligence in Medical Imaging. September 26–27, 2017; Baltimore, MD.
17. Su, J., Vargas, D.V., Kouichi, S., One pixel attack for fooling deep neural networks. arXiv, 2017 [Epub ahead of print].
18. Evtimov, I., Eykholt, K., Fernandes, E., et al. Robust physical-world attacks on deep learning models. arXiv, 2017 [Epub ahead of print].
19. Traboulsee, A., Simon, J.H., Stone, L., et al. Revised recommendations of the Consortium of MS Centers task force for a standardized MRI protocol and clinical guidelines for the diagnosis and follow-up of multiple sclerosis. AJNR Am J Neuroradiol 37 (2016), 394–401.
20. Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277 (2015), 826–832.
21. Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162 (2015), 55–63.
22. Kamnitsas, K., Baumgartner, C., Ledig, C., et al. Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks. 2017, Springer International, Cham, Switzerland.
23. Canada needs to nurture local tech champions and protect research, says AI pioneer. National Post. 2017. Available at: http://nationalpost.com/pmn/news-pmn/canada-news-pmn/canada-needs-to-nurture-local-tech-champions-and-protect-research-says-ai-pioneer. Accessed January 28, 2018.
24. Deveau D. Revolution AI: Canada's early start in artificial intelligence set it up to be today's global powerhouse. Financial Post. 2017. Available at: http://business.financialpost.com/entrepreneur/0123-biz-dd-intelligence. Accessed January 28, 2018.
25. Government of Canada. Canada's health infostructure. 2004. Available at: https://www.canada.ca/en/health-canada/services/health-care-system/ehealth/canada-health-infostructure.html. Accessed January 28, 2018.
26. Canada Health Infoway. Standards centre. Available at: https://infocentral.infoway-inforoute.ca/en/standards/standards-selection-framework/standards-ssf. Accessed February 6, 2018.
27. Brosch, T., Yoo, Y., Tang, L., Tam, R., Deep learning of brain images and its application to multiple sclerosis. Wu, G., Shen, D., Sabuncu, M., (eds.) Machine Learning and Medical Imaging, 2016, Elsevier, New York, 69–96.
28. Radiology Informatics Lab at Stanford Medicine. Medical ImageNet. 2016. Available at: http://langlotzlab.stanford.edu/projects/medical-image-net/. Accessed January 28, 2018.
29. DICOM. Digital Imaging and Communications in Medicine. Available at: https://www.dicomstandard.org/. Accessed January 28, 2018.
30. DICOM PS3.6 2018a - data dictionary. 2018. Available at: http://dicom.nema.org/medical/dicom/current/output/html/part06.html. Accessed January 28, 2018.
31. Nelson GS. Practical implications of sharing data: a primer on data privacy, anonymization, and de-identification. Paper 1884–2015. SAS Global Forum Proceedings 2015. Available at: https://support.sas.com/resources/papers/proceedings15/1884-2015.pdf. Accessed January 28, 2018.
32. Council of Canadian Academies. Accessing Health and Health-Related Data in Canada: The Expert Panel on Timely Access to Health and Social Data for Health Research and Health System Innovation. 2015. Available at: http://www.scienceadvice.ca/uploads/eng/assessments%20and%20publications%20and%20news%20releases/Health-data/HealthDataFullReportEn.pdf. Accessed January 28, 2018.
33. Channin, D.S., Mongkolwat, P., Kleper, V., Rubin, D.L., The annotation and image mark-up project. Radiology 253 (2009), 590–592.
34. Cardoso, M.J., Arbel, T., Lee, S.L., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. 2017, Springer, New York.
35. Comparison of deep learning software. Available at: https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software. Accessed January 28, 2018.
36. Varangaonkar A. Top 10 deep learning frameworks. Packt Hub. 2017. Available at: https://datahub.packtpub.com/deep-learning/top-10-deep-learning-frameworks/. Accessed January 28, 2018.
37. Merkel, D., Docker: lightweight linux containers for consistent development and deployment. Linux J, 2014, 2014, 2.
38. Compute Canada. Available at: https://www.computecanada.ca/research-showcase/. Accessed February 6, 2018.
39. Bossuyt, P.M., Irwig, L., Craig, J., Glasziou, P., Comparative accuracy: assessing new tests against existing diagnostic pathways. BMJ 332 (2006), 1089–1092.
40. Becker, A.S., Marcon, M., Ghafoor, S., Wurnig, M.C., Frauenfelder, T., Boss, A., Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol 52 (2017), 434–440.
41. Gillies, R.J., Kinahan, P.E., Hricak, H., Radiomics: images are more than pictures, they are data. Radiology 278 (2016), 563–577.
42. American College of Radiology. Reporting and data systems. Available at: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems. Accessed January 28, 2018.
43. Pons, E., Braun, L.M., Hunink, M.G., Kors, J.A., Natural language processing in radiology: a systematic review. Radiology 279 (2016), 329–343.
44. Alkasab, T.K., Bizzo, B.C., Berland, L.L., Nair, S., Pandharipande, P.V., Harvey, H.B., Creation of an open framework for point-of-care computer-assisted reporting and decision support tools for radiologists. J Am Coll Radiol 14 (2017), 1184–1189.
45. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. arXiv, 2017 [Epub ahead of print].
46. Zalis, M., Harris, M., Advanced search of the electronic medical record: augmenting safety and efficiency in radiology. J Am Coll Radiol 7 (2010), 625–633.
47. Prior, F.W., Clark, K., Commean, P., et al. TCIA: an information resource to enable open science. Conf Proc IEEE Eng Med Biol Soc 2013 (2013), 1282–1285.
48. Wolstencroft, K., Haines, R., Fellows, D., et al. The Taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucleic Acids Res 41 (2013), W557–W561.
49. Dumontier, M., Baker, C.J., Baran, J., et al. The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery. J Biomed Semantics, 5, 2014, 14.
50. Langlotz, C.P., RadLex: a new method for indexing online educational materials. Radiographics 26 (2006), 1595–1597.
51. Creative Destruction Lab. Geoff Hinton: on radiology. 2016. Available at: https://www.youtube.com/watch?v=2HMPRXstSvQ. Accessed January 28, 2018.
52. Ng A. Should radiologists be worried about their jobs? Breaking news: We can now diagnose pneumonia from chest X-rays better than radiologists. 2017. Available at: https://twitter.com/andrewyng/status/930938692310482944?lang=en. Accessed January 28, 2018.
53. Kooi, T., Litjens, G., van Ginneken, B., et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35 (2017), 303–312.
54. Drozdzal, M., Chartrand, G., Vorontsov, E., et al. Learning normalized inputs for iterative estimation in medical image segmentation. Med Image Anal 44 (2018), 1–13.
55. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S., Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35 (2016), 1207–1216.
56. Shin, H.-C., Lu, L., Kim, L., Seff, A., Yao, J., Summers, R.M., Interleaved text/image deep mining on a large-scale radiology database for automated image interpretation. J Mach Learn Res 17 (2016), 1–31.
57. Litjens, G., Kooi, T., Bejnordi, B.E., et al. A survey on deep learning in medical image analysis. arXiv, 2017 [Epub ahead of print].
58. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H., Deep learning for identifying metastatic breast cancer. arXiv, 2016 [Epub ahead of print].
59. Lakhani, P., Sundaram, B., Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology 284 (2017), 574–582.