Artificial Intelligence in Medical Imaging: Threat or Opportunity? Radiologists Again at the Forefront of Innovation in Medicine

Overview

Journal Eur Radiol Exp

Date 2018 Oct 25

PMID 30353365

Citations 200

Authors

Filippo Pesapane

Marina Codari

Francesco Sardanelli

Affiliations

Soon will be listed here.

Abstract

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.

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References

Lakhani P, Prater A, Hutson R, Andriole K, Dreyer K, Morey J . Machine Learning in Radiology: Applications Beyond Image Interpretation. J Am Coll Radiol. 2017; 15(2):350-359. DOI: 10.1016/j.jacr.2017.09.044. View

Sardanelli F . Trends in radiology and research. Eur Radiol Exp. 2018; 1(1):1. PMC: 5909338. DOI: 10.1186/s41747-017-0006-5. View

Makris G, Uberoi R . Interventional Radiology-The Future: Evolution or Extinction?. Cardiovasc Intervent Radiol. 2016; 39(12):1789-1790. DOI: 10.1007/s00270-016-1450-y. View

Sardanelli F, Ali M, Hunink M, Houssami N, Sconfienza L, Di Leo G . To share or not to share? Expected pros and cons of data sharing in radiological research. Eur Radiol. 2018; 28(6):2328-2335. DOI: 10.1007/s00330-017-5165-5. View

Moeskops P, Viergever M, Mendrik A, de Vries L, Benders M, Isgum I . Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. IEEE Trans Med Imaging. 2016; 35(5):1252-1261. DOI: 10.1109/TMI.2016.2548501. View

Becker A, Schneider M, Wurnig M, Wagner M, Clavien P, Boss A . Radiomics of liver MRI predict metastases in mice. Eur Radiol Exp. 2018; 2(1):11. PMC: 5971192. DOI: 10.1186/s41747-018-0044-7. View

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

van der Burgh H, Schmidt R, Westeneng H, de Reus M, van den Berg L, van den Heuvel M . Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. Neuroimage Clin. 2017; 13:361-369. PMC: 5219634. DOI: 10.1016/j.nicl.2016.10.008. View

Li R, Zhang W, Suk H, Wang L, Li J, Shen D . Deep learning based imaging data completion for improved brain disease diagnosis. Med Image Comput Comput Assist Interv. 2014; 17(Pt 3):305-12. PMC: 4464771. DOI: 10.1007/978-3-319-10443-0_39. View

10.

Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J . Low-dose CT via convolutional neural network. Biomed Opt Express. 2017; 8(2):679-694. PMC: 5330597. DOI: 10.1364/BOE.8.000679. View

11.

Peng S, Chen C, Liu H, Lui C, Huang Y, Lee T . Analysis of parametric histogram from dynamic contrast-enhanced MRI: application in evaluating brain tumor response to radiotherapy. NMR Biomed. 2012; 26(4):443-50. DOI: 10.1002/nbm.2882. View

12.

Obrzut B, Kusy M, Semczuk A, Obrzut M, Kluska J . Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods. BMC Cancer. 2017; 17(1):840. PMC: 5727988. DOI: 10.1186/s12885-017-3806-3. View

13.

Obermeyer Z, Emanuel E . Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016; 375(13):1216-9. PMC: 5070532. DOI: 10.1056/NEJMp1606181. View

14.

Lee J, Jun S, Cho Y, Lee H, Kim G, Seo J . Deep Learning in Medical Imaging: General Overview. Korean J Radiol. 2017; 18(4):570-584. PMC: 5447633. DOI: 10.3348/kjr.2017.18.4.570. View

15.

Pesapane F, Patella F, Fumarola E, Panella S, Ierardi A, Pompili G . Intravoxel Incoherent Motion (IVIM) Diffusion Weighted Imaging (DWI) in the Periferic Prostate Cancer Detection and Stratification. Med Oncol. 2017; 34(3):35. DOI: 10.1007/s12032-017-0892-7. View

16.

Sachs P, Gassert G, Cain M, Rubinstein D, Davey M, Decoteau D . Imaging study protocol selection in the electronic medical record. J Am Coll Radiol. 2013; 10(3):220-2. DOI: 10.1016/j.jacr.2012.11.004. View

17.

Recht M, Bryan R . Artificial Intelligence: Threat or Boon to Radiologists?. J Am Coll Radiol. 2017; 14(11):1476-1480. DOI: 10.1016/j.jacr.2017.07.007. View

18.

Lambin P, Leijenaar R, Deist T, Peerlings J, de Jong E, van Timmeren J . Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14(12):749-762. DOI: 10.1038/nrclinonc.2017.141. View

19.

Yip S, Parmar C, Kim J, Huynh E, Mak R, Aerts H . Impact of experimental design on PET radiomics in predicting somatic mutation status. Eur J Radiol. 2017; 97:8-15. DOI: 10.1016/j.ejrad.2017.10.009. View

20.

Golkov V, Dosovitskiy A, Sperl J, Menzel M, Czisch M, Samann P . q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. IEEE Trans Med Imaging. 2016; 35(5):1344-1351. DOI: 10.1109/TMI.2016.2551324. View