{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:49:58Z","timestamp":1782546598829,"version":"3.54.5"},"reference-count":182,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.<\/jats:p>","DOI":"10.3390\/s23063062","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:28:33Z","timestamp":1678678113000},"page":"3062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders\u2014A Scoping Review"],"prefix":"10.3390","volume":"23","author":[{"given":"Chellammal","family":"Surianarayanan","sequence":"first","affiliation":[{"name":"Centre of Distance and Online Education, Bharathidasan University, Tiruchirappalli 620024, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John Jeyasekaran","family":"Lawrence","sequence":"additional","affiliation":[{"name":"Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5220-0408","authenticated-orcid":false,"given":"Pethuru Raj","family":"Chelliah","sequence":"additional","affiliation":[{"name":"Edge AI Division, Reliance Jio Platforms Ltd., Bangalore 560103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-0186","authenticated-orcid":false,"given":"Edmond","family":"Prakash","sequence":"additional","affiliation":[{"name":"Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7593-6661","authenticated-orcid":false,"given":"Chaminda","family":"Hewage","sequence":"additional","affiliation":[{"name":"Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Verma, S., and Tomar, P. (2021). Impact of AI Technologies on Teaching, Learning, and Research in Higher Education, IGI Global.","DOI":"10.4018\/978-1-7998-4763-2"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Meyers, R. (2009). Encyclopedia of Complexity and Systems Science, Springer.","DOI":"10.1007\/978-0-387-30440-3"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s42979-020-00394-7","article-title":"Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset","volume":"2","author":"Muhammad","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s00354-021-00128-0","article-title":"Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment","volume":"39","author":"Surianarayanan","year":"2021","journal-title":"New Gener. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jin, S., Wang, B., Xu, H., Luo, C., Wei, L., Zhao, W., Hou, X., Ma, W., Zhengqing, X., and Zheng, Z. (2020). AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. medRxiv.","DOI":"10.1101\/2020.03.19.20039354"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rana, A., Rawat, A.S., Bijalwan, A., and Bahuguna, H. (2018, January 22\u201324). Application of multi-layer (perceptron) artificial neural network in the diagnosis system: A systematic review. Proceedings of the 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, El Salvador.","DOI":"10.1109\/RICE.2018.8509069"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1361-6528\/aba70f","article-title":"Roadmap on emerging hardware and technology for machine learning","volume":"32","author":"Berggren","year":"2020","journal-title":"Nanotechnology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.eng.2019.11.012","article-title":"From brain science to artificial intelligence, Engineering","volume":"6","author":"Fan","year":"2020","journal-title":"Engineering"},{"key":"ref_9","first-page":"S1","article-title":"Neuropsychiatry in the Century of Neuroscience","volume":"59","year":"2022","journal-title":"Noro. Psikiyatr. Ars."},{"key":"ref_10","unstructured":"Kaur, K. (2021). Neurodevelopmental Disorders and Treatment, Pulsus Group. Available online: https:\/\/www.pulsus.com\/abstract\/a-study-of-neuroscience-8559.html."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"464","DOI":"10.3389\/fnhum.2016.00464","article-title":"Contribution of Neuroimaging Studies to Understanding Development of Human Cognitive Brain Functions","volume":"10","author":"Morita","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"629503","DOI":"10.3389\/fcell.2021.629503","article-title":"Brain Ultrastructure: Putting the Pieces Together","volume":"9","author":"Nahirney","year":"2021","journal-title":"Front. Cell Dev. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20140164","DOI":"10.1098\/rstb.2014.0164","article-title":"The BRAIN Initiative: Developing technology to catalyse neuroscience discovery","volume":"370","author":"Jorgenson","year":"2015","journal-title":"Philos. Trans. Soc. Lond. B Biol. Sci."},{"key":"ref_14","first-page":"S14","article-title":"Neuroimaging in the Era of Artificial Intelligence: Current Applications","volume":"39","author":"Monsour","year":"2022","journal-title":"Fed. Pract."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"K\u00f6tter, R. (2003). Neuroscience Databases, Springer.","DOI":"10.1007\/978-1-4615-1079-6"},{"key":"ref_16","first-page":"918062","article-title":"Where Artificial Intelligence and Neuroscience Meet: The Search for Grounded Architectures of Cognition","volume":"2010","year":"2010","journal-title":"Adv. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.neuron.2017.06.011","article-title":"Neuroscience-Inspired Artificial Intelligence","volume":"95","author":"Hassabis","year":"2017","journal-title":"Neuron"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.neunet.2021.09.018","article-title":"Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research","volume":"144","author":"Macpherson","year":"2021","journal-title":"Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1126\/science.aau6595","article-title":"Using neuroscience to develop artificial intelligence","volume":"363","author":"Ullman","year":"2019","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neunet.2021.10.003","article-title":"Reinforcement learning and its connections with neuroscience and psychology","volume":"145","author":"Subramanian","year":"2022","journal-title":"Neural Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.neuron.2020.01.014","article-title":"Intelligence: From Invention to Discovery","volume":"105","author":"Li","year":"2020","journal-title":"Neuron"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lungarella, M., Iida, F., Bongard, J., and Pfeifer, R. (2007). 50 Years of Artificial Intelligence, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-540-77296-5"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.3389\/fnins.2019.01346","article-title":"Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation","volume":"13","author":"Fellous","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chauhan, N.K., and Singh, K. (2018, January 28\u201329). A Review on Conventional Machine Learning vs Deep Learning. Proceedings of the 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India.","DOI":"10.1109\/GUCON.2018.8675097"},{"key":"ref_25","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_27","unstructured":"(2023, March 01). Available online: https:\/\/www.zendesk.com\/in\/blog\/machine-learning-and-deep-learning\/."},{"key":"ref_28","unstructured":"(2023, February 17). Available online: https:\/\/www.deepmind.com\/blog\/ai-and-neuroscience-a-virtuous-circle."},{"key":"ref_29","unstructured":"(2023, February 17). Available online: https:\/\/www.linkedin.com\/pulse\/shared-vision-machine-learning-neuroscience-harshit-goyal\/."},{"key":"ref_30","unstructured":"Nwadiugwu, M.C. (2023, January 24). Neural Networks, Artificial Intelligence and the Computational Brain. Available online: https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2101\/2101.08635.pdf."},{"key":"ref_31","unstructured":"Hebb, D.O. (1949). The Organization of Behavior, Wiley."},{"key":"ref_32","unstructured":"Casarella, J.M., and Alan, M. (2011, January 6). Turing, Connectionism, and Artificial Intelligence. Proceedings of the Student-Faculty Research Day, CSIS, Pace University, New York, NY, USA. Available online: http:\/\/csis.pace.edu\/~ctappert\/srd2011\/d4.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: A probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"fe2","DOI":"10.1187\/cbe.17-01-0005","article-title":"Teaching as Brain Changing: Exploring Connections between Neuroscience and Innovative Teaching","volume":"16","author":"Owens","year":"2017","journal-title":"CBE Life Sci. Educ."},{"key":"ref_35","unstructured":"Werbos, P.J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Applied Mathematics. [Ph.D. Thesis, Harvard University]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1162\/NECO_a_00949","article-title":"An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity","volume":"29","author":"Whittington","year":"2017","journal-title":"Neural Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1162\/NECO_a_00934","article-title":"STDP-Compatible approximation of backpropagation in an energy-based model","volume":"29","author":"Bengio","year":"2017","journal-title":"Neural Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"760864","DOI":"10.3389\/fnsys.2022.760864","article-title":"Neural Mechanisms of Working Memory Accuracy Revealed by Recurrent Neural Networks","volume":"16","author":"Xie","year":"2022","journal-title":"Front. Syst. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/nrn.2017.74","article-title":"Prefrontal\u2013hippocampal interactions in episodic memory","volume":"18","author":"Eichenbaum","year":"2017","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.tics.2003.10.005","article-title":"Working memory capacity and its relation to general intelligence","volume":"7","author":"Conway","year":"2003","journal-title":"Trends Cogn. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.neunet.2021.07.011","article-title":"Bio-instantiated recurrent neural networks","volume":"142","author":"Goulas","year":"2021","journal-title":"Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8262","DOI":"10.1523\/JNEUROSCI.0164-22.2022","article-title":"A Midbrain Inspired Recurrent Neural Network Model for Robust Change Detection","volume":"42","author":"Sawant","year":"2022","journal-title":"J. Neurosci."},{"key":"ref_43","unstructured":"Close Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25, Lake Tahoe, NV, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/TPAMI.2007.56","article-title":"Robust object recognition with cortex-like mechanisms","volume":"29","author":"Thomas","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"343","DOI":"10.3758\/CABN.9.4.343","article-title":"Reinforcement learning, conditioning, and the brain: Successes and challenges","volume":"9","author":"Maia","year":"2009","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref_46","unstructured":"(2023, February 12). Available online: https:\/\/www.akc.org\/expert-advice\/training\/operant-conditioning-the-science-behind-positive-reinforcement-dog-training\/."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.neunet.2019.10.011","article-title":"A complementary learning systems approach to temporal difference learning","volume":"122","author":"Black","year":"2020","journal-title":"Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1146\/annurev-neuro-062111-150512","article-title":"Neural Basis of Reinforcement Learning and Decision Making","volume":"35","author":"Seo","year":"2012","journal-title":"Annu. Rev. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/s41586-019-1924-6","article-title":"A distributional code for value in dopamine-based reinforcement learning","volume":"577","author":"Dabney","year":"2020","journal-title":"Nature"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1145\/203330.203343","article-title":"Temporal difference learning and td-gammon","volume":"38","author":"Tesauro","year":"1995","journal-title":"Commun. ACM"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.tics.2016.05.004","article-title":"What learning systems do intelligent agents need? Complementary learning systems theory updated","volume":"20","author":"Kumaran","year":"2016","journal-title":"Trends Cogn. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"ref_53","unstructured":"Blakeman, S., and Mareschal, D. (2022). Generating Explanations from Deep Reinforcement Learning Using Episodic Memory. arXiv."},{"key":"ref_54","unstructured":"Hu, H., Ye, J., Zhu, G., Ren, Z., and Zhang, C. (2021, January 18\u201324). Generalizable Episodic Memory for Deep Reinforcement Learning. Proceedings of the 38th International Conference on Machine Learning, Virtual Event. PMLR 139."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"646125","DOI":"10.3389\/fncom.2021.646125","article-title":"Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update","volume":"15","author":"Kim","year":"2021","journal-title":"Front. Comput. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"774","DOI":"10.3389\/fnins.2018.00774","article-title":"Deep Learning With Spiking Neurons: Opportunities and Challenges","volume":"12","author":"Pfeiffer","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhan, G., Song, Z., Fang, T., Zhang, Y., Le, S., Zhang, X., Wang, S., Lin, Y., Jia, J., and Zhang, L. (2021, January 22\u201324). Applications of Spiking Neural Network in Brain Computer Interface. Proceedings of the 2021 9th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Republic of Korea.","DOI":"10.1109\/BCI51272.2021.9385361"},{"key":"ref_58","unstructured":"(2023, February 17). Available online: https:\/\/www.healtheuropa.com\/the-role-of-artificial-intelligence-in-neuroscience\/116572\/."},{"key":"ref_59","unstructured":"Frye, J., Ananthanarayanan, R., and Modha, D.S. (2023, January 24). Towards Real-Time, Mouse-Scale Cortical Simulations. Available online: https:\/\/dominoweb.draco.res.ibm.com\/reports\/rj10404.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.mayocp.2011.12.008","article-title":"Brain-computer interfaces in medicine","volume":"87","author":"Shih","year":"2012","journal-title":"Mayo Clin. Proc."},{"key":"ref_61","unstructured":"(2023, February 14). Available online: https:\/\/www.linkedin.com\/pulse\/artificial-intelligence-can-make-brain-computer-more-chhabra\/?trk=public_profile_article_view."},{"key":"ref_62","first-page":"11","article-title":"The combination of brain-computer interfaces and artificial intelligence: Applications and challenges","volume":"8","author":"Zhang","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13073-019-0689-8","article-title":"Artificial intelligence in clinical and genomic diagnostics","volume":"11","author":"Dias","year":"2019","journal-title":"Genome Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/nrn1848","article-title":"The blue brain project","volume":"7","author":"Markram","year":"2006","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_65","unstructured":"Almeida, J.E., Teixeira, C., Morais, J., Oliveira, E., and Couto, L. (2023, January 24). Applications of Artificial Intelligence in Neuroscience Research: An Overview. Available online: http:\/\/www.kriativ-tech.com\/wp-content\/uploads\/2022\/06\/JoaoAlmeida_IA_Neurociencias-EN-2.pdf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1227\/NEU.0b013e318258e9ff","article-title":"Mapping the human connectome","volume":"71","author":"Toga","year":"2012","journal-title":"Neurosurgery"},{"key":"ref_67","unstructured":"Brown, C., and Hamarneh, G. (2016). Machine Learning on Human Connectome Data from MRI. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"869","DOI":"10.3389\/fneur.2019.00869","article-title":"Applications of Deep Learning to Neuro-Imaging Techniques","volume":"10","author":"Zhu","year":"2019","journal-title":"Front. Neurol."},{"key":"ref_69","first-page":"25","article-title":"The Mutual Inspirations of Machine Learning and Neuroscience","volume":"86","author":"Helmstaedter","year":"2015","journal-title":"Neuroview"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s00330-008-1159-7","article-title":"The radiologist\u2019s conundrum: Benefits and costs of increasing CT capacity and utilization","volume":"19","author":"Boland","year":"2009","journal-title":"Eur. Radiol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.acra.2015.05.007","article-title":"The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload","volume":"22","author":"McDonald","year":"2015","journal-title":"Acad. Radiol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1053\/crad.2001.0858","article-title":"Error in radiology","volume":"56","author":"Fitzgerald","year":"2001","journal-title":"Clin. Radiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5110","DOI":"10.1038\/s41598-017-05300-5","article-title":"Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities","volume":"7","author":"Ghafoorian","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., and Wells, W. (2016). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Springer. MICCAI 2016; Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46726-9"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1016\/j.acra.2018.02.018","article-title":"Deep learning in radiology","volume":"25","author":"McBee","year":"2018","journal-title":"Acad. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"022077","DOI":"10.1088\/1742-6596\/1852\/2\/022077","article-title":"MRI Image Reconstruction Based on Artificial Intelligence","volume":"1852","author":"Liang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.mri.2019.05.038","article-title":"Applications of a deep learning method for anti-aliasing and super-resolution in MRI","volume":"64","author":"Zhao","year":"2019","journal-title":"Magn. Reason. Imaging"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1007\/s11604-018-0758-8","article-title":"Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network","volume":"36","author":"Jiang","year":"2018","journal-title":"Jpn. J. Radiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11604-018-0796-2","article-title":"Improvement of image quality at CT and MRI using deep learning","volume":"37","author":"Higaki","year":"2019","journal-title":"Jpn. J. Radiol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.cpet.2021.06.005","article-title":"Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement","volume":"16","author":"Liu","year":"2021","journal-title":"PET Clin."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1002\/jmri.24687","article-title":"Image reconstruction: An overview for clinicians","volume":"41","author":"Hansen","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1002\/jmri.27078","article-title":"Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians","volume":"53","author":"Lin","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"101545","DOI":"10.1016\/j.media.2019.101545","article-title":"Adversarial learning for mono-or multi-modal registration","volume":"58","author":"Fan","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"20TR01","DOI":"10.1088\/1361-6560\/ab843e","article-title":"Deep learning in medical image registration: A review","volume":"65","author":"Fu","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1109\/TMI.2021.3139507","article-title":"Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration","volume":"41","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1177\/0146645320940827","article-title":"Use of artificial intelligence in computed tomography dose optimization","volume":"49","author":"McCollough","year":"2020","journal-title":"Ann. ICRP"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ng, C.K.C. (2022). Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review. Children, 9.","DOI":"10.3390\/children9071044"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/mp.13271","article-title":"Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique","volume":"46","author":"Fan","year":"2019","journal-title":"Med. Phys."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e13525","DOI":"10.1002\/acm2.13525","article-title":"Head and neck cancer patient positioning using synthetic CT data in MRI-only radiation therapy","volume":"23","author":"Karlsson","year":"2022","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"822","DOI":"10.21037\/atm-20-6220","article-title":"Artificial intelligence for molecular neuroimaging","volume":"9","author":"Boyle","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_94","unstructured":"(2023, February 18). Available online: https:\/\/engineering.cmu.edu\/news-events\/news\/2022\/07\/29-brain-imaging.html."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"217","DOI":"10.3174\/ajnr.A5926","article-title":"A deep learning-based approach to reduce rescan and recall rates in clinical MRI examinations","volume":"40","author":"Sreekumari","year":"2019","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_96","first-page":"1","article-title":"Interpretable classification of Alzheimer\u2019s disease pathologies with a convolutional neural network pipeline","volume":"10","author":"Tang","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1177\/0972753121990175","article-title":"A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison","volume":"28","author":"Singh","year":"2021","journal-title":"Ann. Neurosci."},{"key":"ref_98","first-page":"71","article-title":"Software Tools for the Analysis of Functional Magnetic Resonance Imaging","volume":"3","author":"Behroozi","year":"2012","journal-title":"Basic Clin. Neurosci."},{"key":"ref_99","unstructured":"(2023, February 18). Available online: https:\/\/www.bitbrain.com\/blog\/ai-eeg-data-processing."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.neuroimage.2012.01.083","article-title":"BrainVoyager\u2014Past, present, future","volume":"62","author":"Goebel","year":"2012","journal-title":"Neuroimage"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"FreeSurfer","volume":"62","author":"Fischl","year":"2012","journal-title":"NeuroImage"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"379","DOI":"10.3389\/fnins.2015.00379","article-title":"A comparison of FreeSurfer-generated data with and without manual intervention","volume":"9","author":"McCarthy","year":"2015","journal-title":"Front. Neurosci."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s00234-008-0383-9","article-title":"Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder","volume":"50","author":"Tae","year":"2008","journal-title":"Neuroradiology"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","article-title":"FastSurfer\u2014A fast and accurate deep learning based neuroimaging pipeline","volume":"219","author":"Henschel","year":"2020","journal-title":"NeuroImage"},{"key":"ref_105","unstructured":"Ghazia, M.M., and Nielsen, M. (2022). FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain. arXiv."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1002\/hbm.25011","article-title":"From a deep learning model back to the brain-Identifying regional predictors and their relation to aging","volume":"41","author":"Levakov","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_107","unstructured":"(2023, February 19). Available online: https:\/\/news.usc.edu\/204691\/ai-brain-aging-risk-of-cognitive-decline-alzheimers\/."},{"key":"ref_108","first-page":"1","article-title":"Biomarker Detection of Neurological Disorders through Spectroscopy Analysis","volume":"4","author":"Khan","year":"2018","journal-title":"Int. Dent. Med. J. Adv. Res."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Akinyelu, A.A., Zaccagna, F., Grist, J.T., Castelli, M., and Rundo, L. (2022). Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey. J. Imaging, 8.","DOI":"10.3390\/jimaging8080205"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s00138-021-01262-x","article-title":"An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature","volume":"33","author":"Jena","year":"2022","journal-title":"Mach. Vis. Appl."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","article-title":"Multi-grade brain tumor classification using deep CNN with extensive data augmentation","volume":"30","author":"Sajjad","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Abd El Kader, I., Xu, G., Shuai, Z., Saminu, S., Javaid, I., and Salim Ahmad, I. (2021). Differential deep convolutional neural network model for brain tumor classification. Brain Sci., 11.","DOI":"10.3390\/brainsci11030352"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1007\/s10278-020-00347-9","article-title":"Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification","volume":"33","author":"Mzoughi","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_114","first-page":"1295","article-title":"Capsule networks\u2014A survey","volume":"34","author":"Patrick","year":"2022","journal-title":"J. King Saud Univ. Inf. Sci."},{"key":"ref_115","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_116","unstructured":"Shoeb, A.H. (2009). Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment, Massachusetts Institute of Technology. Available online: https:\/\/dspace.mit.edu\/handle\/1721.1\/54669."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"13475","DOI":"10.1016\/j.eswa.2011.04.149","article-title":"EEG signals classification using the K-means clustering and a multilayer perceptron neural network model","volume":"38","author":"Orhan","year":"2011","journal-title":"Exp. Syst. Appl."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.bspc.2017.01.005","article-title":"Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals","volume":"34","author":"HaiderBanka","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_119","first-page":"1","article-title":"Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Transact","volume":"15","author":"Hossain","year":"2019","journal-title":"Multimed. Comput. Commun. Appl."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Hu, W., Cao, J., Lai, X., and Liu, J. (2019). Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks. J. Ambient Intell. Human. Comput.","DOI":"10.1007\/s12652-019-01220-6"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"650050","DOI":"10.3389\/fncom.2021.650050","article-title":"A Deep Learning Approach for Automatic Seizure Detection in Children with Epilepsy","volume":"15","author":"Abdelhameed","year":"2021","journal-title":"Front. Comput. Neurosci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.nicl.2017.08.017","article-title":"Identification of autism spectrum d0isorder using deep learning and the ABIDE dataset","volume":"17","author":"Heinsfeld","year":"2018","journal-title":"Neuroimage Clin."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"066046","DOI":"10.1088\/1741-2552\/ab3a0a","article-title":"Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD","volume":"16","author":"Chen","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Movaghar, A., Page, D., Brilliant, M., and Mailick, M. (2022). Advancing artificial intelligence-assisted pre-screening for fragile X syndrome. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-01896-5"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.1016\/j.csbj.2021.05.024","article-title":"Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis","volume":"19","author":"Zhang","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.neuron.2016.03.038","article-title":"Understanding dopaminergic cell death pathways in Parkinson disease","volume":"90","author":"Michel","year":"2016","journal-title":"Neuron"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1093\/brain\/awm319","article-title":"Automatic classification of MR scans in Alzheimer\u2019s disease","volume":"131","author":"Stonnington","year":"2008","journal-title":"Brain"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Korolev, S., Safiullin, A., Belyaev, M., and Dodonova, Y. (2017, January 18\u201321). Residual and plain convolutional neural networks for 3D brain MRI classification. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","article-title":"Machine learning framework for early MRI based Alzheimer\u2019s conversion prediction in MCI subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"Neuroimage"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/s00234-008-0463-x","article-title":"Support vector machine- based classification of Alzheimer\u2019s disease from whole- brain anatomical MRI","volume":"51","author":"Magnin","year":"2009","journal-title":"Neuroradiology"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1136\/practneurol-2017-001719","article-title":"How to use pen and paper tasks to aid tremor diagnosis in the clinic","volume":"17","author":"Alty","year":"2017","journal-title":"Pract. Neurol."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.bspc.2016.08.003","article-title":"Machine learning- based classification of simple drawing movements in Parkinson\u2019s disease","volume":"31","author":"Kotsavasiloglou","year":"2017","journal-title":"Biomed. Signal. Process. Control"},{"key":"ref_133","unstructured":"Orimaye, S.O., Wong, J.S.-M., and Golden, K.J. (2014). Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Association for Computational Linguistics."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/JBHI.2015.2432832","article-title":"Feature Selection Based on the SVM Weight Vector for Classification of Dementia","volume":"19","author":"Bron","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.knosys.2018.01.004","article-title":"Dual channel LSTM based multi- feature extraction in gait for diagnosis of neurodegenerative diseases","volume":"145","author":"Zhao","year":"2018","journal-title":"Knowl. Syst."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1016\/j.csbj.2020.06.006","article-title":"Machine learning approach to predict medication overuse in migraine patients","volume":"18","author":"Ferroni","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"14062","DOI":"10.1038\/s41598-020-70992-1","article-title":"Machine learning-based automated classification of headache disorders using patient-reported questionnaires","volume":"10","author":"Kwon","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_138","unstructured":"Menon, B., Pillai, A.S., Mathew, P.S., and Bartkowiak, A.M. (2022). Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, Academic Press."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1186\/s10194-022-01490-0","article-title":"Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache","volume":"23","author":"Vandenbussche","year":"2022","journal-title":"J. Headache Pain"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1177\/1941874415583116","article-title":"Intravenous Thrombolysis for Acute Ischemic Stroke within 3 hours versus between 3 and 4.5 Hours of Symptom Onset","volume":"5","author":"Cheng","year":"2015","journal-title":"Neurohospitalist"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3103\/S1060992X07020063","article-title":"Neuroevolutionary method of stroke diagnosis","volume":"16","author":"Mosalov","year":"2007","journal-title":"Opt. Mem. Neural Netw."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3844\/jcssp.2012.18.25","article-title":"Cerebrovascular Accident Attack Classification Using Multilayer Feed Forward Artificial Neural Network with Back Propagation Error","volume":"8","author":"Olabode","year":"2012","journal-title":"J. Comput. Sci."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"277","DOI":"10.5853\/jos.2017.02054","article-title":"Deep into the Brain: Artificial Intelligence in Stroke Imaging","volume":"19","author":"Lee","year":"2017","journal-title":"J. Stroke"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.media.2018.08.008","article-title":"Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke","volume":"50","author":"Giacalone","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.nicl.2016.09.018","article-title":"Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs","volume":"12","author":"Chen","year":"2016","journal-title":"NeuroImage Clin."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Ni, Y., Alwell, K., Moomaw, C.J., Woo, D., Adeoye, O., Flaherty, M.L., Ferioli, S., Mackey, J., Rosa, F.D.L.R.L., and Martini, S. (2018). Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192586"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"3488","DOI":"10.1161\/STROKEAHA.115.011139","article-title":"Rates and determinants of 5-year outcomes after atrial fibrillation-related stroke: A population study","volume":"46","author":"Hayden","year":"2015","journal-title":"Stroke"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.21037\/qims-20-886","article-title":"Common infectious diseases of the central nervous system-clinical features and imaging characteristics","volume":"10","author":"Li","year":"2020","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"S263","DOI":"10.1093\/infdis\/jiz251","article-title":"Invasive Meningococcal Disease in Africa\u2019s Meningitis Belt: More Than Just Meningitis?","volume":"220","author":"Reese","year":"2019","journal-title":"J. Infect. Dis."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1007\/s10096-020-03986-6","article-title":"Impact of cerebrospinal fluid syndromic testing in the management of children with suspected central nervous system infection","volume":"39","author":"Posnakoglou","year":"2020","journal-title":"Eur. J. Clin. Microbiol. Infect. Dis."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Mentis, A.A., Garcia, I., Jim\u00e9nez, J., Paparoupa, M., Xirogianni, A., Papandreou, A., and Tzanakaki, G. (2021). Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study. Diagnostics, 11.","DOI":"10.3390\/diagnostics11040602"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.ifacol.2022.06.009","article-title":"Using Artificial Intelligence in Diagnostics of Meningitis","volume":"55","author":"Tabak","year":"2022","journal-title":"IFAC-Pap. OnLine"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Jash, S., and Sharma, S. (2022). Pathogenic Infections during Pregnancy and the Consequences for Fetal Brain Development. Pathogens, 11.","DOI":"10.3390\/pathogens11020193"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"913703","DOI":"10.3389\/fimmu.2022.913703","article-title":"Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China","volume":"13","author":"Xiang","year":"2022","journal-title":"Front. Immunol."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"947974","DOI":"10.3389\/fneur.2022.947974","article-title":"Deep learning-based relapse prediction of neuromyelitis optica spectrum disorder with anti-aquaporin-4 antibody","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ijsu.2010.11.005","article-title":"Brain abscess: An overview","volume":"9","author":"Muzumdar","year":"2011","journal-title":"Int. J. Surg."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"748144","DOI":"10.3389\/fmed.2021.748144","article-title":"Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features","volume":"8","author":"Bo","year":"2021","journal-title":"Front. Med."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/s12028-019-00835-z","article-title":"Global survey of outcomes of neurocritical care patients: Analysis of the PRINCE study part 2","volume":"32","author":"Suarez","year":"2020","journal-title":"Neurocrit. Care"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1056\/NEJMra0804573","article-title":"Nosocomial bacterial meningitis","volume":"362","author":"Drake","year":"2010","journal-title":"N. Engl. J. Med."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jcrc.2018.01.022","article-title":"Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach","volume":"45","author":"Savin","year":"2018","journal-title":"J. Crit. Care."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"554633","DOI":"10.3389\/fneur.2020.554633","article-title":"Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?","volume":"11","author":"Chaudhry","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_162","first-page":"169","article-title":"Cranial ultrasound-based prediction of post hemorrhagic hydrocephalus outcome in premature neonates with intraventricular hemorrhage","volume":"2017","author":"Tabrizi","year":"2017","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1097\/WCO.0000000000000761","article-title":"Applications of artificial intelligence in neuro-oncology","volume":"32","author":"Aneja","year":"2019","journal-title":"Curr. Opin. Neurol."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1148\/radiol.2018181928","article-title":"Emerging Applications of Artificial Intelligence in Neuro-Oncology","volume":"290","author":"Rudie","year":"2019","journal-title":"Radiology"},{"key":"ref_165","first-page":"145","article-title":"Seven challenges for neuroscience","volume":"28","author":"Markram","year":"2013","journal-title":"Funct. Neurol."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3389\/fncom.2020.00039","article-title":"Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence","volume":"14","author":"Chance","year":"2020","journal-title":"Front. Comput. Neurosci."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_168","unstructured":"Graham, J. (2023, January 24). Artificial Intelligence, Machine Learning, and the FDA. Available online: https:\/\/www.forbes.com\/sites\/theapothecary\/2016\/08\/19\/artificial-intelligence-machine-learning-and-the-fda\/#4aca26121aa1."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"ref_170","unstructured":"Ayyali, B., Knott, D., and Kuiken, S.V. (2023, January 24). The Big-Data Revolution in US Health Care: Accelerating Value and Innovation. Available online: http:\/\/www.mckinsey.com\/industries\/healthcare-systems-and-services\/our-insights\/the-big-data-revolution-in-us-health-care."},{"key":"ref_171","first-page":"77","article-title":"Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges","volume":"11","author":"Ienca","year":"2020","journal-title":"Neuroscience"},{"key":"ref_172","first-page":"692","article-title":"Evaluation of Different Brain Imaging Technologies","volume":"Volume 638","author":"Tong","year":"2021","journal-title":"Advances in Social Science, Education and Humanities Research, Proceedings of the 2021 International Conference on Public Art and Human Development (ICPAHD 2021), Kunming, China, 24\u201326 December 2021"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Warbrick, T. (2022). Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?. Sensors, 22.","DOI":"10.3390\/s22062262"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3389\/fphy.2017.00042","article-title":"Safety of simultaneous scalp or intracranial EEG during MRI: A review","volume":"5","author":"Hawsawi","year":"2017","journal-title":"Front. Phys."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/TRPMS.2018.2886525","article-title":"Simultaneous PET-MR-EEG: Technology, Challenges and Application in Clinical Neuroscience","volume":"3","author":"Neuner","year":"2019","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"7377","DOI":"10.1088\/0031-9155\/61\/20\/7377","article-title":"EVolution: An edge-based variational method for non-rigid multi-modal image registration","volume":"61","author":"Zachiu","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.neuroimage.2018.04.044","article-title":"Challenges in pediatric neuroimaging","volume":"185","author":"Barkovich","year":"2019","journal-title":"Neuroimage"},{"key":"ref_178","unstructured":"(2023, January 24). Available online: https:\/\/www.himss.org\/resources\/interoperability-healthcare."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1002\/hbm.25120","article-title":"Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed","volume":"43","author":"White","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.neuroimage.2018.08.042","article-title":"Reproducible evaluation of classification methods in Alzheimer\u2019s disease: Framework and application to MRI and PET data","volume":"183","author":"Burgos","year":"2018","journal-title":"Neuroimage"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"24","DOI":"10.37549\/AR2261","article-title":"Avoiding diagnostic pitfalls in neuroimaging","volume":"45","author":"Johnson","year":"2016","journal-title":"Appl. Radiol."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1055\/s-0028-1083694","article-title":"Neuroimaging practice issues for the neurologist","volume":"28","author":"Preston","year":"2008","journal-title":"Semin. Neurol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3062\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:53:28Z","timestamp":1760122408000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,13]]},"references-count":182,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063062"],"URL":"https:\/\/doi.org\/10.3390\/s23063062","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,13]]}}}