Artificial Intelligence in Neuroradiology: Current Status and Future Directions

References

1. 1.↵
   1. Pesapane F,
   2. Codari M,
   3. Sardanelli F

   . Artificial intelligence in machine learning: threat or opportunity? Radiologists again at the forefront of innovation. Eur Radiol Exp 2018;2:35 doi:10.1186/s41747-018-0061-6 pmid:30353365

   CrossRefPubMed
2. 2.↵
   1. Balasubramanian S

   . Artificial Intelligence is Not Ready for the Intricacies of Radiology. Forbes https://www.forbes.com/sites/saibala/2020/02/03/artificial-intelligence-is-not-ready-for-the-intricacies-of-radiology/#bc4b92b67eb1. February 3, 2020. Accessed March 15, 2020
3. 3.↵
   1. Curtis C,
   2. Liu C,
   3. Bollerman TJ, et al

   . Machine learning for predicting patient wait times and appointment delays. J Am Coll Radiol 2018;15:1310–16 doi:10.1016/j.jacr.2017.08.021 pmid:29079248

   CrossRefPubMed
4. 4.↵
   1. Ginat DT

   . Analysis of head CT flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology 2020;62:335–40 doi:10.1007/s00234-019-02330-w pmid:31828361

   CrossRefPubMed
5. 5.↵
   1. Kuo W,
   2. Häne C,
   3. Mukherjee P, et al

   . Expert-level detection of acute intracranial hemorrhage on computed tomography using deep learning. Proc Natl Acad Sci USA 2019;116:22737–45 doi:10.1073/pnas.1908021116 pmid:31636195

   Abstract/FREE Full Text
6. 6.↵
   1. Chang PD,
   2. Kuoy E,
   3. Grinband J, et al

   . Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am J Neuroradiol 2018;39:1609–16 doi:10.3174/ajnr.A5742 pmid:30049723

   Abstract/FREE Full Text
7. 7.↵
   1. Cho J,
   2. Park KS,
   3. Karki M, et al

   . Improved sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models. J Digit Imaging 2019;32:450–61 doi:10.1007/s10278-018-00172-1 pmid:30680471

   CrossRefPubMed
8. 8.↵
   1. Lee H,
   2. Yune S,
   3. Mansouri M, et al

   . An explainable deep learning algorithm for the detection of acute intracranial hemorrhage from small datasets. Nat Biomed Eng 2019;3:173–82 doi:10.1038/s41551-018-0324-9 pmid:30948806

   CrossRefPubMed
9. 9.↵
   1. Ye H,
   2. Gao F,
   3. Yin Y, et al

   . Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 2019;29:6191–01 doi:10.1007/s00330-019-06163-2 pmid:31041565

   CrossRefPubMed
10. 10.↵
    1. Chilamkurthy S,
    2. Ghosh R,
    3. Tanamala S, et al

    . Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018;392:2388–96 doi:10.1016/S0140-6736(18)31645-3 pmid:30318264

    CrossRefPubMed
11. 11.↵
    1. Yu Y,
    2. Xie Y,
    3. Thamm T, et al

    . Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging. JAMA Netw Open 2020;3:e200722 doi:10.1001/jamanetworkopen.2020.0772

    CrossRef
12. 12.↵
    1. Ho KC,
    2. Scalzo F,
    3. Sarma KV, et al

    . Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images. J Med Imag 2019;6:1 doi:10.1117/1.JMI.6.2.026001

    CrossRef
13. 13.↵
    1. Heo J,
    2. Yoon JG,
    3. Park H, et al

    . Machine-learning based model for prediction of outcomes in acute stroke. Stroke 2019;50:1263–65 doi:10.1161/STROKEAHA.118.024293 pmid:30890116

    CrossRefPubMed
14. 14.↵
    1. Nielsen A,
    2. Hansen MB,
    3. Tietze A, et al

    . Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke 2018;49:1394–401 doi:10.1161/STROKEAHA.117.019740 pmid:29720437

    Abstract/FREE Full Text
15. 15.↵
    1. Murray NM,
    2. Unberath M,
    3. Hager GD, et al

    . Artificial intelligence to diagnose ischemic stroke and large vessel occlusions: a systematic review. J Neurointerv Surg 2020;12:156–64 doi:10.1136/neurintsurg-2019-015135 pmid:31594798

    Abstract/FREE Full Text
16. 16.↵
    1. Alawieh A,
    2. Zaraket F,
    3. Alawieh MB, et al

    . Use of machine learning to optimize selection of elderly patients for endovascular thrombectomy. J Neurointerv Surg 2019;11:847–51 doi:10.1136/neurintsurg-2018-014381 pmid:30712013

    Abstract/FREE Full Text
17. 17.↵
    1. Sichtermann T,
    2. Faron A,
    3. Sijben R, et al

    . Deep learning-based detection of intracranial aneurysms in 3D TOF-MRA. AJNR Am J Neuroradiol 2019;40:25–32 doi:10.3174/ajnr.A5911 pmid:30573461

    Abstract/FREE Full Text
18. 18.↵
    1. Park A,
    2. Chute C,
    3. Rajpurkar P, et al

    . Deep learning-assisted diagnosis of intracranial aneurysms using the HeadXNet model. JAMA Netw Open 2019;2:e195600 doi:10.1001/jamanetworkopen.2019.5600 pmid:31173130

    CrossRefPubMed
19. 19.↵
    1. Stember JN,
    2. Chang P,
    3. Stember DM, et al

    . Convolutional neural networks for the detection and measurement of cerebral aneurysms on magnetic resonance angiography. J Digit Imaging 2019;32:808–15 doi:10.1007/s10278-018-0162-z pmid:30511281

    CrossRefPubMed
20. 20.↵
    1. Stone JR,
    2. Wilde EA,
    3. Taylor RA, et al

    . Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury. Brain Inj 2016;30:1458–68 doi:10.1080/02699052.2016.1222080 pmid:27834541

    CrossRefPubMed
21. 21.↵
    1. Jain S,
    2. Vyvere TV,
    3. Terzopoulos V, et al

    . Automatic quantification of computed tomography features in acute traumatic brain injury. J Neurotrauma 2019;36:1794–1803 doi:10.1089/neu.2018.6183 pmid:30648469

    CrossRefPubMed
22. 22.↵
    1. Kerley CI,
    2. Huo Y,
    3. Chaganti S, et al

    . Montage based 3D medical image retrieval from traumatic brain injury cohort using deep convolutional neural network. Proc SPIE Int Soc Opt Eng 2019;10949:109492U doi:10.1117/12.2512559 pmid:31762533

    CrossRefPubMed
23. 23.↵
    1. Li F,
    2. Liu M

    ; Alzheimer's Disease Neuroimaging Initiative. A hybrid convolutional and recurrent neural network for hippocampal analysis in Alzheimer’s disease. J Neurosci Methods 2019;323:108–18 doi:10.1016/j.jneumeth.2019.05.006 pmid:31132373

    CrossRefPubMed
24. 24.↵
    1. ;

    1. Liu M,
    2. Li F,
    3. Yan H, et al

    ; Alzheimer’s Disease Neuroimaging Initiative. A multi-modal deep convolutional neural network for automatic hippocampal segmentation and classification in Alzheimer’s disease. Neuroimage 2020;208:116459 doi:10.1016/j.neuroimage.2019.116459 pmid:31837471

    CrossRefPubMed
25. 25.↵
    1. Huff TJ,
    2. Ludwig PE,
    3. Salazar D, et al

    . Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume. Int J Comput Assist Radiol Surg.2019;14:1923–32 doi:10.1007/s11548-019-02038-5 pmid:31350705

    CrossRefPubMed
26. 26.↵
    1. Klimont M,
    2. Flieger M,
    3. Rzeszutek J, et al

    . Automated ventricular system segmentation in pediatric patients treated for hydrocephalus using deep learning methods. Biomed Res Int 2019;2019:3059170 doi:10.1155/2019/3059170 pmid:31360710

    CrossRefPubMed
27. 27.↵
    1. Irie R,
    2. Otsuka Y,
    3. Hagiwara A, et al

    . A novel deep learning approach with 3D convolutional ladder network for differential diagnosis of idiopathic normal pressure hydrocephalus and Alzheimer’s disease. Magn Reson Med Sci 2020 Jan 22 [Epub ahead of print] doi:10.2463/mrms.mp.2019-0106 pmid:1969525

    CrossRefPubMed
28. 28.↵
    1. Narayana PA,
    2. Coronado I,
    3. Sujit SJ, et al

    . Deep learning for predicting enhancing lesions in multiple sclerosis from noncontrast MRI. Radiology 2020;294:398–404 doi:10.1148/radiol.2019191061 pmid:31845845

    CrossRefPubMed
29. 29.↵
    1. Zhao Y,
    2. Healy BC,
    3. Rotstein D, et al

    . Exploration of machine learning techniques in prediction of multiple sclerosis disease course. PLoS One 2017;12:e0174866 doi:10.1371/journal.pone.0174866 pmid:28379999

    CrossRefPubMed
30. 30.↵
    1. Ion-Margineanu A,
    2. Kocevar G,
    3. Stamile C, et al

    . Machine learning approach for classifying mutliple sclerosis courses by combining clinical data with lesion loads and magnetic resonance metabolic features. Front Neurosci 2017;11:398 doi:10.3389/fnins.2017.00398 pmid:8744195

    CrossRefPubMed
31. 31.↵
    1. Lao J,
    2. Chen Y,
    3. Li ZC, et al

    . A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep 2017;7:10353 doi:10.1038/s41598-017-10649-8 pmid:28871110

    CrossRefPubMed
32. 32.↵
    1. Mobadersany P,
    2. Yousefi S,
    3. Amgad M, et al

    . Predicting cancer outcomes from histology and genetics using convolutional neural networks. Proc Natl Acad Sci USA 2018;115:E2970–79 doi:10.1073/pnas.1717139115 pmid:29531073

    Abstract/FREE Full Text
33. 33.↵
    1. Charron O,
    2. Lallement A,
    3. Jarnet D, et al

    . Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 2018;95:43–54 doi:10.1016/j.compbiomed.2018.02.004 pmid:29455079

    CrossRefPubMed
34. 34.↵
    1. Xue J,
    2. Wang B,
    3. Ming Y, et al

    . Deep-learning-based detection and segmentation-assisted management on brain metastases. Neuro Oncol 2020;22:505–14 doi:10.1093/neuonc/noz234] pmid:31867599

    CrossRefPubMed
35. 35.↵
    1. Grovik E,
    2. Yi D,
    3. Iv M, et al

    . Deep learning enables automatic detection and segmentation of brain metastases on mulitsequence MRI. J Magn Reson Imaging 2020;51:175–82 doi:10.1002/jmri.26766 pmid:31050074

    CrossRefPubMed
36. 36.↵
    1. Burns JE,
    2. Yao J,
    3. Summers RM

    . Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology 2017;284:788–97 doi:10.1148/radiol.2017162100 pmid:28301777

    CrossRefPubMed
37. 37.↵
    1. Lessmann N,
    2. van Ginneken B,
    3. de Jong PA, et al

    . Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 2019;53:142–55 doi:10.1016/j.media.2019.02.005 pmid:30771712

    CrossRefPubMed
38. 38.↵
    1. Zhao C,
    2. Shao M,
    3. Carass A, et al

    . Applications of a deep learning method for anti-aliasing and super-resolution in MRI. Magn Reson Imaging 2019;64:132–41 doi:10.1016/j.mri.2019.05.038 pmid:31247254

    CrossRefPubMed
39. 39.↵
    1. Sreekumari A,
    2. Shanbhag D,
    3. Yeo D, et al

    . A deep learning-based approach to reduce rescan and recall rates in clinical MRI examinations. AJNR Am J Neuroradiol 2019;40:217–23 doi:10.3174/ajnr.A5926 pmid:30606726

    Abstract/FREE Full Text
40. 40.↵
    1. Mardani M,
    2. Gong E,
    3. Cheng JY, et al

    . Deep generative adversarial neural networks for compressed sensing MRI. IEEE Trans Med Imaging 2019;38:167–79 doi:10.1109/TMI.2018.2858752 pmid:30040634

    CrossRefPubMed
41. 41.↵
    1. Lee D,
    2. Yoo J,
    3. Tak S, et al

    . Deep residual learning for accelerated MRI using magnitude and phase networks. IEEE Trans Biomed Eng 2018;65:1985–95 doi:10.1109/TBME.2018.2821699 pmid:29993390

    CrossRefPubMed
42. 42.↵
    1. Ouyang J,
    2. Chen KT,
    3. Gong E, et al

    . Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys 2019;46:3555–64 doi:10.1002/mp.13626 pmid:31131901

    CrossRefPubMed
43. 43.↵
    1. Gong E,
    2. Pauly JM,
    3. Wintermark M, et al

    . Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J Magn Reson Imaging 2018;48:330–40 doi:10.1002/jmri.25970 pmid:29437269

    CrossRefPubMed
44. 44.↵
    1. Dong C,
    2. Loy CC,
    3. He K, et al

    . Image super-resolution using deep convolutional neural networks. IEEE Trans Pattern Anal Mach Intell 2016;38:295–307 doi:10.1109/TPAMI.2015.2439281 pmid:26761735

    CrossRefPubMed
45. 45.↵
    Subtle Medical Receives FDA 510 (K) Clearance for AI-Powered Subtle MRI. October 15, 2019. https://finance.yahoo.com/news/subtle-medical-receives-fda-510-121200456.html. Accessed April 20, 2020
46. 46.↵
    ClariPI Gets FDA Clearance for AI-Powered CT Image Denoising Solution. June 24, 2019. http://www.itnonline.com/claripi-gets-fda-clearance-for-ai-powered-ct-image-denoising-solution. Accessed April 27, 2020
47. 47.↵
    1. Chang P,
    2. Grinband J,
    3. Weinberg BD, et al

    . Deep-learning convolutional neural networks accurately classify genetic mutations in glioma. AJNR Am J Neuroradiol 2018;39:1201–17 doi:10.3174/ajnr.A5667 pmid:29748206

    Abstract/FREE Full Text
48. 48.↵
    1. Young JD,
    2. Cai C,
    3. Lu X

    . Unsupervised deep learning reveals subtypes of glioblastoma. BMC Bioinformatics 2017;18:381 doi:10.1186/s12859-017-1798-2 pmid:28984190

    CrossRefPubMed
49. 49.↵
    1. Chang K,
    2. Bai HX,
    3. Zhou H, et al

    . Residual convolutional neural network for the determination of IDH status in low-and high-grade gliomas from MR imaging. Clin Cancer Res 2018;24:1073–81 doi:10.1158/1078-0432.CCR-17-2236 pmid:29167275

    Abstract/FREE Full Text
50. 50.↵
    1. Fujima N,
    2. Andreu-Arasa VC,
    3. Meibom SK, et al

    . Prediction of the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma by FDG-PET imaging dataset using deep learning analysis: a hypothesis-generating study. Eur J Radiol 2020;126:108936 doi:10.1016/j.ejrad.2020.108936 pmid:32171912

    CrossRefPubMed
51. 51.↵
    1. Han W,
    2. Qin L,
    3. Bay C, et al

    . Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. AJNR Am J Neuroradiol 2020;41:40–48 doi:10.3174/ajnr.A6365 pmid:31857325

    Abstract/FREE Full Text
52. 52.↵
    1. Sun L,
    2. Zhang S,
    3. Chen H, et al

    . Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front Neurosci 2019;13:810 doi:10.3389/fnins.2019.00810 pmid:31474816

    CrossRefPubMed
53. 53.↵
    1. Li H,
    2. Habes M,
    3. Wolk DA,
    4. Fan Y

    ; Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Study of Aging. A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimers Dement 2019;15:1059–70 doi:10.1016/j.jalz.2019.02.007 pmid:31201098

    CrossRefPubMed
54. 54.↵
    1. Basaia S,
    2. Agosta F,
    3. Wagner L, et al

    ; Alzheimer's Disease Neuroimaging Initiative. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019;21:101645 doi:10.1016/j.nicl.2018.101645 pmid:30584016

    CrossRefPubMed
55. 55.↵
    1. Combs TS,
    2. Sandt LS,
    3. Clamann MP, et al

    . Automated vehicles and pedestrian safety: exploring the promise and limits of pedestrian safety. Am J Prev Med 2019;56:1–7 doi:10.1016/j.amepre.2018.06.024 pmid:30337236

    CrossRefPubMed
56. 56.↵
    1. Holodny AI

    . “Am I about to lose my job?!”: A comment on “computer-extracted texture features to distinguish cerebral radiation necrosis from recurrent brain tumors on multiparametric MRI—a feasibility study.” AJNR Am J Neuroradiol 2016;37:2237–38 doi:10.3174/ajnr.A5002 pmid:27737854

    FREE Full Text
57. 57.↵
    1. Thrall MJ

    . Automated screening of Papanicolaou tests: a review of the literature. Diagn Cytopathol 2019;47:20–27 doi:10.1002/dc.23931 pmid:29603675

    CrossRefPubMed
58. 58.↵
    1. Kitchener HC,
    2. Blanks R,
    3. Cubie H, et al

    ; MAVARIC Trial Study Group. MAVARIC: a comparison of automation-assisted and manual cervical screening—a randomised controlled trial. Health Technol Assess 2011;15:1–70 doi:10.3310/hta15030 pmid:21266159

    CrossRefPubMed
59. 59.↵
    1. Schläpfer J,
    2. Wellens HJ

    . Computer-interpreted electrocardiograms: benefits and limitations. J Am Coll Cardiol 2017;70:1183–92 doi:10.1016/j.jacc.2017.07.723 pmid:28838369

    FREE Full Text
60. 60.↵
    1. Lindow T,
    2. Kron J,
    3. Thulesius H, et al

    . Erroneous computer-based interpretations of atrial fibrillation and atrial flutter in a Swedish primary health care setting. Scand J Prim Health Care 2019;37:426–33 doi:10.1080/02813432.2019.1684429 pmid:31684791

    CrossRefPubMed
61. 61.↵
    1. Thrall JH,
    2. Li X,
    3. Li Q, et al

    . Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018;15:504–08 doi:10.1016/j.jacr.2017.12.026 pmid:29402533

    CrossRefPubMed
62. 62.↵
    1. Pakdemirli E

    . Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading? Acta Radiol Open 2019;8:2058460119830222 doi:10.1177/2058460119830222 pmid:30815280

    CrossRefPubMed
63. 63.↵
    1. Duong MT,
    2. Rauschecker AM,
    3. Rudie JD, et al

    . Artificial intelligence for precision education in radiology. Br J Radiol 2019;92:20190389 doi:10.1259/bjr.20190389 pmid:31322909

    CrossRefPubMed
64. 64.↵
    1. Flanders AE,
    2. Prevedello LM,
    3. Shih G, et al

    . Construction of a machine learning dataset through collaboration: the RSNA 2019 Brain CT Hemorrhage Challenge. Radiology: Artificial Intelligence 2020;2:e190211 doi:10.1148/ryai.2020190211

    CrossRef
65. 65.↵
    1. Yune S,
    2. Lee H,
    3. Pomerantz SR, et al

    . Real-world performance of deep-learning-based automated detection system for intracranial hemorrhage. In: Proceedings of the 2018 Society for Imaging Informatics in Imaging 2018 Conference on Machine Intelligence in Medical Imaging, San Francisco, California; September 9–10, 2018
66. 66.↵
    1. Guo J,
    2. Gong E,
    3. Fan AP, et al

    . Predicting 15O-water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias. J Cereb Blood Flow Metab 2019 Nov 13. [Epub ahead of print] doi:10.1177/0271678X19888123 pmid:31722599

    CrossRefPubMed
67. 67.↵
    1. Chen IY,
    2. Joshi S,
    3. Ghassemi M

    . Treating health disparities with artificial intelligence. Nat Med 2020;26:16–17 doi:10.1038/s41591-019-0649-2 pmid:31932779

    CrossRefPubMed