Use of Artificial Intelligence in Clinical Neurology

Abstract

Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities. These advances, however, reflect only the tip of the iceberg for the ways in which artificial intelligence may transform neurologic care in the future.

Keywords

artificial intelligence - machine learning - neurology

Publication History

Article published online:
16 May 2022

• References

• 1 Siri Team. Hey Siri: An On-device DNN-Powered Voice Trigger for Apple's Personal Assistant. Apple. Updated October 2017. Accessed April 11, 2021 at: https://machinelearning.apple.com/research/hey-siri
• 2 Barrett B. The Year Alexa Grew Up. Wired. Updated December 19, 2018. Accessed April 11, 2021 at: https://www.wired.com/story/amazon-alexa-2018-machine-learning/
• 3 Silver D, Huang A, Maddison CJ. et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016; 529 (7587): 484-489
• 4 Silver D, Schrittwieser J, Simonyan K. et al. Mastering the game of Go without human knowledge. Nature 2017; 550 (7676): 354-359
• 5 Campbell M, Hoane AJ, Hsu F-h. Deep Blue. Artificial Intelligence. 2002/01/01/. 2002; 134 (01) 57-83 . doi: https://doi.org/10.1016/S0004-3702(01)00129-1
• 6 Vari G. Can AI improve your job search?. It already has. Forbes. Updated July 10, 2020. Accessed April 11, 2021 at: https://www.forbes.com/sites/forbestechcouncil/2020/07/10/can-ai-improve-your-job-search-it-already-has/
• 7 Tuffley D. Love in the time of algorithms: Would you let artificial intelligence choose your partner?. The Conversation. Updated January 17, 2021. Accessed April 11, 2021 at: https://theconversation.com/love-in-the-time-of-algorithms-would-you-let-artificial-intelligence-choose-your-partner-152817
• 8 Boden MA. AI: Its Nature and Future. 1st ed.. Oxford University Press; 2016: 198
• 9 Anderson AJ. Foundations of Computer Technology. CRC Press; 1994
• 10 Hodson H. DeepMind and Google: the battle to control artificial intelligence. 1843 magazine. The Economist; 2019
• 11 Turing AM. I.—Computing machinery and intelligence. Mind 1950; LIX (236) 433-460
• 12 Krohn J, Beyleveld G, Bassens A. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence. Addison-Wesley Professional; 2019
• 13 FDA. FDA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients. Updated February 13, 2018. Accessed April 18, 2021 at: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-clinical-decision-support-software-alerting-providers-potential-stroke
• 14 FDA. Evaluation of automatic class III designation for ContaCT. Decision Summary. Accessed April 18, 2021 at: https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN170073.pdf
• 15 Morey JR, Zhang X, Yaeger KA. et al. Real-world experience with artificial intelligence-based triage in transferred large vessel occlusion stroke patients. Cerebrovasc Dis 2021; 50 (04) 450-455
• 16 De Fauw J, Ledsam JR, Romera-Paredes B. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018; 24 (09) 1342-1350
• 17 Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet 2020; 395 (10236): 1579-1586
• 18 Hillis JM, Berkowitz AL. Neurology training worldwide. Semin Neurol 2018; 38 (02) 135-144
• 19 Sokolov E, Abdoul Bachir DH, Sakadi F. et al. Tablet-based electroencephalography diagnostics for patients with epilepsy in the West African Republic of Guinea. Eur J Neurol 2020; 27 (08) 1570-1577
• 20 Williams JA, Cisse FA, Schaekermann M. et al; Guinea Epilepsy Project. Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: quality, characteristics, and practice implications. Seizure 2019; 71: 93-99
• 21 Munford M. Zebra Takes Healthcare To Next Level With $1 Image Scans. Forbes. Accessed April 18, 2021 at: https://www.forbes.com/sites/montymunford/2017/10/27/zebra-takes-healthcare-to-next-level-with-1-image-scans/
• 22 Miller S, Gilbert S, Virani V, Wicks P. Patients' utilization and perception of an artificial intelligence-based symptom assessment and advice technology in a British primary care waiting room: exploratory pilot study. JMIR Human Factors 2020; 7 (03) e19713
• 23 Gilbert S, Mehl A, Baluch A. et al. How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs. BMJ Open 2020; 10 (12) e040269
• 24 Jing J, Herlopian A, Karakis I. et al. Interrater reliability of experts in identifying interictal epileptiform discharges in electroencephalograms. JAMA Neurol 2020; 77 (01) 49-57
• 25 Jing J, Sun H, Kim JA. et al. Development of expert-level automated detection of epileptiform discharges during electroencephalogram interpretation. JAMA Neurol 2020; 77 (01) 103-108
• 26 de Boer R. Automatic Analysis of Brain Tissue and Structural Connectivity in MRI. [PhD thesis]. Rotterdam, The Netherlands: Erasmus University Rotterdam; 2011
• 27 FDA. Quantib™ Brain 1.3. Accessed April 25, 2021 at: https://www.accessdata.fda.gov/cdrh_docs/pdf17/K173939.pdf
• 28 Vernooij MW, Jasperse B, Steketee R. et al. Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: a clinical evaluation of diagnostic accuracy. Neuroimage Clin 2018; 20: 374-379
• 29 Gauriau R, Bizzo BC, Comeau D. et al. Nature Portfolio. May 31, 2021;
• 30 Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson's disease diagnosis and treatment: a systematic review. Front Neurosci 2017; 11: 555
• 31 Fraser KC, Meltzer JA, Rudzicz F. Linguistic features identify Alzheimer's disease in narrative speech. J Alzheimers Dis 2016; 49 (02) 407-422
• 32 Zulueta J, Piscitello A, Rasic M. et al. Predicting mood disturbance severity with mobile phone keystroke metadata: a BiAffect digital phenotyping study. J Med Internet Res 2018; 20 (07) e241
• 33 Tomašev N, Glorot X, Rae JW. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 2019; 572 (7767): 116-119
• 34 Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet 2018; 19 (05) 299-310
• 35 Li ZC, Bai H, Sun Q. et al. Multiregional radiomics profiling from multiparametric MRI: identifying an imaging predictor of IDH1 mutation status in glioblastoma. Cancer Med 2018; 7 (12) 5999-6009
• 36 Calabrese E, Villanueva-Meyer JE, Cha S. A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Sci Rep 2020; 10 (01) 11852
• 37 Choi Y, Nam Y, Lee YS. et al. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation. Eur J Radiol 2020; 128: 109031
• 38 Dinov ID, Heavner B, Tang M. et al. Predictive big data analytics: a study of Parkinson's disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One 2016; 11 (08) e0157077
• 39 Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest 2010; 137 (02) 263-272
• 40 Hemphill III JC, Bonovich DC, Besmertis L, Manley GT, Johnston SC. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke 2001; 32 (04) 891-897
• 41 Raj R, Luostarinen T, Pursiainen E. et al. Machine learning-based dynamic mortality prediction after traumatic brain injury. Sci Rep 2019; 9 (01) 17672
• 42 Eshaghi A, Young AL, Wijeratne PA. et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat Commun 2021; 12 (01) 2078
• 43 Young AL, Marinescu RV, Oxtoby NP. et al; Genetic FTD Initiative (GENFI), Alzheimer's Disease Neuroimaging Initiative (ADNI). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun 2018; 9 (01) 4273
• 44 Delen D, Davazdahemami B, Eryarsoy E, Tomak L, Valluru A. Using predictive analytics to identify drug-resistant epilepsy patients. Health Informatics J 2020; 26 (01) 449-460
• 45 Yao L, Cai M, Chen Y, Shen C, Shi L, Guo Y. Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning. Epilepsy Behav 2019; 96: 92-97
• 46 Armañanzas R, Alonso-Nanclares L, Defelipe-Oroquieta J. et al. Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery. PLoS One 2013; 8 (04) e62819
• 47 Memarian N, Kim S, Dewar S, Engel Jr J, Staba RJ. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput Biol Med 2015; 64: 67-78
• 48 doc.ai. Help accelerate epilepsy research. Accessed May 31, 2021 at: https://doc.ai/epilepsy
• 49 Al-Mufti F, Kim M, Dodson V. et al. Machine learning and artificial intelligence in neurocritical care: a specialty-wide disruptive transformation or a strategy for success. Curr Neurol Neurosci Rep 2019; 19 (11) 89
• 50 Habets JGV, Heijmans M, Kuijf ML, Janssen MLF, Temel Y, Kubben PL. An update on adaptive deep brain stimulation in Parkinson's disease. Mov Disord 2018; 33 (12) 1834-1843
• 51 Amengual-Gual M, Ulate-Campos A, Loddenkemper T. Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure 2019; 68: 31-37
• 52 Kang T, Zhang S, Tang Y. et al. EliIE: an open-source information extraction system for clinical trial eligibility criteria. J Am Med Inform Assoc 2017; 24 (06) 1062-1071
• 53 Jonnalagadda SR, Adupa AK, Garg RP, Corona-Cox J, Shah SJ. Text mining of the electronic health record: an information extraction approach for automated identification and subphenotyping of HFpEF patients for clinical trials. J Cardiovasc Transl Res 2017; 10 (03) 313-321
• 54 Yuan C, Ryan PB, Ta C. et al. Criteria2Query: a natural language interface to clinical databases for cohort definition. J Am Med Inform Assoc 2019; 26 (04) 294-305
• 55 Kirshner JJ, Cohn K, Dunder S. et al. An automated EHR-based tool for identification of patients (pts) with metastatic disease to facilitate clinical trial pt ascertainment. J Clin Oncol 2020; 38 (15, Suppl): 2051
• 56 OneOncology. About OneOncology: Why Community Oncology, Board Members, Leadership Team. Accessed May 31, 2021 at: https://www.oneoncology.com/about/
• 57 Fleming N. How artificial intelligence is changing drug discovery. Nature 2018; 557 (7707): S55-S57
• 58 Bakkar N, Kovalik T, Lorenzini I. et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol 2018; 135 (02) 227-247
• 59 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366 (6464): 447-453
• 60 FDA. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. Accessed June 11, 2021 at: https://www.fda.gov/media/145022/download
• 61 FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback. Accessed June 11, 2021 at: https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf
• 62 Kitamura FC, Marques O. Trustworthiness of artificial intelligence models in radiology and the role of explainability. J Am Coll Radiol 2021; 18 (08) 1160-1162
• 63 Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform 2021; 113: 103655
• 64 Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Precise4Q Consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 2020; 20 (01) 310
• 65 Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov 2019; 9 (04) e1312
• 66 Viz.ai. Viz Platform. June 11, 2021. Accessed December 20, 2021 at: https://www.viz.ai/viz-platform
• 67 Nuance. AI Marketplace for Diagnostic Imaging - AI for Radiology. Accessed June 11, 2021 at: https://www.nuance.com/healthcare/diagnostics-solutions/ai-marketplace.html
• 68 American College of Radiology. FDA Cleared AI Algorithms. Accessed June 10, 2021 at: https://models.acrdsi.org/
• 69 FDA. Step 3: Clinical Research. Updated January 04, 2018. Accessed April 25, 2021 at: https://www.fda.gov/patients/drug-development-process/step-3-clinical-research