Home Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology
Article
Licensed
Unlicensed Requires Authentication

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology

  • Brian Fiani ORCID logo EMAIL logo , Kory B. Dylan Pasko , Kasra Sarhadi and Claudia Covarrubias
Published/Copyright: September 10, 2021
Become an author with De Gruyter Brill

Abstract

Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer’s disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.


Corresponding author: Brian Fiani, Department of Neurosurgery, Desert Regional Medical Center, 1150 N Indian Canyon Dr, Palm Springs, CA, 92262, USA, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The authors have not received any funding for this work from any organization.

  3. Conflict of interest statement: The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

References

2018. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. U.S. Food and Drug Administration.Search in Google Scholar

2019a. 2019 Alzheimer’s disease facts and figures report. Chicago, IL: Alzheimer’s Association.Search in Google Scholar

2019b. RSNA intracranial hemorrhage detection. Identify acute intracranial hemorrhage and its subtypes.Search in Google Scholar

Akbari, H., Macyszyn, L., Da, X., Bilello, M., Wolf, R.L., Martinez-Lage, M., Biros, G., Alonso-Basanta, M., O’Rourke, D.M., and Davatzikos, C. (2016). Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery 78: 572–580.10.1227/NEU.0000000000001202Search in Google Scholar

Akbari, H., Rathore, S., Bakas, S., Nasrallah, M., Rozycki, M., Mohan, S., Wolf, R., Bilello, M., Martinez-Lage, M., and Davatzikos, C. (2018). Nimg-70. Quantitative image analysis and machine learning techniques for distinguishing true progression from pseudoprogression in patients with glioblastoma. Neuro-Oncology 20: vi191–vi92.10.1093/neuonc/noy148.794Search in Google Scholar

Akkus, Z., Ali, I., Sedlar, J., Agrawal, J.P., Parney, I.F., Giannini, C., and Erickson, B.J. (2017). Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J. Digit. Imag. 30: 469–476.10.1007/s10278-017-9984-3Search in Google Scholar PubMed PubMed Central

Arab, A., Chinda, B., Medvedev, G., Siu, W., Guo, H., Gu, T., Moreno, S., Hamarneh, G., Ester, M., and Song, X. (2020). A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT. Sci. Rep. 10: 19389, https://doi.org/10.1227/neu.0000000000001202.Search in Google Scholar

Arbabshirani, M.R., Fornwalt, B.K., Mongelluzzo, G.J., Suever, J.D., Geise, B.D., Patel, A.A., and Moore, G.J. (2018). Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit. Med. 1: 9, https://doi.org/10.1093/neuonc/noy148.794.Search in Google Scholar

Armato, S.G., Petrick, N.A., Bar, A., Wolf, L., Bergman Amitai, O., Toledano, E., and Elnekave, E. (2017). Compression fractures detection on CT. Presented at Medical imaging 2017: computer-aided diagnosis.10.1117/12.2249635Search in Google Scholar

Aslani, S., Dayan, M., Storelli, L., Filippi, M., Murino, V., Rocca, M.A., and Sona, D. (2019). Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Neuroimage 196: 1–15, https://doi.org/10.1038/s41598-020-76459-7.Search in Google Scholar PubMed PubMed Central

Barber, P.A., Demchuk, A.M., Zhang, J., and Buchan, A.M. (2000). Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score. Lancet 355: 1670–1674, https://doi.org/10.1038/s41746-017-0015-z.Search in Google Scholar PubMed PubMed Central

Bijay Dev, K.M., Jogi, P.S., Niyas, S., Vinayagamani, S., Kesavadas, C., and Rajan, J. (2019). Automatic detection and localization of focal cortical dysplasia lesions in MRI using fully convolutional neural network. Biomed. Signal Process Contr. 52: 218–225.10.1016/j.bspc.2019.04.024Search in Google Scholar

Brossard, C., Lemasson, B., Attye, A., de Busschere, J.A., Payen, J.F., Barbier, E.L., Greze, J., and Bouzat, P. (2021). Contribution of CT-scan analysis by artificial intelligence to the clinical care of TBI patients. Front. Neurol. 12: 666875, https://doi.org/10.1016/j.neuroimage.2019.03.068.Search in Google Scholar PubMed

Brugnara, G., Isensee, F., Neuberger, U., Bonekamp, D., Petersen, J., Diem, R., Wildemann, B., Heiland, S., Wick, W., Bendszus, M., et al.. (2020). Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis. Eur. Radiol. 30: 2356–2364, https://doi.org/10.1016/s0140-6736(00)02237-6.Search in Google Scholar

Burduja, M., Ionescu, R.T., and Verga, N. (2020). Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks. Sensors 20, https://doi.org/10.1016/j.bspc.2019.04.024.Search in Google Scholar

Burns, J.E., Yao, J., and Summers, R.M. (2017). Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology 284: 788–797, https://doi.org/10.3389/fneur.2021.666875.Search in Google Scholar PubMed PubMed Central

Cao, C., Tutwiler, R.L., and Slobounov, S. (2008). Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine. IEEE Trans. Neural Syst. Rehabil. Eng. 16: 327–335, https://doi.org/10.1007/s00330-019-06593-y.Search in Google Scholar PubMed

Chang, P., Grinband, J., Weinberg, B.D., Bardis, M., Khy, M., Cadena, G., Su, M.Y., Cha, S., Filippi, C.G., Bota, D., et al.. (2018a). Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am. J. Neuroradiol. 39: 1201–1207, https://doi.org/10.3390/s20195611.Search in Google Scholar PubMed PubMed Central

Chang, P.D., Kuoy, E., Grinband, J., Weinberg, B.D., Thompson, M., Homo, R., Chen, J., Abcede, H., Shafie, M., Sugrue, L., et al.. (2018b). Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am. J. Neuroradiol. 39: 1609–1616, https://doi.org/10.1148/radiol.2017162100.Search in Google Scholar PubMed PubMed Central

Chartrand, G., Cheng, P.M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C.J., Kadoury, S., and Tang, A. (2017). Deep learning: a primer for radiologists. Radiographics 37: 2113–2131, https://doi.org/10.1109/tnsre.2008.918422.Search in Google Scholar PubMed

Chen, B., Xu, Q., Wang, L., Leung, S., Chung, J., and Li, S. (2019). An automated and accurate spine curve analysis system. IEEE Access 7: 124596–124605, https://doi.org/10.3174/ajnr.a5667.Search in Google Scholar

Chen, Y., Xie, Y., Zhou, Z., Shi, F., Christodoulou, A.G., and Li, D. (2018). Brain MRI super resolution using 3D deep densely connected neural networks. Presented at 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).10.1109/ISBI.2018.8363679Search in Google Scholar

Dawud, A.M., Yurtkan, K., and Oztoprak, H. (2019). Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput. Intell. Neurosci. 2019: 4629859, https://doi.org/10.1148/rg.2017170077.Search in Google Scholar PubMed

Del Gaizo, J., Mofrad, N., Jensen, J.H., Clark, D., Glenn, R., Helpern, J., and Bonilha, L. (2017). Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain Behav. 7: e00801, https://doi.org/10.1109/access.2019.2938402.Search in Google Scholar

Derkatch, S., Kirby, C., Kimelman, D., Jozani, M.J., Davidson, J.M., and Leslie, W.D. (2019). Identification of vertebral fractures by convolutional neural networks to predict nonvertebral and hip fractures: a registry-based cohort study of dual X-ray absorptiometry. Radiology 293: 405–411, https://doi.org/10.1109/isbi.2018.8363679.Search in Google Scholar

Dhar, R., Chen, Y., An, H., and Lee, J.M. (2018). Application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients. Front. Neurol. 9: 687, https://doi.org/10.1155/2019/4629859.Search in Google Scholar PubMed PubMed Central

Ding, Y., Sohn, J.H., Kawczynski, M.G., Trivedi, H., Harnish, R., Jenkins, N.W., Lituiev, D., Copeland, T.P., Aboian, M.S., Mari Aparici, C., et al.. (2019). A deep learning model to predict a diagnosis of Alzheimer disease by using (18)F-FDG PET of the brain. Radiology 290: 456–464, https://doi.org/10.1002/brb3.801.Search in Google Scholar PubMed PubMed Central

Eshaghi, A., Young, A.L., Wijeratne, P.A., Prados, F., Arnold, D.L., Narayanan, S., Guttmann, C.R.G., Barkhof, F., Alexander, D.C., Thompson, A.J., et al.. (2021). Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat. Commun. 12: 2078, https://doi.org/10.1148/radiol.2019190201.Search in Google Scholar PubMed

Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Med. Inf. Decis. Making 20, https://doi.org/10.3389/fneur.2018.00687.Search in Google Scholar PubMed PubMed Central

Faron, A., Sichtermann, T., Teichert, N., Luetkens, J.A., Keulers, A., Nikoubashman, O., Freiherr, J., Mpotsaris, A., and Wiesmann, M. (2020). Performance of a deep-learning neural network to detect intracranial aneurysms from 3D TOF-MRA compared to human readers. Clin. Neuroradiol. 30: 591–598, https://doi.org/10.1148/radiol.2018180958.Search in Google Scholar PubMed PubMed Central

Flanders, A.E., Prevedello, L.M., Shih, G., Halabi, S.S., Kalpathy-Cramer, J., Ball, R., Mongan, J.T., Stein, A., Kitamura, F.C., Lungren, M.P., et al.. (2020). Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge. Radiol. Artif. Intell. 2: e190211, https://doi.org/10.1038/s41467-021-22265-2.Search in Google Scholar PubMed PubMed Central

Friedman, J.H. (2002). Stochastic gradient boosting. Comput. Stat. Data Anal. 38: 367–378, https://doi.org/10.1186/s12911-020-01191-1.Search in Google Scholar PubMed PubMed Central

Furlan, A.J. (2006). Time is brain. Stroke 37: 2863–2864, https://doi.org/10.1007/s00062-019-00809-w.Search in Google Scholar PubMed

Gleichgerrcht, E., Munsell, B., Bhatia, S., Vandergrift, W.A.3rd, Rorden, C., McDonald, C., Edwards, J., Kuzniecky, R., and Bonilha, L. (2018). Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery. Epilepsia 59: 1643–1654, https://doi.org/10.1148/ryai.2020190211.Search in Google Scholar

Gordon, B.A., Blazey, T.M., Su, Y., Hari-Raj, A., Dincer, A., Flores, S., Christensen, J., McDade, E., Wang, G., Xiong, C., et al.. (2018). Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol. 17: 241–250, https://doi.org/10.1016/s0167-9473(01)00065-2.Search in Google Scholar

Guberina, N., Dietrich, U., Radbruch, A., Goebel, J., Deuschl, C., Ringelstein, A., Kohrmann, M., Kleinschnitz, C., Forsting, M., and Monninghoff, C. (2018). Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine. Neuroradiology 60: 889–901, https://doi.org/10.1161/01.str.0000251852.07152.63.Search in Google Scholar

Guo, B.J., Yang, Z.L., and Zhang, L.J. (2018). Gadolinium deposition in brain: current scientific evidence and future perspectives. Front. Mol. Neurosci. 11: 335, https://doi.org/10.1111/epi.14528.Search in Google Scholar

Hao, Y., Khoo, H.M., von Ellenrieder, N., Zazubovits, N., and Gotman, J. (2018). DeepIED: an epileptic discharge detector for EEG-fMRI based on deep learning. Neuroimage Clin. 17: 962–975, https://doi.org/10.1016/s1474-4422(18)30028-0.Search in Google Scholar

Hemphill, J.C.3rd, Greenberg, S.M., Anderson, C.S., Becker, K., Bendok, B.R., Cushman, M., Fung, G.L., Goldstein, J.N., Macdonald, R.L., Mitchell, P.H., et al.. (2015). Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart association/American stroke association. Stroke 46: 2032–2060, https://doi.org/10.1007/s00234-018-2066-5.Search in Google Scholar PubMed

Huang, J., Shen, H., Wu, J., Hu, X., Zhu, Z., Lv, X., Liu, Y., and Wang, Y. (2020). Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images. Spine J. 20: 590–599, https://doi.org/10.3389/fnmol.2018.00335.Search in Google Scholar PubMed PubMed Central

Hurford, R., Taveira, I., Kuker, W., Rothwell, P.M., and Oxford Vascular Study Phenotyped C. (2021). Prevalence, predictors and prognosis of incidental intracranial aneurysms in patients with suspected TIA and minor stroke: a population-based study and systematic review. J. Neurol. Neurosurg. Psychiatry 92: 542–548, https://doi.org/10.1016/j.nicl.2017.12.005.Search in Google Scholar PubMed PubMed Central

Jacobs, B., Beems, T., van der Vliet, T.M., Diaz-Arrastia, R.R., Borm, G.F., and Vos, P.E. (2011). Computed tomography and outcome in moderate and severe traumatic brain injury: hematoma volume and midline shift revisited. J. Neurotrauma 28: 203–215, https://doi.org/10.1161/str.0000000000000069.Search in Google Scholar

Jain, S., Vyvere, T.V., Terzopoulos, V., Sima, D.M., Roura, E., Maas, A., Wilms, G., and Verheyden, J. (2019). Automatic quantification of computed tomography features in acute traumatic brain injury. J. Neurotrauma 36: 1794–1803, https://doi.org/10.1016/j.spinee.2019.11.010.Search in Google Scholar PubMed

Kaesmacher, J., Chaloulos-Iakovidis, P., Panos, L., Mordasini, P., Michel, P., Hajdu, S.D., Ribo, M., Requena, M., Maegerlein, C., Friedrich, B., et al.. (2019). Mechanical thrombectomy in ischemic stroke patients with Alberta stroke program early computed tomography score 0–5. Stroke 50: 880–888, https://doi.org/10.1136/jnnp-2020-324418.Search in Google Scholar PubMed PubMed Central

Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., and Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36: 61–78, https://doi.org/10.1089/neu.2010.1558.Search in Google Scholar PubMed

Kanber, B., Nachev, P., Barkhof, F., Calvi, A., Cardoso, J., Cortese, R., Prados, F., Sudre, C.H., Tur, C., Ourselin, S., et al.. (2019). High-dimensional detection of imaging response to treatment in multiple sclerosis. NPJ Digit. Med. 2: 49, https://doi.org/10.1089/neu.2018.6183.Search in Google Scholar PubMed PubMed Central

Ker, J., Singh, S.P., Bai, Y., Rao, J., Lim, T., and Wang, L. (2019). Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans. Sensors 19, https://doi.org/10.1161/strokeaha.118.023465.Search in Google Scholar PubMed PubMed Central

Kickingereder, P., Burth, S., Wick, A., Gotz, M., Eidel, O., Schlemmer, H.P., Maier-Hein, K.H., Wick, W., Bendszus, M., Radbruch, A., et al.. (2016). Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280: 880–889, https://doi.org/10.1016/j.media.2016.10.004.Search in Google Scholar PubMed

Kim, J.P., Kim, J., Park, Y.H., Park, S.B., Lee, J.S., Yoo, S., Kim, E.J., Kim, H.J., Na, D.L., Brown, J.A., et al.. (2019). Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer’s disease. Neuroimage Clin. 23: 101811, https://doi.org/10.1038/s41746-019-0127-8.Search in Google Scholar PubMed PubMed Central

Kiral-Kornek, I., Roy, S., Nurse, E., Mashford, B., Karoly, P., Carroll, T., Payne, D., Saha, S., Baldassano, S., O’Brien, T., et al.. (2018). Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine 27: 103–111, https://doi.org/10.3390/s19092167.Search in Google Scholar PubMed PubMed Central

Korfiatis, P., Kline, T.L., Lachance, D.H., Parney, I.F., Buckner, J.C., and Erickson, B.J. (2017). Residual deep convolutional neural network predicts MGMT methylation status. J. Digit. Imag. 30: 622–628, https://doi.org/10.1148/radiol.2016160845.Search in Google Scholar PubMed

Kuo, W., Hne, C., Mukherjee, P., Malik, J., and Yuh, E.L. (2019). Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc. Natl. Acad. Sci. U. S. A. 116: 22737–22745, https://doi.org/10.1016/j.nicl.2019.101811.Search in Google Scholar PubMed PubMed Central

Lai, C.Q., Abdullah, M.Z., Abdullah, J.M., Azman, A., and Ibrahim, H. (2019). Screening of moderate traumatic brain injury from power feature of resting state electroencephalography using support vector machine. Presented at Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology.10.1145/3362752.3362758Search in Google Scholar

Lai, C.Q., Ibrahim, H., Abd. Hamid, A.I., Abdullah, M.Z., Azman, A., and Abdullah, J.M. (2020). Detection of moderate traumatic brain injury from resting-state eye-closed electroencephalography. Comput. Intell. Neurosci. 2020: 1–10, https://doi.org/10.1007/s10278-017-0009-z.Search in Google Scholar PubMed PubMed Central

Lessmann, N., van Ginneken, B., de Jong, P.A., and Išgum, I. (2019). Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med. Image Anal. 53: 142–155, https://doi.org/10.1073/pnas.1908021116.Search in Google Scholar

Li, H., Habes, M., Wolk, D.A., Fan, Y., and Alzheimer’s Disease Neuroimaging I, the Australian Imaging B and Lifestyle Study of A. (2019). A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimers Dement 15: 1059–1070, https://doi.org/10.1145/3362752.3362758.Search in Google Scholar

Macyszyn, L., Akbari, H., Pisapia, J.M., Da, X., Attiah, M., Pigrish, V., Bi, Y., Pal, S., Davuluri, R.V., Roccograndi, L., et al.. (2016). Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro-Oncology 18: 417–425, https://doi.org/10.1155/2020/8923906.Search in Google Scholar

Maegerlein, C., Fischer, J., Monch, S., Berndt, M., Wunderlich, S., Seifert, C.L., Lehm, M., Boeckh-Behrens, T., Zimmer, C., and Friedrich, B. (2019). Automated calculation of the Alberta stroke program early CT score: feasibility and reliability. Radiology 291: 141–148, https://doi.org/10.1016/j.media.2019.02.005.Search in Google Scholar

Majumdar, A., Brattain, L., Telfer, B., Farris, C., and Scalera, J. (2018). Detecting intracranial hemorrhage with deep learning. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018: 583–587, https://doi.org/10.1016/j.jalz.2019.02.007.Search in Google Scholar

McCulloch, W.S., and Pitts, W. (1990). A logical calculus of the ideas immanent in nervous activity. 1943. Bull. Math. Biol. 52: 99–115, https://doi.org/10.1093/neuonc/nov127.Search in Google Scholar

McKinley, R., Hung, F., Wiest, R., Liebeskind, D.S., and Scalzo, F. (2018). A machine learning approach to perfusion imaging with dynamic susceptibility contrast MR. Front. Neurol. 9: 717, https://doi.org/10.1148/radiol.2019181228.Search in Google Scholar

McNerney, M.W., Hobday, T., Cole, B., Ganong, R., Winans, N., Matthews, D., Hood, J., and Lane, S. (2019). Objective classification of mTBI using machine learning on a combination of frontopolar electroencephalography measurements and self-reported symptoms. Sports Med. Open 5: 14, https://doi.org/10.1109/EMBC.2018.8512336.Search in Google Scholar

Mollura, D.J., Culp, M.P., Pollack, E., Battino, G., Scheel, J.R., Mango, V.L., Elahi, A., Schweitzer, A., and Dako, F. (2020). Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology 297: 513–520, https://doi.org/10.1016/s0092-8240(05)80006-0.Search in Google Scholar

Monteiro, M., Newcombe, V.F.J., Mathieu, F., Adatia, K., Kamnitsas, K., Ferrante, E., Das, T., Whitehouse, D., Rueckert, D., Menon, D.K, et al.. (2020). Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. Lancet Digit Health 2: e314–e22, https://doi.org/10.3389/fneur.2018.00717.Search in Google Scholar PubMed PubMed Central

Nair, T., Precup, D., Arnold, D.L., and Arbel, T. (2020). Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Med. Image Anal. 59: 101557, https://doi.org/10.1186/s40798-019-0187-y.Search in Google Scholar

Nakao, T., Hanaoka, S., Nomura, Y., Sato, I., Nemoto, M., Miki, S., Maeda, E., Yoshikawa, T., Hayashi, N., and Abe, O. (2018). Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J. Magn. Reson. Imag. 47: 948–953, https://doi.org/10.1148/radiol.2020201434.Search in Google Scholar

Narayana, P.A., Coronado, I., Sujit, S.J., Wolinsky, J.S., Lublin, F.D., and Gabr, R.E. (2020). Deep learning for predicting enhancing lesions in multiple sclerosis from noncontrast MRI. Radiology 294: 398–404, https://doi.org/10.1016/s2589-7500(20)30085-6.Search in Google Scholar

Niemeyer, F., Galbusera, F., Tao, Y., Kienle, A., Beer, M., and Wilke, H.J. (2021). A deep learning model for the accurate and reliable classification of disc degeneration based on MRI data. Invest. Radiol. 56: 78–85, https://doi.org/10.1016/j.media.2019.101557.Search in Google Scholar PubMed

Nishi, H., Oishi, N., Ishii, A., Ono, I., Ogura, T., Sunohara, T., Chihara, H., Fukumitsu, R., Okawa, M., Yamana, N., et al.. (2020). Deep learning-derived high-level neuroimaging features predict clinical outcomes for large vessel occlusion. Stroke 51: 1484–1492, https://doi.org/10.1002/jmri.25842.Search in Google Scholar PubMed

Olthof, A.W., van Ooijen, P.M.A., and Rezazade Mehrizi, M.H. (2020). Promises of artificial intelligence in neuroradiology: a systematic technographic review. Neuroradiology 62: 1265–1278, https://doi.org/10.1148/radiol.2019191061.Search in Google Scholar PubMed PubMed Central

Park, A., Chute, C., Rajpurkar, P., Lou, J., Ball, R.L., Shpanskaya, K., Jabarkheel, R., Kim, L.H., McKenna, E., Tseng, J., et al.. (2019). Deep learning-assisted diagnosis of cerebral aneurysms using the HeadXNet model. JAMA Netw. Open 2: e195600, https://doi.org/10.1097/rli.0000000000000709.Search in Google Scholar

Park, J., Hwang, D., Kim, K.Y., Kang, S.K., Kim, Y.K., and Lee, J.S. (2018). Computed tomography super-resolution using deep convolutional neural network. Phys. Med. Biol. 63: 145011, https://doi.org/10.1161/strokeaha.119.028101.Search in Google Scholar

Pinto, M.F., Oliveira, H., Batista, S., Cruz, L., Pinto, M., Correia, I., Martins, P., and Teixeira, C. (2020). Prediction of disease progression and outcomes in multiple sclerosis with machine learning. Sci. Rep. 10: 21038, https://doi.org/10.1007/s00234-020-02424-w.Search in Google Scholar PubMed PubMed Central

Prevedello, L.M., Erdal, B.S., Ryu, J.L., Little, K.J., Demirer, M., Qian, S., and White, R.D. (2017). Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285: 923–931, https://doi.org/10.1001/jamanetworkopen.2019.5600.Search in Google Scholar PubMed PubMed Central

Pszczolkowski, S., Law, Z.K., Gallagher, R.G., Meng, D., Swienton, D.J., Morgan, P.S., Bath, P.M., Sprigg, N., and Dineen, R.A. (2019). Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage. Comput. Biol. Med. 106: 126–139, https://doi.org/10.1088/1361-6560/aacdd4.Search in Google Scholar PubMed

Rao, B., Zohrabian, V., Cedeno, P., Saha, A., Pahade, J., and Davis, M.A. (2021). Utility of artificial intelligence tool as a prospective radiology peer reviewer-detection of unreported intracranial hemorrhage. Acad. Radiol. 28: 85–93, https://doi.org/10.1038/s41598-020-78212-6.Search in Google Scholar PubMed PubMed Central

Rathore, S., Akbari, H., Doshi, J., Shukla, G., Rozycki, M., Bilello, M., Lustig, R., and Davatzikos, C. (2018). Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. J. Med. Imag. 5: 021219, https://doi.org/10.1148/radiol.2017162664.Search in Google Scholar PubMed

Rodriguez, S., Hug, C., Todorov, P., Moret, N., Boswell, S.A., Evans, K., Zhou, G., Johnson, N.T., Hyman, B.T., Sorger, P.K., et al.. (2021). Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nat. Commun. 12: 1033, https://doi.org/10.1016/j.compbiomed.2019.01.022.Search in Google Scholar PubMed PubMed Central

Rudie, J.D., Rauschecker, A.M., Bryan, R.N., Davatzikos, C., and Mohan, S. (2019). Emerging applications of artificial intelligence in neuro-oncology. Radiology 290: 607–618, https://doi.org/10.1016/j.acra.2020.01.035.Search in Google Scholar PubMed

Sander, L., Pezold, S., Andermatt, S., Amann, M., Meier, D., Wendebourg, M.J., Sinnecker, T., Radue, E.W., Naegelin, Y., Granziera, C, et al.. (2019). Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases. Hum. Brain Mapp. 40: 4091–4104, https://doi.org/10.1117/1.JMI.5.2.021219.Search in Google Scholar PubMed PubMed Central

Schweitzer, A.D., Niogi, S.N., Whitlow, C.T., and Tsiouris, A.J. (2019). Traumatic brain injury: imaging patterns and complications. Radiographics 39: 1571–1595, https://doi.org/10.1038/s41467-021-21330-0.Search in Google Scholar PubMed PubMed Central

Shi, Z., Miao, C., Schoepf, U.J., Savage, R.H., Dargis, D.M., Pan, C., Chai, X., Li, X.L., Xia, S., Zhang, X., et al.. (2020). A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images. Nat. Commun. 11: 6090, https://doi.org/10.1148/radiol.2018181928.Search in Google Scholar PubMed PubMed Central

Sichtermann, T., Faron, A., Sijben, R., Teichert, N., Freiherr, J., and Wiesmann, M. (2019). Deep learning-based detection of intracranial aneurysms in 3D TOF-MRA. AJNR Am. J. Neuroradiol. 40: 25–32, https://doi.org/10.1002/hbm.24687.Search in Google Scholar PubMed PubMed Central

Thompson, B.G., Brown, R.D.Jr., Amin-Hanjani, S., Broderick, J.P., Cockroft, K.M., Connolly, E.S.Jr., Duckwiler, G.R., Harris, C.C., Howard, V.J., Johnston, S.C, et al.. (2015). Guidelines for the management of patients with unruptured intracranial aneurysms: a guideline for healthcare professionals from the American Heart association/American stroke association. Stroke 46: 2368–2400, https://doi.org/10.1148/rg.2019190076.Search in Google Scholar PubMed

Titano, J.J., Badgeley, M., Schefflein, J., Pain, M., Su, A., Cai, M., Swinburne, N., Zech, J., Kim, J., Bederson, J., et al.. (2018). Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24: 1337–1341, https://doi.org/10.1038/s41467-020-19527-w.Search in Google Scholar PubMed PubMed Central

Ueda, D., Katayama, Y., Yamamoto, A., Ichinose, T., Arima, H., Watanabe, Y., Walston, S.L., Tatekawa, H., Takita, H., Honjo, T., et al.. (2021). Deep learning-based angiogram generation model for cerebral angiography without misregistration artifacts. Radiology 299: 675–681, https://doi.org/10.3174/ajnr.a5911.Search in Google Scholar PubMed PubMed Central

Ueda, D., Yamamoto, A., Nishimori, M., Shimono, T., Doishita, S., Shimazaki, A., Katayama, Y., Fukumoto, S., Choppin, A., Shimahara, Y., et al.. (2019). Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology 290: 187–194, https://doi.org/10.1161/str.0000000000000070.Search in Google Scholar

van den Brink, R.L., Nieuwenhuis, S., van Boxtel, G.J.M., van Luijtelaar, G., Eilander, H.J., and Wijnen, V.J.M. (2018). Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury. Neuroimage Clin. 17: 43–52, https://doi.org/10.1038/s41591-018-0147-y.Search in Google Scholar PubMed

Wang, H.-C., Ho, S.-H., Xiao, F., and Chou, J.-H. (2017). A simple, fast and fully automated approach for midline shift measurement on brain computed tomography.Search in Google Scholar

Wang, L., Xu, Q., Leung, S., Chung, J., Chen, B., and Li, S. (2019). Accurate automated Cobb angles estimation using multi-view extrapolation net. Med. Image Anal. 58: 101542, https://doi.org/10.1148/radiol.2018180901.Search in Google Scholar PubMed

Wang, S.H., Tang, C., Sun, J., Yang, J., Huang, C., Phillips, P., and Zhang, Y.D. (2018). Multiple sclerosis identification by 14-layer convolutional neural network with batch normalization, dropout, and stochastic pooling. Front. Neurosci. 12: 818, https://doi.org/10.1016/j.nicl.2017.10.003.Search in Google Scholar PubMed PubMed Central

Yang, J., Xie, M., Hu, C., Alwalid, O., Xu, Y., Liu, J., Jin, T., Li, C., Tu, D., Liu, X, et al.. (2021). Deep learning for detecting cerebral aneurysms with CT angiography. Radiology 298: 155–163.10.1148/radiol.2020192154Search in Google Scholar PubMed

Yang, X., Xia, D., Kin, T., and Igarashi, T. (2020). IntrA: 3D intracranial aneurysm dataset for deep learning. Presented at 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).10.1109/CVPR42600.2020.00273Search in Google Scholar

Yao, A.D., Cheng, D.L., Pan, I., and Kitamura, F. (2020). Deep learning in neuroradiology: a systematic review of current algorithms and approaches for the new wave of imaging technology. Radiol. Artif. Intell. 2: e190026, https://doi.org/10.3389/fnins.2018.00818.Search in Google Scholar PubMed PubMed Central

Yoo, Y., Tang, L.Y.W., Li, D.K.B., Metz, L., Kolind, S., Traboulsee, A.L., and Tam, R.C (2017). Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome. Comput. Methods Biomech. Biomed. Eng.: Imag. Visual. 7: 250–259, https://doi.org/10.1148/radiol.2020192154.Search in Google Scholar PubMed

You, J., Tsang, A.C.O., Yu, P.L.H., Tsui, E.L.H., Woo, P.P.S., Lui, C.S.M., and Leung, G.K.K. (2020). Automated hierarchy evaluation system of large vessel occlusion in acute ischemia stroke. Front. Neuroinf. 14: 13, https://doi.org/10.1109/cvpr42600.2020.00273.Search in Google Scholar

You, J., Yu, P.L.H., Tsang, A.C.O., Tsui, E.L.H., Pauline, P., Woo, S., and Leung, G.K.K. (2019). Automated computer evaluation of acute ischemic stroke and large vessel occlusion.Search in Google Scholar

Yu, Y., Guo, D., Lou, M., Liebeskind, D., and Scalzo, F. (2018). Prediction of hemorrhagic transformation severity in acute stroke from source perfusion MRI. IEEE Trans. Biomed. Eng. 65: 2058–2065, https://doi.org/10.1080/21681163.2017.1356750.Search in Google Scholar

Zhang, Y. and Yu, H. (2018). Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans. Med. Imag. 37: 1370–1381, https://doi.org/10.3389/fninf.2020.00013.Search in Google Scholar PubMed PubMed Central

Received: 2021-07-30
Accepted: 2021-08-18
Published Online: 2021-09-10
Published in Print: 2022-06-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 28.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/revneuro-2021-0101/html
Scroll to top button