Advertisement

European Radiology

, Volume 29, Issue 3, pp 1616–1624 | Cite as

Demystification of AI-driven medical image interpretation: past, present and future

  • Peter Savadjiev
  • Jaron Chong
  • Anthony Dohan
  • Maria Vakalopoulou
  • Caroline Reinhold
  • Nikos Paragios
  • Benoit GallixEmail author
Computer Applications

Abstract

The recent explosion of ‘big data’ has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it.

Key Points

• Artificial intelligence (AI) research in medical imaging has a long history

• The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods.

• A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.

Keywords

Diagnostic imaging Artificial intelligence (AI) Machine learning Computer-assisted image processing Computer-assisted image interpretation 

Abbreviations

AI

Artificial intelligence

ANN

Artificial neural network

LASSO

Least absolute shrinkage and selection operator

PICO

Patient/problem, intervention, comparison intervention and outcomes

TB

Tuberculosis

Notes

Funding

The authors state that this work has not received any funding.

Compliance with Ethical Standards

Guarantor

The scientific guarantor of this publication is Dr. Benoit Gallix.

Conflict of Interest

Professor Nikos Paragions declares a relationship with the following company: TheraPanacea, Paris, France.

The other co-authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and Biometry

No complex statistical methods were necessary for this paper.

Informed Consent

Written informed consent was not required for this study because this is a review article, no study was performed.

Ethical Approval

Institutional review board approval was not required because this is a review article and no study was performed.

References

  1. 1.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  2. 2.
    Tang A, Tam R, Cadrin-Chênevert A et al Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group (2018) Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 69:120–135Google Scholar
  3. 3.
    Summers RM (2016) Progress in fully automated abdominal CT interpretation. AJR Am J Roentgenol 207:67–79CrossRefGoogle Scholar
  4. 4.
    Matsuyama T (1989) Expert systems for image processing: knowledge-based composition of image analysis processes. Comput Vision Graph 48:22–49Google Scholar
  5. 5.
    Stansfield SA (1986) ANGY: a rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms. IEEE Trans Pattern Anal Mach Intell 2:188–199CrossRefGoogle Scholar
  6. 6.
    Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22:483–492CrossRefGoogle Scholar
  7. 7.
    Warfield SK, Zou KH, Wells WM. (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–21Google Scholar
  8. 8.
    Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal 26:1–18CrossRefGoogle Scholar
  9. 9.
    Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24:205–219CrossRefGoogle Scholar
  10. 10.
    Van Leemput K, Maes F, Vandermeulen D, Colchester A, Suetens P (2001) Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 20:677–688CrossRefGoogle Scholar
  11. 11.
    Prastawa M, Bullitt E, Moon N, Van Leemput K, Gerig G (2003) Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad Radiol 10:1341–1348CrossRefGoogle Scholar
  12. 12.
    Erus G, Zacharaki EI, Davatzikos C (2014) Individualized statistical learning from medical image databases: application to identification of brain lesions. Med Image Anal 18:542–554CrossRefGoogle Scholar
  13. 13.
    Viergever MA, Maintz JBA, Klein S, Murphy K, Staring M, Pluim JPW (2016) A survey of medical image registration - under review. Med Image Anal 33:140–144CrossRefGoogle Scholar
  14. 14.
    Kraus WL (2015) Editorial: would you like a hypothesis with those data? Omics and the age of discovery science. Mol Endocrinol 29:1531–1534CrossRefGoogle Scholar
  15. 15.
    Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2:1636–1642CrossRefGoogle Scholar
  16. 16.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
  17. 17.
    Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106Google Scholar
  18. 18.
    Matzner-Lober E, Suehs CM, Dohan A, Molinari N (2018) Thoughts on entering correlated imaging variables into a multivariable model: application to radiomics and texture analysis. Diagn Interv Imaging 99:269–270CrossRefGoogle Scholar
  19. 19.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  20. 20.
    Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267–288Google Scholar
  21. 21.
    Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131Google Scholar
  22. 22.
    Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard UnivGoogle Scholar
  23. 23.
    Rosenblatt F (1957). The Perceptron—a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical LaboratoryGoogle Scholar
  24. 24.
    Lawrence N (2016) Deep learning, Pachinko and James Watt: efficiency is the driver of uncertainty. http://inverseprobability.com/2016/03/04/deep-learning-and-uncertainty. Accessed 23 May 2018
  25. 25.
    Szegedy C, Zaremba W, Sutskever I et al (2013) Intriguing properties of neural networks. arXiv:1312.6199Google Scholar
  26. 26.
    Richardson WS, Wilson MC, Nishikawa J, Hayward RS (1995) The well-built clinical question: a key to evidence-based decisions. ACP J Club 123:A12–A13Google Scholar
  27. 27.
    Simmons JP, Nelson LD, Simonsohn U (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci 22:1359–1366CrossRefGoogle Scholar
  28. 28.
    Ferrante E, Dokania PK, Marini R, Paragios N (2017) Deformable registration through learning of context-specific metric aggregation. Machine Learning in Medical Imaging Workshop. MLMI (MICCAI 2017), Sep 2017, Quebec City, CanadaGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Peter Savadjiev
    • 1
  • Jaron Chong
    • 1
    • 2
  • Anthony Dohan
    • 1
    • 2
    • 3
  • Maria Vakalopoulou
    • 4
    • 5
  • Caroline Reinhold
    • 1
    • 2
  • Nikos Paragios
    • 4
    • 6
  • Benoit Gallix
    • 1
    • 2
    Email author
  1. 1.Department of Diagnostic RadiologyMcGill UniversityMontrealCanada
  2. 2.Department of Diagnostic RadiologyMcGill University Health CentreMontrealCanada
  3. 3.Department of Body & Interventional Imaging, Hôpital Lariboisière-AP-HPUniversité Diderot-Paris 7 and INSERM U965Paris Cedex 10France
  4. 4.Ecole CentraleSupelecGif-sur-YvetteFrance
  5. 5.Inria Saclay/Ile-de-FrancePalaiseauFrance
  6. 6.TheraPanaceaParisFrance

Personalised recommendations