Login Register Cart 0
Generic placeholder image

Current Indian Science

Editor-in-Chief

ISSN (Print): 2210-299X
ISSN (Online): 2210-3007

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Review Article

Decoding Artificial Intelligence in Neuroscience: Applications Beyond Diagnosis

Author(s): Sagar Salave, Dhwani Rana, Derajram Benival and Aakanchha Jain*

Volume 1, 2023

Published on: 03 May, 2023

Article ID: e230223213949 Pages: 20

DOI: 10.2174/2210299X01666230223104508

open_access

Open Access Journals Promotions 2
Abstract

Background: Artificial Intelligence has witnessed exponential expansion in health care applications. The article pronounces the dynamic excellence AI is achieving in the healthcare discipline of neuroscience.

Objective: The paper highlights basic concepts of AI and acmes the interdisciplinary collaboration of Computational neuroscience, Cognitive science, and AI. Also, the article draws out important findings related to AI in neuroscience amongst its diverse application in the various disciplines. An ephemeral overview of applications of AI-based constructs in neurological disorders namely Neuroinflammation, Schizophrenia, Parkinson’s disease, Epilepsy, Autism spectrum disorder, Alzheimer’s disease, Brain tumor and Anesthesiology has been demonstrated in the present work.

Methods: The method includes the collection of data from different search engines like google Scholar, PubMed, ScienceDirect, SciFinder, etc. to get coverage of relevant literature for accumulating appropriate information regarding AI, neuroscience, and their linkages.

Results: These considerations are made to expand the existing literature on the progressing role of AI in the management of neurological disorders.

Conclusion: The exponential expansion in the development of AI-based systems might aid in addressing the prevailing limitations in the domain of neurological disorders and neuroscience.

Keywords: Artificial intelligence, AI in diagnosis, Machine learning, Neurological disorder, Neurology, Neuroscience.

[1]
Agrawal, P. Artificial intelligence in drug discovery and development. J. Pharmacovigil., 2018, 6(2), e173.
[http://dx.doi.org/10.4172/2329-6887.1000e173]
[2]
Singh, P.; Bardhan, A.; Han, F.; Samui, P.; Zhang, W. A critical review of conventional and soft computing methods for slope stability analysis. Model. Earth Syst. Environ., 2023, 9, 1-17.
[http://dx.doi.org/10.1007/s40808-022-01489-1]
[3]
Vatansever, S.; Schlessinger, A.; Wacker, D.; Kaniskan, Ümit; Jin, J.; Zhou, M.-M.; Zhang, B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med. Res. Rev., 2020, 41(3), 1427-1473.
[http://dx.doi.org/10.1002/med.21764]
[4]
Chuang, K.V.; Gunsalus, L.M.; Keiser, M.J. Learning molecular representations for medicinal chemistry. J. Med. Chem., 2020, 63(16), 8705-8722.
[http://dx.doi.org/10.1021/acs.jmedchem.0c00385] [PMID: 32366098]
[5]
Zhang, L.; Tan, J.; Han, D.; Zhu, H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today, 2017, 22(11), 1680-1685.
[http://dx.doi.org/10.1016/j.drudis.2017.08.010] [PMID: 28881183]
[6]
McKinney, S.M.; Sieniek, M.; Godbole, V.; Godwin, J.; Antropova, N.; Ashrafian, H.; Back, T.; Chesus, M.; Corrado, G.S.; Darzi, A.; Etemadi, M.; Garcia-Vicente, F.; Gilbert, F.J.; Halling-Brown, M.; Hassabis, D.; Jansen, S.; Karthikesalingam, A.; Kelly, C.J.; King, D.; Ledsam, J.R.; Melnick, D.; Mostofi, H.; Peng, L.; Reicher, J.J.; Romera-Paredes, B.; Sidebottom, R.; Suleyman, M.; Tse, D.; Young, K.C.; De Fauw, J.; Shetty, S. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788), 89-94.
[http://dx.doi.org/10.1038/s41586-019-1799-6] [PMID: 31894144]
[7]
Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments - Bloomberg. Available from: https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments (Accessed on: July 13, 2022).
[8]
IBM and Pfizer to accelerate immuno-oncology research with watson for drug discovery. 2016. Available from: https://newsroom.ibm.com/2016-12-01-IBM-and-Pfizer-to-Accelerate-Immuno-oncology-Research-with-Watson-for-Drug-Discovery (Accessed on: July 13, 2022).
[9]
Are autonomous robots your next surgeons? Available from: https://edition.cnn.com/2016/05/12/health/robot-surgeon-bowel-operation/index.html (Accessed on: July 13, 2022).
[10]
Zhu, H. Big data and artifical intelligence modeling for drug discovery. Ann. Rev. Pharmacol. Toxicol., 2020, 60, 573-589.
[http://dx.doi.org/10.1146/annurev-pharmtox-010919]
[11]
Zhang, W.; Gu, X.; Tang, L.; Yin, Y.; Liu, D.; Zhang, Y. Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge. Gondwana Res., 2022, 109, 1-17.
[http://dx.doi.org/10.1016/j.gr.2022.03.015]
[12]
AI. Deep learning, and neural networks explained. Available from: https://www.innoarchitech.com/blog/artificial-intelligence-deep-learning-neural-networks-explained.
[13]
Phoon, K. K.; Zhang, W. Future of machine learning in geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2022.
[http://dx.doi.org/10.1080/17499518.2022.2087884]
[14]
Difference between AI-ML-DL. Available from: https://www.kaggle.com/discussion/196785 (Accessed on: July 13, 2022).
[15]
AI vs. ML vs. DL: What’s the difference. Available from: https://serokell.io/blog/ai-ml-dl-difference (Accessed on: July 13, 2022).
[16]
Fascinating relationship between ai and neuroscience, towards data science. Available from: https: //towardsdatascience.com/the-fascinating-relationship-between-ai-and-neuroscience-89189218bb05 (Accessed on: July 13, 2022).
[17]
Artificial Neural Networks - Intellipaat Blog. Available from: https://intellipaat.com/blog/tutorial/artificial-intelligence-tutorial/artificial-neural-networks/ (Accessed on: July 13, 2022).
[18]
AI, ML, NN and DL: a visual explanation. Available from: http://themainstreamseer.blogspot.com/2019/07/ai-ml-nn-and-dl-visual-explanation.html (Accessed on: July 13, 2022).
[19]
Cousins of artificial intelligence. Available from: https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55 (Accessed on: July 13, 2022).
[20]
AI vs. Machine Learning vs. Deep Learning. Available from: https://blog.dataiku.com/ai-vs.-machine-learning-vs.-deep-learning (Accessed on: July 13, 2022).
[21]
Institute, I.C.T. AI, Machine Learning and neural networks explained Available from: https:// ictinstitute.nl/ai-machine-learning-and-neural-networks-explained/ (Accessed on: July 13, 2022).
[22]
3 types of neural networks that AI uses, Artificial Intelligence. Available from: https:// www.allerin.com/blog/3-types-of-neural-networks-that-ai-uses (Accessed on: July 13, 2022).
[23]
Hassabis, D.; Kumaran, D.; Summerfield, C.; Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron, 2017, 95(2), 245-258.
[http://dx.doi.org/10.1016/j.neuron.2017.06.011] [PMID: 28728020]
[24]
Kriegeskorte, N.; Douglas, P.K. Cognitive computational neuroscience. Nat. Neurosci., 2018, 21(9), 1148-1160.
[http://dx.doi.org/10.1038/s41593-018-0210-5] [PMID: 30127428]
[25]
Barchet, T.M.; Amiji, M.M. Challenges and opportunities in CNS delivery of therapeutics for neurodegenerative diseases. Expert Opin. Drug Deliv., 2009, 6(3), 211-225.
[http://dx.doi.org/10.1517/17425240902758188] [PMID: 19290842]
[26]
Zhang, X.; Ma, Z.; Zheng, H.; Li, T.; Chen, K.; Wang, X.; Liu, C.; Xu, L.; Wu, X.; Lin, D.; Lin, H. The combination of brain-computer interfaces and artificial intelligence: applications and challenges. Ann. Transl. Med., 2020, 8(11), 712-712.
[http://dx.doi.org/10.21037/atm.2019.11.109] [PMID: 32617332]
[27]
Krucoff, M. O.; Rahimpour, S.; Slutzky, M. W.; Edgerton, V. R.; Turner, D. A. Enhancing nervous system recovery through neurobiologics, neural interface training, and neurorehabilitation. Fronti. Neurosci., 2016, 10, 584.
[http://dx.doi.org/10.3389/fnins.2016.00584]
[28]
Friedenberg, D.A.; Schwemmer, M.A.; Landgraf, A.J.; Annetta, N.V.; Bockbrader, M.A.; Bouton, C.E.; Zhang, M.; Rezai, A.R.; Mysiw, W.J.; Bresler, H.S.; Sharma, G. Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human. Sci. Rep., 2017, 7(1), 8386.
[http://dx.doi.org/10.1038/s41598-017-08120-9] [PMID: 28827605]
[29]
Niketeghad, S.; Pouratian, N. Brain machine interfaces for vision restoration: The current state of cortical visual prosthetics. Neurotherapeutics, 2019, 16(1), 134-143.
[http://dx.doi.org/10.1007/s13311-018-0660-1] [PMID: 30194614]
[30]
Sebastián-Romagosa, M.; Cho, W.; Ortner, R.; Murovec, N.; Von Oertzen, T.; Kamada, K.; Allison, B.Z.; Guger, C. Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients—a feasibility study. Front. Neurosci., 2020, 14, 591435.
[http://dx.doi.org/10.3389/fnins.2020.591435] [PMID: 33192277]
[31]
Brumberg, J. S.; Guenther, F. H. Development of speech prostheses: Current status and recent advances. Rev. Med. Der., 2010, 667-679.
[http://dx.doi.org/10.1586/erd.10.34]
[32]
Brumberg, J.S.; Nieto-Castanon, A.; Kennedy, P.R.; Guenther, F.H. Brain–computer interfaces for speech communication. Speech Commun., 2010, 52(4), 367-379.
[http://dx.doi.org/10.1016/j.specom.2010.01.001] [PMID: 20204164]
[33]
Schreuder, M.; Blankertz, B.; Tangermann, M. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS One, 2010, 5(4), e9813.
[http://dx.doi.org/10.1371/journal.pone.0009813] [PMID: 20368976]
[34]
Ferracuti, F.; Freddi, A.; Iarlori, S.; Longhi, S.; Peretti, P. Auditory paradigm for a P300 BCI system using spatial hearing.2013 IEEE/RSJ International Conference on Intelligent Robots and Systems; Tokyo, Japan, 2013, pp. 871-876.
[http://dx.doi.org/10.1109/IROS.2013.6696453]
[35]
Yao, L.; Sheng, X.; Zhang, D.; Jiang, N.; Farina, D.; Zhu, X. A BCI system based on somatosensory attentional orientation. IEEE Trans. Neural Syst. Rehabil. Eng., 2017, 25(1), 81-90.
[http://dx.doi.org/10.1109/TNSRE.2016.2572226] [PMID: 27244745]
[36]
Krol, S. Challenges in drug delivery to the brain: Nature is against us. J. Control. Release, 2012, 164(2), 145-155.
[http://dx.doi.org/10.1016/j.jconrel.2012.04.044] [PMID: 22609350]
[37]
Wolburg, H.; Lippoldt, A. Tight junctions of the blood–brain barrier. Vascul. Pharmacol., 2002, 38(6), 323-337.
[http://dx.doi.org/10.1016/S1537-1891(02)00200-8] [PMID: 12529927]
[38]
Wong, A.D.; Ye, M.; Levy, A.F.; Rothstein, J.D.; Bergles, D.E.; Searson, P.C. The blood-brain barrier: an engineering perspective. Front. Neuroeng., 2013, 6, 7.
[http://dx.doi.org/10.3389/fneng.2013.00007] [PMID: 24009582]
[39]
Dotiwala, A.K.; McCausland, C.; Samra, N.S. Anatomy, Head and Neck, Blood Brain Barrier; StatPearls Publishing, 2020.
[40]
Fenstermacher, J.D. Pharmacology of the blood-brain barrier. In: Implications of the Blood-Brain Barrier and Its Manipulation; Springer US, 1989; pp. 137-155.
[http://dx.doi.org/10.1007/978-1-4613-0701-3_6]
[41]
Daneman, R.; Prat, A. The blood-brain barrier. Cold Spring Harb. Perspect. Biol., 2015, 7(1), a020412.
[http://dx.doi.org/10.1101/cshperspect.a020412] [PMID: 25561720]
[42]
Shen, J.; Cheng, F.; Xu, Y.; Li, W.; Tang, Y. Estimation of ADME properties with substructure pattern recognition. J. Chem. Inf. Model., 2010, 50(6), 1034-1041.
[http://dx.doi.org/10.1021/ci100104j] [PMID: 20578727]
[43]
Andres, C.; Hutter, M.C. CNS Permeability of drugs predicted by a decision tree. QSAR Comb. Sci., 2006, 25(4), 305-309.
[http://dx.doi.org/10.1002/qsar.200510200]
[44]
Zhang, L.; Zhu, H.; Oprea, T.I.; Golbraikh, A.; Tropsha, A. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm. Res., 2008, 25(8), 1902-1914.
[http://dx.doi.org/10.1007/s11095-008-9609-0] [PMID: 18553217]
[45]
Mente, S.R.; Lombardo, F. A recursive-partitioning model for blood–brain barrier permeation. J. Comput. Aided Mol. Des., 2005, 19(7), 465-481.
[http://dx.doi.org/10.1007/s10822-005-9001-7] [PMID: 16331406]
[46]
Obrezanova, O.; Csányi, G.; Gola, J.M.R.; Segall, M.D. Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model., 2007, 47(5), 1847-1857.
[http://dx.doi.org/10.1021/ci7000633] [PMID: 17602549]
[47]
Vilar, S.; Chakrabarti, M.; Costanzi, S. Prediction of passive blood–brain partitioning: Straightforward and effective classification models based on in silico derived physicochemical descriptors. J. Mol. Graph. Model., 2010, 28(8), 899-903.
[http://dx.doi.org/10.1016/j.jmgm.2010.03.010] [PMID: 20427217]
[48]
Garg, P.; Verma, J. In silico prediction of blood brain barrier permeability: an artificial neural network model. J. Chemi. Inform. Model., 2006, 46(1), 289-297.
[http://dx.doi.org/10.1021/ci050303i]
[49]
Wang, Z.; Yang, H.; Wu, Z.; Wang, T.; Li, W.; Tang, Y.; Liu, G. In silico prediction of blood-brain barrier permeability of compounds by machine learning and resampling methods. ChemMedChem, 2018, 13(20), 2189-2201.
[http://dx.doi.org/10.1002/cmdc.201800533] [PMID: 30110511]
[50]
Gao, Z.; Chen, Y.; Cai, X.; Xu, R.; Sahinalp, C. Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications. Bioinformatics, 2016, 33(6), btw713.
[http://dx.doi.org/10.1093/bioinformatics/btw713] [PMID: 27993785]
[51]
Patching, S. G. Glucose transporters at the blood-brain barrier: function, regulation and gateways for drug delivery. Molecular Neurobiology, 2017, 1046-1077.
[http://dx.doi.org/10.1007/s12035-015-9672-6]
[52]
Löscher, W.; Potschka, H. Blood-brain barrier active efflux transporters: ATP-binding cassette gene family. NeuroRx, 2005, 2(1), 86-98.
[http://dx.doi.org/10.1602/neurorx.2.1.86] [PMID: 15717060]
[53]
Yuan, Y.; Zheng, F.; Zhan, C.G. Improved prediction of blood–brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints. AAPS J., 2018, 20(3), 54.
[http://dx.doi.org/10.1208/s12248-018-0215-8] [PMID: 29564576]
[54]
Frank, C.L.; Brown, J.P.; Wallace, K.; Mundy, W.R.; Shafer, T.J. From the cover: developmental neurotoxicants disrupt activity in cortical networks on microelectrode arrays: Results of screening 86 compounds during neural network formation. Toxicol. Sci., 2017, 160(1), 121-135.
[http://dx.doi.org/10.1093/toxsci/kfx169] [PMID: 28973552]
[55]
Chan, H.P.; Doi, K.; Galhotra, S.; Vyborny, C.J.; MacMahon, H.; Jokich, P.M. Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Med. Phys., 1987, 14(4), 538-548.
[http://dx.doi.org/10.1118/1.596065] [PMID: 3626993]
[56]
Giger, M.L.; Doi, K.; MacMahon, H. Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med. Phys., 1988, 15(2), 158-166.
[http://dx.doi.org/10.1118/1.596247] [PMID: 3386584]
[57]
Arimura, H.; Magome, T.; Yamashita, Y.; Yamamoto, D. Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms, 2009, 2, 925-952.
[http://dx.doi.org/10.3390/a2030925]
[58]
Chilamkurthy, S.; Ghosh, R.; Tanamala, S.; Biviji, M.; Campeau, N.G.; Venugopal, V.K.; Mahajan, V.; Rao, P.; Warier, P. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet, 2018, 392(10162), 2388-2396.
[http://dx.doi.org/10.1016/S0140-6736(18)31645-3] [PMID: 30318264]
[59]
Suzuki, K. Pixel-based machine learning in medical imaging. Int. J. Biomed. Imaging, 2012, 2012, 1-18.
[http://dx.doi.org/10.1155/2012/792079] [PMID: 22481907]
[60]
Raghavendra, U.; Acharya, U. R.; Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Euro. Neurol., 2020, 41-64.
[http://dx.doi.org/10.1159/000504292]
[61]
Seo, Y.; Mari, C.; Hasegawa, B. H. Technological development and advances in single-photon emission computed tomography/computed tomography. Semi. Nucl. Med., 2008, 38(3), 177-198.
[http://dx.doi.org/10.1053/j.semnuclmed.2008.01.001]
[62]
Hemond, C.C.; Bakshi, R. Magnetic resonance imaging in multiple sclerosis. Cold Spring Harb. Perspect. Med., 2018, 8(5), a028969.
[http://dx.doi.org/10.1101/cshperspect.a028969] [PMID: 29358319]
[63]
Jensen, A.; la Cour-Harbo, A. Introduction. In: Ripples in Mathematics; Springer Berlin Heidelberg, 2001; pp. 1-5.
[http://dx.doi.org/10.1007/978-3-642-56702-5_1]
[64]
Curvelets: A surprisingly effective nonadaptive representation for objects with edges. Available from: https:// statistics.stanford.edu/ research/curvelets-surprisingly-effective-nonadaptive-representation-objects-edges (Accessed on: July 12, 2022).
[65]
Malladi, R.; Sethian, J.A.; Vemuri, B.C. Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17(2), 158-175.
[http://dx.doi.org/10.1109/34.368173]
[66]
Yin, P.Y. Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization. Sig. Proc., 2002, 82(7), 993-1006.
[http://dx.doi.org/10.1016/S0165-1684(02)00203-7]
[67]
Nixon, M.; Aguado, A.S. Feature Extraction & Image Processing for Computer Vision; Elsevier Ltd, 2012.
[http://dx.doi.org/10.1016/C2011-0-06935-1]
[68]
Khotanzad, A.; Hong, Y.H. Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12(5), 489-497.
[http://dx.doi.org/10.1109/34.55109]
[69]
Ming-Kuei Hu, Visual pattern recognition by moment invariants. IEEE Trans. Inf. Theory, 1962, 8(2), 179-187.
[http://dx.doi.org/10.1109/TIT.1962.1057692]
[70]
Hyvärinen, A.; Oja, E. Independent component analysis: Algorithms and applications. Neutral Netw., 2000, 13(4-5), 411-430.
[http://dx.doi.org/10.1016/S0893-6080(00)00026-5]
[71]
SchölkopfSch, B.; Smola Klaus-Robert, A.M. Communicated by peter dayan nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10, 1299-1319.
[72]
ALM SIGMOO. Dash, M.; Liu, H. Workshop on research issues in data mining & knowledge discovery. Handling large unsupervised data via dimensionality reduction. 1999.
[73]
Yuan, Y.; Van Allen, E.M.; Omberg, L.; Wagle, N.; Amin-Mansour, A.; Sokolov, A.; Byers, L.A.; Xu, Y.; Hess, K.R.; Diao, L.; Han, L.; Huang, X.; Lawrence, M.S.; Weinstein, J.N.; Stuart, J.M.; Mills, G.B.; Garraway, L.A.; Margolin, A.A.; Getz, G.; Liang, H. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol., 2014, 32(7), 644-652.
[http://dx.doi.org/10.1038/nbt.2940] [PMID: 24952901]
[74]
Shi, Y.; Eberhart, R. Modified particle swarm optimizer. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, 1998, , pp. 69-73.
[http://dx.doi.org/10.1109/ICEC.1998.699146]
[75]
Specht, D.F. Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification. IEEE Trans. Neural Netw., 1990, 1(1), 111-121.
[http://dx.doi.org/10.1109/72.80210] [PMID: 18282828]
[76]
Dai, H.; Cao, Z. A wavelet support vector machine-based neural network metamodel for structural reliability assessment. Comput. Aided Civ. Infrastruct. Eng., 2017, 32(4), 344-357.
[http://dx.doi.org/10.1111/mice.12257]
[77]
Olanow, C.W.; Watts, R.L.; Koller, W.C. An algorithm (decision tree) for the management of Parkinson’s disease (2001): Treatment. Neurology, 2001, 56(11)(Suppl. 5), S1-S88.
[http://dx.doi.org/10.1212/WNL.56.suppl_5.S1] [PMID: 11402154]
[78]
Bablani, A.; Edla, D.R.; Dodia, S. Classification of EEG data using K-nearest neighbor approach for concealed information test. In: Procedia Computer Science; Elsevier B.V., 2018; pp. 242-249.
[http://dx.doi.org/10.1016/j.procs.2018.10.392]
[79]
Mandelkow, H.; De Zwart, J. A.; Duyn, J. H. Linear discriminant analysis achieves high classification accuracy for the BOLD FMRI response to naturalistic movie stimuli. Front. Hum. Neurosci., 2016, 10(MAR2016), 1-12.
[http://dx.doi.org/10.3389/fnhum.2016.00128]
[80]
Sarica, A.; Cerasa, A.; Quattrone, A. Random forest algorithm for the classification of neuroim. data in Alzheim. disease: A systematic review. Front. Aging Neurosci., 2017, 9, 329.
[http://dx.doi.org/10.3389/fnagi.2017.00329]
[81]
Li, R.; Perneczky, R.; Yakushev, I.; Förster, S.; Kurz, A.; Drzezga, A.; Kramer, S. Gaussian mixture models and model selection for [18F] fluorodeoxyglucose positron emission tomography classification in alzheimer’s disease. PLoS One, 2015, 10(4), e0122731.
[http://dx.doi.org/10.1371/journal.pone.0122731] [PMID: 25919662]
[82]
Przedborski, S.; Vila, M.; Jackson-Lewis, V. Series introduction: Neurodegeneration: What is it and where are we? J. Clin. Invest., 2003, 111(1), 3-10.
[http://dx.doi.org/10.1172/JCI200317522] [PMID: 12511579]
[83]
Srejovic, I.; Selakovic, D.; Jovicic, N.; Jakovljević, V.; Lukic, M. L.; Rosic, G. Galectin-3: Roles in neurodevelopment, neuroinflammation, and behavior. Biomolecules, 2020, 10(5), 798.
[http://dx.doi.org/10.3390/biom10050798]
[84]
Kwon, H. S.; Koh, S. H. Neuroinflammation in neurodegenerative disorders: The roles of microglia and astrocytes. Transl. Neurodegeneration, 2020, 1-12.
[http://dx.doi.org/10.1186/s40035-020-00221-2]
[85]
Tan, Y.; Zheng, Y.; Xu, D.; Sun, Z.; Yang, H.; Yin, Q. Galectin-3: a key player in microglia-mediated neuroinflammation and Alzheimer’s disease. Cell Biosci., 2021, 11(1), 78.
[http://dx.doi.org/10.1186/s13578-021-00592-7] [PMID: 33906678]
[86]
Deng, L.; Zhong, W.; Zhao, L.; He, X.; Lian, Z.; Jiang, S.; Chen, C.Y.C. Artificial intelligence-based application to explore inhibitors of neurodegenerative diseases. Front. Neurorobot., 2020, 14, 617327.
[http://dx.doi.org/10.3389/fnbot.2020.617327] [PMID: 33414713]
[87]
Symptoms, S. Patterns and statistics and patterns Available from: https://www.mentalhelp.net/schizophrenia/statistics/ (Accessed on July 13, 2022).
[88]
Jablensky, A. The diagnostic concept of schizophrenia: its history, evolution, and future prospects. Dialogues Clin. Neurosci., 2010, 12(3), 271-287.
[http://dx.doi.org/10.31887/DCNS.2010.12.3/ajablensky] [PMID: 20954425]
[89]
Correll, C.U.; Schooler, N.R. Negative Symptoms in Schizophrenia: A Review and Clinical Guide for Recognition, Assessment, and Treatment. Neuropsychiatric Disease and Treatment; Dove Medical Press Ltd., 2020, pp. 519-534.
[http://dx.doi.org/10.2147/NDT.S225643]
[90]
Kambeitz, J.; Kambeitz-Ilankovic, L.; Leucht, S.; Wood, S.; Davatzikos, C.; Malchow, B.; Falkai, P.; Koutsouleris, N. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology, 2015, 40(7), 1742-1751.
[http://dx.doi.org/10.1038/npp.2015.22] [PMID: 25601228]
[91]
Schnack, H.G. Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases). Schizophr. Res., 2019, 214, 34-42.
[http://dx.doi.org/10.1016/j.schres.2017.10.023] [PMID: 29074332]
[92]
Talpalaru, A.; Bhagwat, N.; Devenyi, G.A.; Lepage, M.; Chakravarty, M.M. Identifying schizophrenia subgroups using clustering and supervised learning. Schizophr. Res., 2019, 214, 51-59.
[http://dx.doi.org/10.1016/j.schres.2019.05.044] [PMID: 31455518]
[93]
Hsu, K.C.; Wang, F.S. Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity. PLoS One, 2017, 12(6), e0179575.
[http://dx.doi.org/10.1371/journal.pone.0179575] [PMID: 28614410]
[94]
Marunnan, S.M.; Pulikkal, B.P.; Jabamalairaj, A.; Bandaru, S.; Yadav, M.; Nayarisseri, A.; Doss, V.A. Development of MLR and SVM Aided QSAR Models to Identify Common SAR of GABA Uptake Herbal Inhibitors used in the treatment of Schizophrenia. Curr. Neuropharmacol., 2017, 15(8), 1085-1092.
[http://dx.doi.org/10.2174/1567201814666161205131745] [PMID: 27919211]
[95]
Yang, Q.X.; Wang, Y.X.; Li, F.C.; Zhang, S.; Luo, Y.C.; Li, Y.; Tang, J.; Li, B.; Chen, Y.Z.; Xue, W.W.; Zhu, F. Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence-based method of enhanced reproducibility. CNS Neurosci. Ther., 2019, 25(9), 1054-1063.
[http://dx.doi.org/10.1111/cns.13196] [PMID: 31350824]
[96]
Zhao, K.; So, H.C. Drug repositioning for schizophrenia and depression/anxiety disorders: A machine learning approach leveraging expression data. IEEE J. Biomed. Health Inform., 2019, 23(3), 1304-1315.
[http://dx.doi.org/10.1109/JBHI.2018.2856535] [PMID: 30010603]
[97]
Bain, E.E.; Shafner, L.; Walling, D.P.; Othman, A.A.; Chuang-Stein, C.; Hinkle, J.; Hanina, A. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR mhealth uhealth, 2017, 5(2), e18.
[http://dx.doi.org/10.2196/mhealth.7030] [PMID: 28223265]
[98]
Challa, K.N.R.; Pagolu, V.S.; Panda, G.; Majhi, B. An improved approach for prediction of parkinson’s disease using machine learning techniques. International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings, 2017, , pp. 1446-1451.
[http://dx.doi.org/10.1109/SCOPES.2016.7955679]
[99]
Prashanth, R.; Dutta, R. S.; Mandal, P.K.; Ghosh, S. High-accuracy detection of early parkinson’s disease through multimodal features and machine learning. Int. J. Med. Inform., 2016, 90, 13-21.
[http://dx.doi.org/10.1016/j.ijmedinf.2016.03.001] [PMID: 27103193]
[100]
Kang, J.H.; Irwin, D.J.; Chen-Plotkin, A.S.; Siderowf, A.; Caspell, C.; Coffey, C.S.; Waligórska, T.; Taylor, P.; Pan, S.; Frasier, M.; Marek, K.; Kieburtz, K.; Jennings, D.; Simuni, T.; Tanner, C.M.; Singleton, A.; Toga, A.W.; Chowdhury, S.; Mollenhauer, B.; Trojanowski, J.Q.; Shaw, L.M. Association of cerebrospinal fluid β-amyloid 1-42, T-tau, P-tau181, and α-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol., 2013, 70(10), 1277-1287.
[http://dx.doi.org/10.1001/jamaneurol.2013.3861] [PMID: 23979011]
[101]
Parisi, L.; Ravi, C.N.; Manaog, M.L. Feature-driven machine learning to improve early diagnosis of Parkinson’s disease. Expert Syst. Appl., 2018, 110, 182-190.
[http://dx.doi.org/10.1016/j.eswa.2018.06.003]
[102]
Grover, S.; Bhartia, S. Predicting severity of parkinson’s disease using deep learning. In: Procedia Computer Science; Elsevier B.V., 2018; pp. 1788-1794.
[http://dx.doi.org/10.1016/j.procs.2018.05.154]
[103]
Wroge, T.J.; Özkanca, Y.; Demiroglu, C.; Si, D.; Atkins, D.C.; Ghomi, R.H. Parkinson’s disease diagnosis using machine learning and voice. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings, 2019,
[http://dx.doi.org/10.1109/SPMB.2018.8615607]
[104]
Prashanth, R.; Dutta Roy, S. Early detection of Parkinson’s disease through patient questionnaire and predictive modelling. Int. J. Med. Inform., 2018, 119, 75-87.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.09.008] [PMID: 30342689]
[105]
Karapinar, S. Z. Early diagnosis of Parkinson’s disease using machine learning algorithms. Med. Hypotheses, 2020, 138, 109603.
[http://dx.doi.org/10.1016/j.mehy.2020.109603] [PMID: 32028195]
[106]
Braga, D.; Madureira, A.M.; Coelho, L.; Ajith, R. Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Eng. Appl. Artif. Intell., 2019, 77, 148-158.
[http://dx.doi.org/10.1016/j.engappai.2018.09.018]
[107]
Shetty, S.; Rao, Y.S. SVM based machine learning approach to identify parkinson’s disease using gait analysis. Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016, 2016,
[http://dx.doi.org/10.1109/INVENTIVE.2016.7824836]
[108]
Gunduz, H. Deep learning-based Parkinson’s Disease classification using vocal feature sets. IEEE Access, 2019, 7, 115540-115551.
[http://dx.doi.org/10.1109/ACCESS.2019.2936564]
[109]
Mostafa, S.A.; Mustapha, A.; Mohammed, M.A.; Hamed, R.I.; Arunkumar, N.; Abd Ghani, M.K.; Jaber, M.M.; Khaleefah, S.H. Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease. Cogn. Syst. Res., 2019, 54, 90-99.
[http://dx.doi.org/10.1016/j.cogsys.2018.12.004]
[110]
Cai, Z.; Gu, J.; Chen, H.L. A new hybrid intelligent framework for predicting Parkinson’s Disease. IEEE Access, 2017, 5, 17188-17200.
[http://dx.doi.org/10.1109/ACCESS.2017.2741521]
[111]
Lahmiri, S.; Dawson, D.A.; Shmuel, A. Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures. Biomed. Eng. Lett., 2018, 8(1), 29-39.
[http://dx.doi.org/10.1007/s13534-017-0051-2] [PMID: 30603188]
[112]
Nilashi, M.; Ibrahim, O.; Ahani, A. Accuracy improvement for predicting parkinson’s disease progression. Sci. Rep., 2016, 6(1), 34181.
[http://dx.doi.org/10.1038/srep34181] [PMID: 27686748]
[113]
Salmanpour, M.R.; Shamsaei, M.; Saberi, A.; Setayeshi, S.; Klyuzhin, I.S.; Sossi, V.; Rahmim, A. Optimized machine learning methods for prediction of cognitive outcome in Parkinson’s disease. Comput. Biol. Med., 2019, 111, 103347.
[http://dx.doi.org/10.1016/j.compbiomed.2019.103347] [PMID: 31284154]
[114]
Drotár, P.; Mekyska, J.; Rektorová, I.; Masarová, L.; Smékal, Z.; Faundez-Zanuy, M. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease. Artif. Intell. Med., 2016, 67, 39-46.
[http://dx.doi.org/10.1016/j.artmed.2016.01.004] [PMID: 26874552]
[115]
Ahmadi Rastegar, D.; Ho, N.; Halliday, G.M.; Dzamko, N. Parkinson’s progression prediction using machine learning and serum cytokines. NPJ Parkinsons Dis., 2019, 5(1), 14.
[http://dx.doi.org/10.1038/s41531-019-0086-4] [PMID: 31372494]
[116]
Eskofier, B.M.; Lee, S.I.; Daneault, J.F.; Golabchi, F.N.; Ferreira-Carvalho, G.; Vergara-Diaz, G.; Sapienza, S.; Costante, G.; Klucken, J.; Kautz, T.; Bonato, P. Recent machine learning advancements in sensor-based mobility analysis: Deep learning for parkinson’s disease assessment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, , pp. 655-658.
[http://dx.doi.org/10.1109/EMBC.2016.7590787]
[117]
Beghi, E.; Giussani, G.; Nichols, E.; Abd-Allah, F.; Abdela, J.; Abdelalim, A.; Abraha, H.N.; Adib, M.G.; Agrawal, S.; Alahdab, F.; Awasthi, A.; Ayele, Y.; Barboza, M.A.; Belachew, A.B.; Biadgo, B.; Bijani, A.; Bitew, H.; Carvalho, F.; Chaiah, Y.; Daryani, A.; Do, H.P.; Dubey, M.; Endries, A.Y.Y.; Eskandarieh, S.; Faro, A.; Farzadfar, F.; Fereshtehnejad, S-M.; Fernandes, E.; Fijabi, D.O.; Filip, I.; Fischer, F.; Gebre, A.K.; Tsadik, A.G.; Gebremichael, T.G.; Gezae, K.E.; Ghasemi-Kasman, M.; Weldegwergs, K.G.; Degefa, M.G.; Gnedovskaya, E.V.; Hagos, T.B.; Haj-Mirzaian, A.; Haj-Mirzaian, A.; Hassen, H.Y.; Hay, S.I.; Jakovljevic, M.; Kasaeian, A.; Kassa, T.D.; Khader, Y.S.; Khalil, I.; Khan, E.A.; Khubchandani, J.; Kisa, A.; Krohn, K.J.; Kulkarni, C.; Nirayo, Y.L.; Mackay, M.T.; Majdan, M.; Majeed, A.; Manhertz, T.; Mehndiratta, M.M.; Mekonen, T.; Meles, H.G.; Mengistu, G.; Mohammed, S.; Naghavi, M.; Mokdad, A.H.; Mustafa, G.; Irvani, S.S.N.; Nguyen, L.H.; Nixon, M.R.; Ogbo, F.A.; Olagunju, A.T.; Olagunju, T.O.; Owolabi, M.O.; Phillips, M.R.; Pinilla-Monsalve, G.D.; Qorbani, M.; Radfar, A.; Rafay, A.; Rahimi-Movaghar, V.; Reinig, N.; Sachdev, P.S.; Safari, H.; Safari, S.; Safiri, S.; Sahraian, M.A.; Samy, A.M.; Sarvi, S.; Sawhney, M.; Shaikh, M.A.; Sharif, M.; Singh, G.; Smith, M.; Szoeke, C.E.I.; Tabarés-Seisdedos, R.; Temsah, M.H.; Temsah, O.; Tortajada-Girbés, M.; Tran, B.X.; Tsegay, A.A.T.; Ullah, I.; Venketasubramanian, N.; Westerman, R.; Winkler, A.S.; Yimer, E.M.; Yonemoto, N.; Feigin, V.L.; Vos, T.; Murray, C.J.L. Global, regional, and national burden of epilepsy, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol., 2019, 18(4), 357-375.
[http://dx.doi.org/10.1016/S1474-4422(18)30454-X] [PMID: 30773428]
[118]
Benival, D.; Salave, S.; Rana, D.; Pardhe, R.; Bule, P. Unravelling micro and nano vesicular system in intranasal drug delivery for epilepsy. Pharm. Nanotechnol., 2022, 10(3), 182-193.
[http://dx.doi.org/10.2174/2211738510666220426115340] [PMID: 35473543]
[119]
Anyanwu, C.; Motamedi, G. K. Diagnosis and surgical treatment of drug-resistant epilepsy. Brain Sci., 2018, 8(4), 49.
[http://dx.doi.org/10.3390/brainsci8040049]
[120]
Alotaiby, T. N.; Alshebeili, S. A.; Alshawi, T.; Ahmad, I.; Abd El-Samie, F. E. EEG seizure detection and prediction algorithms: A survey. Eurasip J. Adva. Sig. Proc., 2014, 183.
[http://dx.doi.org/10.1186/1687-6180-2014-183]
[121]
Alturki, F.A.; AlSharabi, K.; Abdurraqeeb, A.M.; Aljalal, M. EEG Signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors (Basel), 2020, 20(9), 2505.
[http://dx.doi.org/10.3390/s20092505] [PMID: 32354161]
[122]
Usman, S.M.; Usman, M.; Fong, S. Epileptic seizures prediction using machine learning methods. Comput. Math. Methods Med., 2017, 2017, 9074759.
[http://dx.doi.org/10.1155/2017/9074759] [PMID: 29410700]
[123]
Ramadhani, I.; Saputro, D.; Maryati, N. D.; Solihati, S. R.; Wijayanto, I.; Hadiyoso, S.; Patmasari, R. Seizure type classification on EEG signal using support vector machine recent citations convolutional neural networks ensemble model for neonatal seizure detection. Fractal Based Feature Extraction Method for Epileptic Seizure Detection in Long-Term EEG Recording Seizure Type Classification on EEG Signal Using Support Vector Machine, 2019, 12065.
[http://dx.doi.org/10.1088/1742-6596/1201/1/012065]
[124]
Raj, K.; Rajagopalan, S. S.; Bhardwaj, S.; Panda, R.; Reddam, V. R.; Chaitanya, G.; Raghavendra, K.; Mundlamuri, R. C.; Thennarasu, K.; Majumdar, K. K.; Satishchandra, P.; Sinha, S.; Bharath, R. D. Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy. Seizures, 2018, 61, 8-13.
[http://dx.doi.org/10.1016/j.seizure.2018.07.007]
[125]
Machine Learning for Seizure Type Classification. Available from: https://www.researchgate.net/publication/330871133_Machine_Learning_for_Seizure_Type_Classification_Setting_the_benchmark (Accessed on: May 12, 2021).
[126]
Liu, T.; Truong, N.D.; Nikpour, A.; Zhou, L.; Kavehei, O. Epileptic seizure classification with symmetric and hybrid bilinear models. IEEE J. Biomed. Health Inform., 2020, 24(10), 2844-2851.
[http://dx.doi.org/10.1109/JBHI.2020.2984128] [PMID: 32248133]
[127]
Ahmedt-Aristizabal, D.; Nguyen, K.; Denman, S.; Sridharan, S.; Dionisio, S.; Fookes, C. Deep motion analysis for epileptic seizure classification. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Institute of Electrical and Electronics Engineers Inc., 2018; pp. 3578-3581.
[http://dx.doi.org/10.1109/EMBC.2018.8513031]
[128]
Baud, M.O.; Kleen, J.K.; Anumanchipalli, G.K.; Hamilton, L.S.; Tan, Y.L.; Knowlton, R.; Chang, E.F. Unsupervised learning of spatiotemporal interictal discharges in focal epilepsy. Neurosurgery, 2018, 83(4), 683-691.
[http://dx.doi.org/10.1093/neuros/nyx480] [PMID: 29040672]
[129]
Akter, M.S.; Islam, M.R.; Iimura, Y.; Sugano, H.; Fukumori, K.; Wang, D.; Tanaka, T.; Cichocki, A. Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG. Sci. Rep., 2020, 10(1), 7044.
[http://dx.doi.org/10.1038/s41598-020-62967-z] [PMID: 32341371]
[130]
Okazaki, E.M.; Yao, R.; Sirven, J.I.; Crepeau, A.Z.; Noe, K.H.; Drazkowski, J.F.; Hoerth, M.T.; Salinas, E.; Csernak, L.; Mehta, N. Usage of EpiFinder clinical decision support in the assessment of epilepsy. Epilepsy Behav., 2018, 82, 140-143.
[http://dx.doi.org/10.1016/j.yebeh.2018.03.018] [PMID: 29625364]
[131]
Poh, M.Z.; Loddenkemper, T.; Swenson, N.C.; Goyal, S.; Madsen, J.R.; Picard, R.W. Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, 2010, pp. 4415-4418.
[http://dx.doi.org/10.1109/IEMBS.2010.5625988]
[132]
Rukasha, T.; Woolley, S. I.; Kyriacou, T.; Collins, T. Evaluation of wearable electronics for epilepsy: A systematic review. Electronics (Switzerland), 2020, 9(6), 968.
[http://dx.doi.org/10.3390/electronics9060968]
[133]
Alkan, A.; Sahin, Y.G.; Karlik, B. A novel mobile epilepsy warning system. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer Verlag, 2006; pp. 922-928.
[http://dx.doi.org/10.1007/11941439_99]
[134]
Ousley, O.; Cermak, T. Autism spectrum disorder: Defining dimensions and subgroups. Curr. Dev. Disord. Rep., 2014, 1(1), 20-28.
[http://dx.doi.org/10.1007/s40474-013-0003-1] [PMID: 25072016]
[135]
Faras, H.; Al Ateeqi, N.; Tidmarsh, L. Autism spectrum disorders. Ann. Saudi Medi., 2010, 30(4), 295-300.
[http://dx.doi.org/10.4103/0256-4947.65261]
[136]
Anzulewicz, A.; Sobota, K.; Delafield-Butt, J.T. Toward the autism motor signature: Gesture patterns during smart tablet gameplay identify children with autism. Sci. Rep., 2016, 6(1), 31107.
[http://dx.doi.org/10.1038/srep31107] [PMID: 27553971]
[137]
Ekins, S.; Gerlach, J.; Zorn, K.M.; Antonio, B.M.; Lin, Z.; Gerlach, A. Repurposing approved drugs as inhibitors of Kv7.1 and Nav1.8 to treat pitt hopkins syndrome. Pharm. Res., 2019, 36(9), 137.
[http://dx.doi.org/10.1007/s11095-019-2671-y] [PMID: 31332533]
[138]
Heyne, H.O.; Baez-Nieto, D.; Iqbal, S.; Palmer, D.S.; Brunklaus, A.; May, P.; Johannesen, K.M.; Lauxmann, S.; Lemke, J.R.; Møller, R.S.; Pérez-Palma, E.; Scholl, U.I.; Syrbe, S.; Lerche, H.; Lal, D.; Campbell, A.J.; Wang, H.R.; Pan, J.; Daly, M.J. Predicting functional effects of missense variants in voltage-gated sodium and calcium channels. Sci. Transl. Med., 2020, 12(556), eaay6848.
[http://dx.doi.org/10.1126/scitranslmed.aay6848] [PMID: 32801145]
[139]
Cummings, J.; Lee, G.; Ritter, A.; Sabbagh, M.; Zhong, K. Alzheimer’s disease drug development pipeline: 2020. Alzheimers Dement. (N. Y.), 2020, 6(1), e12050.
[http://dx.doi.org/10.1002/trc2.12050] [PMID: 32695874]
[140]
Swerdlow, R.H. Pathogenesis of Alzheimer’s Disease. Clinical interventions in aging; Dove Press, 2007, pp. 347-359.
[141]
Sprinz, C.; Zanon, M.; Altmayer, S.; Watte, G.; Irion, K.; Marchiori, E.; Hochhegger, B. Effects of blood glucose level on 18F fluorodeoxyglucose (18F-FDG) uptake for PET/CT in normal organs: an analysis on 5623 patients. Sci. Rep., 2018, 8(1), 2126.
[http://dx.doi.org/10.1038/s41598-018-20529-4] [PMID: 29391555]
[142]
Sarikaya, I.; Albatineh, A.N.; Sarikayaa, A. Effect of various blood glucose levels on regional FDG uptake in the brain. Asia Ocean. J. Nucl. Med. Biol., 2020, 8(1), 46-53.
[http://dx.doi.org/10.22038/aojnmb.2019.14418] [PMID: 32064282]
[143]
Mosconi, L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s Disease: FDG-PET studies in MCI and AD. Euro. J. Nucl. Medi. Mol. Imag., 2005, 32, 486-510.
[http://dx.doi.org/10.1007/s00259-005-1762-7]
[144]
An, Y.; Varma, V.R.; Varma, S.; Casanova, R.; Dammer, E.; Pletnikova, O.; Chia, C.W.; Egan, J.M.; Ferrucci, L.; Troncoso, J.; Levey, A.I.; Lah, J.; Seyfried, N.T.; Legido-Quigley, C.; O’Brien, R.; Thambisetty, M. Evidence for brain glucose dysregulation in Alzheimer’s disease. Alzheimers Dement., 2018, 14(3), 318-329.
[http://dx.doi.org/10.1016/j.jalz.2017.09.011] [PMID: 29055815]
[145]
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.; Behr, S.C.; Flavell, R.R.; Huang, S.Y.; Zalocusky, K.A.; Nardo, L.; Seo, Y.; Hawkins, R.A.; Hernandez Pampaloni, M.; Hadley, D.; Franc, B.L. A deep learning model to predict a diagnosis of alzheimer disease by using 18 F-FDG PET of the Brain. Radiology, 2019, 290(2), 456-464.
[http://dx.doi.org/10.1148/radiol.2018180958] [PMID: 30398430]
[146]
Duffy, I. R.; Boyle, A. J.; Vasdev, N. Improving PET imaging acquisition and analysis with machine learning: A narrative review with focus on Alzheimer’s Disease and oncology. Mol. Imaging, 2019, 18, 1536012119869070.
[http://dx.doi.org/10.1177/1536012119869070]
[147]
Singh, S.; Srivastava, A.; Mi, L.; Chen, K.; Wang, Y.; Caselli, R.J.; Goradia, D.; Reiman, E.M. Deep-learning-based classification of FDG-PET data for alzheimer’s disease categories. Proceedings of SPIE-The Int. Soc. Optical Eng., 2017, p. 84.
[http://dx.doi.org/10.1117/12.2294537]
[148]
Wang, L.; Liu, Z.P. Detecting diagnostic biomarkers of alzheimer’s disease by integrating gene expression data in six brain regions. Front. Genet., 2019, 10(MAR), 157.
[http://dx.doi.org/10.3389/fgene.2019.00157] [PMID: 30915100]
[149]
Huo, Z.; Shen, D.; Huang, H. Genotype-phenotype association study via new multi-task learning model. Pacific Symposium on Biocomputing, 2018, pp. 353-364.
[http://dx.doi.org/10.1142/9789813235533_0033]
[150]
Xu, L.; Liang, G.; Liao, C.; Chen, G.D.; Chang, C.C. An efficient classifier for alzheimer’s disease genes identification. Molecules, 2018, 23(12), 3140.
[http://dx.doi.org/10.3390/molecules23123140] [PMID: 30501121]
[151]
Kong, W.; Mou, X.; Hu, X. Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data. BMC Bioinformatics, 2011, 12(S5), S7.
[http://dx.doi.org/10.1186/1471-2105-12-S5-S7] [PMID: 21989140]
[152]
Scheubert, L.; Luštrek, M.; Schmidt, R.; Repsilber, D.; Fuellen, G. Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets. BMC Bioinformatics, 2012, 13(1), 266.
[http://dx.doi.org/10.1186/1471-2105-13-266] [PMID: 23066814]
[153]
Miao, Y.; Jiang, H.; Liu, H.; Yao, Y. An Alzheimers disease related genes identification method based on multiple classifier integration. Comput. Methods Programs Biomed., 2017, 150, 107-115.
[http://dx.doi.org/10.1016/j.cmpb.2017.08.006] [PMID: 28859826]
[154]
Han, B.; Chen, X.; Talebizadeh, Z.; Xu, H. Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks. BMC Syst. Biol., 2012, 6(S3), S14.
[http://dx.doi.org/10.1186/1752-0509-6-S3-S14] [PMID: 23281790]
[155]
Zieselman, A.L.; Fisher, J.M.; Hu, T.; Andrews, P.C.; Greene, C.S.; Shen, L.; Saykin, A.J.; Moore, J.H. Computational genetics analysis of grey matter density in Alzheimer’s disease. BioData Min., 2014, 7(1), 17.
[http://dx.doi.org/10.1186/1756-0381-7-17] [PMID: 25165488]
[156]
Hibar, D.P.; Stein, J.L.; Jahanshad, N.; Kohannim, O.; Hua, X.; Toga, A.W.; McMahon, K.L.; de Zubicaray, G.I.; Martin, N.G.; Wright, M.J.; Weiner, M.W.; Thompson, P.M. Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. Neurobiol. Aging, 2015, 36(Suppl. 1), S151-S158.
[http://dx.doi.org/10.1016/j.neurobiolaging.2014.02.033] [PMID: 25264344]
[157]
Rana, D.; Salave, S.; Longare, S.; Agarwal, R.; Kalia, K.; Benival, D. Nanotherapeutics in tumour microenvironment for cancer therapy. Nanosci. Nanotechnol. Asia, 2022, 12(1), e080921196283.
[http://dx.doi.org/10.2174/2210681211666210908144839]
[158]
Abd-Ellah, M.K.; Awad, A.I.; Khalaf, A.A.M.; Hamed, H.F.A. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magn. Reson. Imaging, 2019, 61, 300-318.
[http://dx.doi.org/10.1016/j.mri.2019.05.028] [PMID: 31173851]
[159]
Dandil, E.; Çakiroǧlu, M.; Ekşi, Z. Computer-aided diagnosis of malign and benign brain tumors on MR images. In: Advances in Intelligent Systems and Computing; Springer Verlag, 2015; pp. 157-166.
[http://dx.doi.org/10.1007/978-3-319-09879-1_16]
[160]
Mishra, R. MRI based brain tumor detection using wavelet packet feature and artificial neural networks. Conf. Proceed., 2010, pp. 656-659.
[http://dx.doi.org/10.1145/1741906.1742054]
[161]
Tandel, G.S.; Balestrieri, A.; Jujaray, T.; Khanna, N.N.; Saba, L.; Suri, J.S. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput. Biol. Med., 2020, 122, 103804.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103804] [PMID: 32658726]
[162]
Sultan, H.H.; Salem, N.M.; Al-Atabany, W. Multi-classification of brain tumor images using deep neural network. IEEE Access, 2019, 7, 69215-69225.
[http://dx.doi.org/10.1109/ACCESS.2019.2919122]
[163]
Chen, C.; Ou, X.; Wang, J.; Guo, W.; Ma, X. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors. Front. Oncol., 2019, 9, 806.
[http://dx.doi.org/10.3389/fonc.2019.00806] [PMID: 31508366]
[164]
Mohsen, H.; El-Dahshan, E.S.A.; El-Horbaty, E.S.M.; Salem, A.B.M. Classification using deep learning neural networks for brain tumors. Future Comput. Inform. J., 2018, 3(1), 68-71.
[http://dx.doi.org/10.1016/j.fcij.2017.12.001]
[165]
Polly, F.P.; Shil, S.K.; Hossain, M.A.; Ayman, A.; Jang, Y.M. Detection and classification of HGG and LGG brain tumor using machine learning. Int. Conf. Information Networking, 2018, , pp. 813-817.
[http://dx.doi.org/10.1109/ICOIN.2018.8343231]
[166]
Badža, M.M.; Barjaktarović, M.Č. Classification of brain tumors from mri images using a convolutional neural network. Appl. Sci. (Basel), 2020, 10(6), 1999.
[http://dx.doi.org/10.3390/app10061999]
[167]
Badran, E.F.; Mahmoud, E.G.; Hamdy, N. An algorithm for detecting brain tumors in MRI images. Proceedings, ICCES’2010 - 2010 International Conference on Computer Engineering and Systems, 2010, pp. 368-373.
[http://dx.doi.org/10.1109/ICCES.2010.5674887]
[168]
Abdelaziz, I.S.A.; Mohammed, A.; Hefny, H. An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif. Intell. Med., 2020, 102, 101779.
[http://dx.doi.org/10.1016/j.artmed.2019.101779] [PMID: 31980109]
[169]
Zacharaki, E.I.; Wang, S.; Chawla, S.; Soo Yoo, D.; Wolf, R.; Melhem, E.R.; Davatzikos, C. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med., 2009, 62(6), 1609-1618.
[http://dx.doi.org/10.1002/mrm.22147] [PMID: 19859947]
[170]
Chandra, S.; Bhat, R.; Singh, H. A PSO based method for detection of brain tumors from MRI. 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings, 2009, pp. 666-671.
[http://dx.doi.org/10.1109/NABIC.2009.5393455]
[171]
Marshkole, N.; Singh, K.; Thoke, A. S. Texture and shape based classification of brain tumors using linear vector quantization 2011, 30, 21-23.
[172]
Kaus, M.R.; Warfield, S.K.; Nabavi, A.; Black, P.M.; Jolesz, F.A.; Kikinis, R. Automated segmentation of MR images of brain tumors. Radiology, 2001, 218(2), 586-591.
[http://dx.doi.org/10.1148/radiology.218.2.r01fe44586] [PMID: 11161183]
[173]
Fletcher-Heath, L.M.; Hall, L.O.; Goldgof, D.B.; Murtagh, F.R. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif. Intell. Med., 2001, 21(1-3), 43-63.
[http://dx.doi.org/10.1016/S0933-3657(00)00073-7] [PMID: 11154873]
[174]
Schmidt, M.; Levner, I.; Greiner, R.; Murtha, A.; Bistritz, A. Segmenting brain tumors using alignment-based features. ICMLA 2005: Fourth International Conference on Machine Learning and Applications, 2005, pp. 218-220.
[http://dx.doi.org/10.1109/ICMLA.2005.56]
[175]
Das, A.; Bhattacharya, M. A study on prognosis of brain tumors using fuzzy logic and genetic algorithm based techniques. Proceedings - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS, 2009, 2009, pp. 348-351.
[http://dx.doi.org/10.1109/IJCBS.2009.129]
[176]
Nalepa, J.; Ribalta Lorenzo, P.; Marcinkiewicz, M.; Bobek-Billewicz, B.; Wawrzyniak, P.; Walczak, M.; Kawulok, M.; Dudzik, W.; Kotowski, K.; Burda, I.; Machura, B.; Mrukwa, G.; Ulrych, P.; Hayball, M.P. Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artif. Intell. Med., 2020, 102, 101769.
[http://dx.doi.org/10.1016/j.artmed.2019.101769] [PMID: 31980106]
[177]
Manogaran, G.; Shakeel, P.M.; Hassanein, A.S.; Malarvizhi, K.P.; Chandra, B.G. Machine learning approach-based gamma distribution for brain tumor detection and data sample imbalance analysis. IEEE Access, 2019, 7, 12-19.
[http://dx.doi.org/10.1109/ACCESS.2018.2878276]
[178]
Hashimoto, D.A.; Witkowski, E.; Gao, L.; Meireles, O.; Rosman, G. Artificial intelligence in anesthesiology. Anesthesiology, 2020, 132(2), 379-394.
[http://dx.doi.org/10.1097/ALN.0000000000002960] [PMID: 31939856]
[179]
Kobayashi, N.; Shiga, T.; Ikumi, S.; Watanabe, K.; Murakami, H.; Yamauchi, M. Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study. Sci. Rep., 2021, 11(1), 5229.
[http://dx.doi.org/10.1038/s41598-021-84714-8] [PMID: 33664391]
[180]
Wang, R.; Wang, S.; Duan, N.; Wang, Q. From patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: Practices and perspectives. Front. Med. (Lausanne), 2020, 7, 145.
[http://dx.doi.org/10.3389/fmed.2020.00145] [PMID: 32671076]
[181]
Benival, D.; Rana, D.; Salave, S. Emerging trends in abuse-deterrent formulations: Technological insights and regulatory considerations. Curr. Drug Deliv., 2022, 19(8), 846-859.
[http://dx.doi.org/10.2174/1567201818666211208101035] [PMID: 34879799]
[182]
Lo, W.L.A.; Lei, D.; Li, L.; Huang, D.F.; Tong, K.F. The perceived benefits of an artificial intelligence–embedded mobile app implementing evidence-based guidelines for the self-management of chronic neck and back pain: Observational study. JMIR mhealth uhealth, 2018, 6(11), e198.
[http://dx.doi.org/10.2196/mhealth.8127] [PMID: 30478019]
[183]
Agboola, S.; Kamdar, M.; Flanagan, C.; Searl, M.; Traeger, L.; Kvedar, J.; Jethwani, K. Pain management in cancer patients using a mobile app: study design of a randomized controlled trial. JMIR Res. Protoc., 2014, 3(4), e76.
[http://dx.doi.org/10.2196/resprot.3957] [PMID: 25500281]

Rights & Permissions Print Cite
Article Metrics
39
4
Wayfinder Image
Open Access Journals Promotions
© 2024 Bentham Science Publishers | Privacy Policy