Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Indian Science
  4. Volume 1, Issue 1
  5. Article
Volume 1, Issue 1
  • ISSN: 2210-299X
  • E-ISSN: 2210-3007
file format text download EPUB
file format pdf download PDF
side by side viewer icon HTML

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, 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.

© 2023 Bentham Science Publishers
© 2023 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cis/10.2174/2210299X01666230223104508
2023-01-01
2024-11-22

  • From This Site

    /content/journals/cis/10.2174/2210299X01666230223104508
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cis/1/1/CIS-1-E230223213949.html?itemId=/content/journals/cis/10.2174/2210299X01666230223104508&mimeType=html&fmt=ahah

References

  1. AgrawalP. Artificial intelligence in drug discovery and development.J. Pharmacovigil.201862e17310.4172/2329‑6887.1000e173
     [Google Scholar]
  2. SinghP. BardhanA. HanF. SamuiP. ZhangW. A critical review of conventional and soft computing methods for slope stability analysis.Model. Earth Syst. Environ.2023911710.1007/s40808‑022‑01489‑1
     [Google Scholar]
  3. VatanseverS. SchlessingerA. WackerD. KaniskanÜmit JinJ. ZhouM.-M. ZhangB. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Med. Res. Rev.20204131427147310.1002/med.21764
     [Google Scholar]
  4. ChuangK.V. GunsalusL.M. KeiserM.J. Learning molecular representations for medicinal chemistry.J. Med. Chem.202063168705872210.1021/acs.jmedchem.0c0038532366098
     [Google Scholar]
  5. ZhangL. TanJ. HanD. ZhuH. From machine learning to deep learning: progress in machine intelligence for rational drug discovery.Drug Discov. Today201722111680168510.1016/j.drudis.2017.08.01028881183
     [Google Scholar]
  6. McKinneyS.M. SieniekM. GodboleV. GodwinJ. AntropovaN. AshrafianH. BackT. ChesusM. CorradoG.S. DarziA. EtemadiM. Garcia-VicenteF. GilbertF.J. Halling-BrownM. HassabisD. JansenS. KarthikesalingamA. KellyC.J. KingD. LedsamJ.R. MelnickD. MostofiH. PengL. ReicherJ.J. Romera-ParedesB. SidebottomR. SuleymanM. TseD. YoungK.C. De FauwJ. ShettyS. International evaluation of an AI system for breast cancer screening.Nature20205777788899410.1038/s41586‑019‑1799‑631894144
     [Google Scholar]
  7. Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments - BloombergAvailable 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.2016Available 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. ZhuH. Big data and artifical intelligence modeling for drug discovery.Ann. Rev. Pharmacol. Toxicol.20206057358910.1146/annurev‑pharmtox‑010919
     [Google Scholar]
  11. ZhangW. GuX. TangL. YinY. LiuD. ZhangY. Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge.Gondwana Res.202210911710.1016/j.gr.2022.03.015
     [Google Scholar]
  12. AIDeep learning, and neural networks explained.Available from: https://www.innoarchitech.com/blog/artificial-intelligence-deep-learning-neural-networks-explained.
  13. PhoonK. K. ZhangW. Future of machine learning in geotechnics.Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards202210.1080/17499518.2022.2087884
     [Google Scholar]
  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 BlogAvailable 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 explanationAvailable 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. InstituteI.C.T. AI, Machine Learning and neural networks explainedAvailable 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. HassabisD. KumaranD. SummerfieldC. BotvinickM. Neuroscience-inspired artificial intelligence.Neuron201795224525810.1016/j.neuron.2017.06.01128728020
     [Google Scholar]
  24. KriegeskorteN. DouglasP.K. Cognitive computational neuroscience.Nat. Neurosci.20182191148116010.1038/s41593‑018‑0210‑530127428
     [Google Scholar]
  25. BarchetT.M. AmijiM.M. Challenges and opportunities in CNS delivery of therapeutics for neurodegenerative diseases.Expert Opin. Drug Deliv.20096321122510.1517/1742524090275818819290842
     [Google Scholar]
  26. ZhangX. MaZ. ZhengH. LiT. ChenK. WangX. LiuC. XuL. WuX. LinD. LinH. The combination of brain-computer interfaces and artificial intelligence: applications and challenges.Ann. Transl. Med.202081171271210.21037/atm.2019.11.10932617332
     [Google Scholar]
  27. KrucoffM. O. RahimpourS. SlutzkyM. W. EdgertonV. R. TurnerD. A. Enhancing nervous system recovery through neurobiologics, neural interface training, and neurorehabilitation.Fronti. Neurosci.20161058410.3389/fnins.2016.00584
     [Google Scholar]
  28. FriedenbergD.A. SchwemmerM.A. LandgrafA.J. AnnettaN.V. BockbraderM.A. BoutonC.E. ZhangM. RezaiA.R. MysiwW.J. BreslerH.S. SharmaG. Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human.Sci. Rep.201771838610.1038/s41598‑017‑08120‑928827605
     [Google Scholar]
  29. NiketeghadS. PouratianN. Brain machine interfaces for vision restoration: The current state of cortical visual prosthetics.Neurotherapeutics201916113414310.1007/s13311‑018‑0660‑130194614
     [Google Scholar]
  30. Sebastián-RomagosaM. ChoW. OrtnerR. MurovecN. Von OertzenT. KamadaK. AllisonB.Z. GugerC. Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients—a feasibility study.Front. Neurosci.20201459143510.3389/fnins.2020.59143533192277
     [Google Scholar]
  31. BrumbergJ. S. GuentherF. H. Development of speech prostheses: Current status and recent advances.Rev. Med. Der.201066767910.1586/erd.10.34
     [Google Scholar]
  32. BrumbergJ.S. Nieto-CastanonA. KennedyP.R. GuentherF.H. Brain–computer interfaces for speech communication.Speech Commun.201052436737910.1016/j.specom.2010.01.00120204164
     [Google Scholar]
  33. SchreuderM. BlankertzB. TangermannM. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue.PLoS One201054e981310.1371/journal.pone.000981320368976
     [Google Scholar]
  34. FerracutiF. FreddiA. IarloriS. LonghiS. PerettiP. Auditory paradigm for a P300 BCI system using spatial hearing.2013 IEEE/RSJ International Conference on Intelligent Robots and SystemsTokyo, Japan201387187610.1109/IROS.2013.6696453
     [Google Scholar]
  35. YaoL. ShengX. ZhangD. JiangN. FarinaD. ZhuX. A BCI system based on somatosensory attentional orientation.IEEE Trans. Neural Syst. Rehabil. Eng.2017251819010.1109/TNSRE.2016.257222627244745
     [Google Scholar]
  36. KrolS. Challenges in drug delivery to the brain: Nature is against us.J. Control. Release2012164214515510.1016/j.jconrel.2012.04.04422609350
     [Google Scholar]
  37. WolburgH. LippoldtA. Tight junctions of the blood–brain barrier.Vascul. Pharmacol.200238632333710.1016/S1537‑1891(02)00200‑812529927
     [Google Scholar]
  38. WongA.D. YeM. LevyA.F. RothsteinJ.D. BerglesD.E. SearsonP.C. The blood-brain barrier: an engineering perspective.Front. Neuroeng.20136710.3389/fneng.2013.0000724009582
     [Google Scholar]
  39. DotiwalaA.K. McCauslandC. SamraN.S. Anatomy, Head and Neck, Blood Brain Barrier.StatPearls Publishing2020
     [Google Scholar]
  40. FenstermacherJ.D. Pharmacology of the blood-brain barrier.Implications of the Blood-Brain Barrier and Its Manipulation.Springer US198913715510.1007/978‑1‑4613‑0701‑3_6
     [Google Scholar]
  41. DanemanR. PratA. The blood-brain barrier.Cold Spring Harb. Perspect. Biol.201571a02041210.1101/cshperspect.a02041225561720
     [Google Scholar]
  42. ShenJ. ChengF. XuY. LiW. TangY. Estimation of ADME properties with substructure pattern recognition.J. Chem. Inf. Model.20105061034104110.1021/ci100104j20578727
     [Google Scholar]
  43. AndresC. HutterM.C. CNS Permeability of drugs predicted by a decision tree.QSAR Comb. Sci.200625430530910.1002/qsar.200510200
     [Google Scholar]
  44. ZhangL. ZhuH. OpreaT.I. GolbraikhA. TropshaA. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds.Pharm. Res.20082581902191410.1007/s11095‑008‑9609‑018553217
     [Google Scholar]
  45. MenteS.R. LombardoF. A recursive-partitioning model for blood–brain barrier permeation.J. Comput. Aided Mol. Des.200519746548110.1007/s10822‑005‑9001‑716331406
     [Google Scholar]
  46. ObrezanovaO. CsányiG. GolaJ.M.R. SegallM.D. Gaussian processes: a method for automatic QSAR modeling of ADME properties.J. Chem. Inf. Model.20074751847185710.1021/ci700063317602549
     [Google Scholar]
  47. VilarS. ChakrabartiM. CostanziS. Prediction of passive blood–brain partitioning: Straightforward and effective classification models based on in silico derived physicochemical descriptors.J. Mol. Graph. Model.201028889990310.1016/j.jmgm.2010.03.01020427217
     [Google Scholar]
  48. GargP. VermaJ. In silico prediction of blood brain barrier permeability: an artificial neural network model.J. Chemi. Inform. Model.200646128929710.1021/ci050303i
     [Google Scholar]
  49. WangZ. YangH. WuZ. WangT. LiW. TangY. LiuG. In silico prediction of blood-brain barrier permeability of compounds by machine learning and resampling methods.ChemMedChem201813202189220110.1002/cmdc.20180053330110511
     [Google Scholar]
  50. GaoZ. ChenY. CaiX. XuR. SahinalpC. Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications.Bioinformatics2016336btw71310.1093/bioinformatics/btw71327993785
     [Google Scholar]
  51. PatchingS. G. Glucose transporters at the blood-brain barrier: function, regulation and gateways for drug delivery.Molecular Neurobiology20171046107710.1007/s12035‑015‑9672‑6
     [Google Scholar]
  52. LöscherW. PotschkaH. Blood-brain barrier active efflux transporters: ATP-binding cassette gene family.NeuroRx200521869810.1602/neurorx.2.1.8615717060
     [Google Scholar]
  53. YuanY. ZhengF. ZhanC.G. Improved prediction of blood–brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints.AAPS J.20182035410.1208/s12248‑018‑0215‑829564576
     [Google Scholar]
  54. FrankC.L. BrownJ.P. WallaceK. MundyW.R. ShaferT.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.2017160112113510.1093/toxsci/kfx16928973552
     [Google Scholar]
  55. ChanH.P. DoiK. GalhotraS. VybornyC.J. MacMahonH. JokichP.M. Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography.Med. Phys.198714453854810.1118/1.5960653626993
     [Google Scholar]
  56. GigerM.L. DoiK. MacMahonH. Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields.Med. Phys.198815215816610.1118/1.5962473386584
     [Google Scholar]
  57. ArimuraH. MagomeT. YamashitaY. YamamotoD. Computer-aided diagnosis systems for brain diseases in magnetic resonance images.Algorithms2009292595210.3390/a2030925
     [Google Scholar]
  58. ChilamkurthyS. GhoshR. TanamalaS. BivijiM. CampeauN.G. VenugopalV.K. MahajanV. RaoP. WarierP. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.Lancet2018392101622388239610.1016/S0140‑6736(18)31645‑330318264
     [Google Scholar]
  59. SuzukiK. Pixel-based machine learning in medical imaging.Int. J. Biomed. Imaging2012201211810.1155/2012/79207922481907
     [Google Scholar]
  60. RaghavendraU. AcharyaU. R. AdeliH. Artificial intelligence techniques for automated diagnosis of neurological disorders.Euro. Neurol.2020416410.1159/000504292
     [Google Scholar]
  61. SeoY. MariC. HasegawaB. H. Technological development and advances in single-photon emission computed tomography/computed tomography.Semi. Nucl. Med.200838317719810.1053/j.semnuclmed.2008.01.001
     [Google Scholar]
  62. HemondC.C. BakshiR. Magnetic resonance imaging in multiple sclerosis.Cold Spring Harb. Perspect. Med.201885a02896910.1101/cshperspect.a02896929358319
     [Google Scholar]
  63. JensenA. la Cour-HarboA. Introduction.Ripples in MathematicsSpringer Berlin Heidelberg20011510.1007/978‑3‑642‑56702‑5_1
     [Google Scholar]
  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. MalladiR. SethianJ.A. VemuriB.C. Shape modeling with front propagation: a level set approach.IEEE Trans. Pattern Anal. Mach. Intell.199517215817510.1109/34.368173
     [Google Scholar]
  66. YinP.Y. Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization.Sig. Proc.2002827993100610.1016/S0165‑1684(02)00203‑7
     [Google Scholar]
  67. NixonM. AguadoA.S. Feature Extraction & Image Processing for Computer Vision.Elsevier Ltd201210.1016/C2011‑0‑06935‑1
     [Google Scholar]
  68. KhotanzadA. HongY.H. Invariant image recognition by Zernike moments.IEEE Trans. Pattern Anal. Mach. Intell.199012548949710.1109/34.55109
     [Google Scholar]
  69. Ming-Kuei Hu, Visual pattern recognition by moment invariants.IEEE Trans. Inf. Theory19628217918710.1109/TIT.1962.1057692
     [Google Scholar]
  70. HyvärinenA. OjaE. Independent component analysis: Algorithms and applications.Neutral Netw.2000134-541143010.1016/S0893‑6080(00)00026‑5
     [Google Scholar]
  71. SchölkopfSchB. Smola Klaus-RobertA.M. Communicated by peter dayan nonlinear component analysis as a kernel eigenvalue problem.Neural Computation19981012991319
     [Google Scholar]
  72. ALM SIGMOO DashM. LiuH. Workshop on research issues in data mining & knowledge discovery. Handling large unsupervised data via dimensionality reduction.1999
     [Google Scholar]
  73. YuanY. Van AllenE.M. OmbergL. WagleN. Amin-MansourA. SokolovA. ByersL.A. XuY. HessK.R. DiaoL. HanL. HuangX. LawrenceM.S. WeinsteinJ.N. StuartJ.M. MillsG.B. GarrawayL.A. MargolinA.A. GetzG. LiangH. Assessing the clinical utility of cancer genomic and proteomic data across tumor types.Nat. Biotechnol.201432764465210.1038/nbt.294024952901
     [Google Scholar]
  74. ShiY. EberhartR. Modified particle swarm optimizer.Proceedings of the IEEE Conference on Evolutionary Computation, ICECIEEE1998697310.1109/ICEC.1998.699146
     [Google Scholar]
  75. SpechtD.F. Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification.IEEE Trans. Neural Netw.19901111112110.1109/72.8021018282828
     [Google Scholar]
  76. DaiH. CaoZ. A wavelet support vector machine-based neural network metamodel for structural reliability assessment.Comput. Aided Civ. Infrastruct. Eng.201732434435710.1111/mice.12257
     [Google Scholar]
  77. OlanowC.W. WattsR.L. KollerW.C. An algorithm (decision tree) for the management of Parkinson’s disease (2001): Treatment.Neurology20015611Suppl. 5S1S8810.1212/WNL.56.suppl_5.S111402154
     [Google Scholar]
  78. BablaniA. EdlaD.R. DodiaS. Classification of EEG data using K-nearest neighbor approach for concealed information test.Procedia Computer Science.Elsevier B.V.201824224910.1016/j.procs.2018.10.392
     [Google Scholar]
  79. MandelkowH. De ZwartJ. A. DuynJ. H. Linear discriminant analysis achieves high classification accuracy for the BOLD FMRI response to naturalistic movie stimuli.Front. Hum. Neurosci.201610MAR201611210.3389/fnhum.2016.00128
     [Google Scholar]
  80. SaricaA. CerasaA. QuattroneA. Random forest algorithm for the classification of neuroim. data in Alzheim. disease: A systematic review.Front. Aging Neurosci.2017932910.3389/fnagi.2017.00329
     [Google Scholar]
  81. LiR. PerneczkyR. YakushevI. FörsterS. KurzA. DrzezgaA. KramerS. Alzheimer’s Disease Neuroimaging Initiative Gaussian mixture models and model selection for [18F] fluorodeoxyglucose positron emission tomography classification in alzheimer’s disease.PLoS One2015104e012273110.1371/journal.pone.012273125919662
     [Google Scholar]
  82. PrzedborskiS. VilaM. Jackson-LewisV. Series introduction: Neurodegeneration: What is it and where are we?J. Clin. Invest.2003111131010.1172/JCI20031752212511579
     [Google Scholar]
  83. SrejovicI. SelakovicD. JovicicN. JakovljevićV. LukicM. L. RosicG. Galectin-3: Roles in neurodevelopment, neuroinflammation, and behavior.Biomolecules202010579810.3390/biom10050798
     [Google Scholar]
  84. KwonH. S. KohS. H. Neuroinflammation in neurodegenerative disorders: The roles of microglia and astrocytes.Transl. Neurodegeneration202011210.1186/s40035‑020‑00221‑2
     [Google Scholar]
  85. TanY. ZhengY. XuD. SunZ. YangH. YinQ. Galectin-3: a key player in microglia-mediated neuroinflammation and Alzheimer’s disease.Cell Biosci.20211117810.1186/s13578‑021‑00592‑733906678
     [Google Scholar]
  86. DengL. ZhongW. ZhaoL. HeX. LianZ. JiangS. ChenC.Y.C. Artificial intelligence-based application to explore inhibitors of neurodegenerative diseases.Front. Neurorobot.20201461732710.3389/fnbot.2020.61732733414713
     [Google Scholar]
  87. SymptomsS. Patterns and statistics and patternsAvailable from: https://www.mentalhelp.net/schizophrenia/statistics/ (Accessed on July 13, 2022).
  88. JablenskyA. The diagnostic concept of schizophrenia: its history, evolution, and future prospects.Dialogues Clin. Neurosci.201012327128710.31887/DCNS.2010.12.3/ajablensky20954425
     [Google Scholar]
  89. CorrellC.U. SchoolerN.R. Negative Symptoms in Schizophrenia: A Review and Clinical Guide for Recognition, Assessment, and Treatment. Neuropsychiatric Disease and Treatment.Dove Medical Press Ltd.202051953410.2147/NDT.S225643
     [Google Scholar]
  90. KambeitzJ. Kambeitz-IlankovicL. LeuchtS. WoodS. DavatzikosC. MalchowB. FalkaiP. KoutsoulerisN. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.Neuropsychopharmacology20154071742175110.1038/npp.2015.2225601228
     [Google Scholar]
  91. SchnackH.G. Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases).Schizophr. Res.2019214344210.1016/j.schres.2017.10.02329074332
     [Google Scholar]
  92. TalpalaruA. BhagwatN. DevenyiG.A. LepageM. ChakravartyM.M. Identifying schizophrenia subgroups using clustering and supervised learning.Schizophr. Res.2019214515910.1016/j.schres.2019.05.04431455518
     [Google Scholar]
  93. HsuK.C. WangF.S. Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity.PLoS One2017126e017957510.1371/journal.pone.017957528614410
     [Google Scholar]
  94. MarunnanS.M. PulikkalB.P. JabamalairajA. BandaruS. YadavM. NayarisseriA. DossV.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.20171581085109210.2174/156720181466616120513174527919211
     [Google Scholar]
  95. YangQ.X. WangY.X. LiF.C. ZhangS. LuoY.C. LiY. TangJ. LiB. ChenY.Z. XueW.W. ZhuF. Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence-based method of enhanced reproducibility.CNS Neurosci. Ther.20192591054106310.1111/cns.1319631350824
     [Google Scholar]
  96. ZhaoK. SoH.C. Drug repositioning for schizophrenia and depression/anxiety disorders: A machine learning approach leveraging expression data.IEEE J. Biomed. Health Inform.20192331304131510.1109/JBHI.2018.285653530010603
     [Google Scholar]
  97. BainE.E. ShafnerL. WallingD.P. OthmanA.A. Chuang-SteinC. HinkleJ. HaninaA. 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 uhealth201752e1810.2196/mhealth.703028223265
     [Google Scholar]
  98. ChallaK.N.R. PagoluV.S. PandaG. MajhiB. 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 - ProceedingsInstitute of Electrical and Electronics Engineers Inc.20171446145110.1109/SCOPES.2016.7955679
     [Google Scholar]
  99. PrashanthR. DuttaR. S. MandalP.K. GhoshS. High-accuracy detection of early parkinson’s disease through multimodal features and machine learning.Int. J. Med. Inform.201690132110.1016/j.ijmedinf.2016.03.00127103193
     [Google Scholar]
  100. KangJ.H. IrwinD.J. Chen-PlotkinA.S. SiderowfA. CaspellC. CoffeyC.S. WaligórskaT. TaylorP. PanS. FrasierM. MarekK. KieburtzK. JenningsD. SimuniT. TannerC.M. SingletonA. TogaA.W. ChowdhuryS. MollenhauerB. TrojanowskiJ.Q. ShawL.M. Parkinson’s Progression Markers Initiative 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.201370101277128710.1001/jamaneurol.2013.386123979011
     [Google Scholar]
  101. ParisiL. RaviC.N. ManaogM.L. Feature-driven machine learning to improve early diagnosis of Parkinson’s disease.Expert Syst. Appl.201811018219010.1016/j.eswa.2018.06.003
     [Google Scholar]
  102. GroverS. BhartiaS. Predicting severity of parkinson’s disease using deep learning.Procedia Computer ScienceElsevier B.V.20181788179410.1016/j.procs.2018.05.154
     [Google Scholar]
  103. WrogeT.J. ÖzkancaY. DemirogluC. SiD. AtkinsD.C. GhomiR.H. Parkinson’s disease diagnosis using machine learning and voice.2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - ProceedingsInstitute of Electrical and Electronics Engineers Inc.201910.1109/SPMB.2018.8615607
     [Google Scholar]
  104. PrashanthR. Dutta RoyS. Early detection of Parkinson’s disease through patient questionnaire and predictive modelling.Int. J. Med. Inform.2018119758710.1016/j.ijmedinf.2018.09.00830342689
     [Google Scholar]
  105. KarapinarS. Z. Early diagnosis of Parkinson’s disease using machine learning algorithms.Med. Hypotheses202013810960310.1016/j.mehy.2020.10960332028195
     [Google Scholar]
  106. BragaD. MadureiraA.M. CoelhoL. AjithR. Automatic detection of Parkinson’s disease based on acoustic analysis of speech.Eng. Appl. Artif. Intell.20197714815810.1016/j.engappai.2018.09.018
     [Google Scholar]
  107. ShettyS. RaoY.S. SVM based machine learning approach to identify parkinson’s disease using gait analysis.Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016Institute of Electrical and Electronics Engineers Inc.201610.1109/INVENTIVE.2016.7824836
     [Google Scholar]
  108. GunduzH. Deep learning-based Parkinson’s Disease classification using vocal feature sets.IEEE Access2019711554011555110.1109/ACCESS.2019.2936564
     [Google Scholar]
  109. MostafaS.A. MustaphaA. MohammedM.A. HamedR.I. ArunkumarN. Abd GhaniM.K. JaberM.M. KhaleefahS.H. Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease.Cogn. Syst. Res.201954909910.1016/j.cogsys.2018.12.004
     [Google Scholar]
  110. CaiZ. GuJ. ChenH.L. A new hybrid intelligent framework for predicting Parkinson’s Disease.IEEE Access20175171881720010.1109/ACCESS.2017.2741521
     [Google Scholar]
  111. LahmiriS. DawsonD.A. ShmuelA. Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures.Biomed. Eng. Lett.201881293910.1007/s13534‑017‑0051‑230603188
     [Google Scholar]
  112. NilashiM. IbrahimO. AhaniA. Accuracy improvement for predicting parkinson’s disease progression.Sci. Rep.2016613418110.1038/srep3418127686748
     [Google Scholar]
  113. SalmanpourM.R. ShamsaeiM. SaberiA. SetayeshiS. KlyuzhinI.S. SossiV. RahmimA. Optimized machine learning methods for prediction of cognitive outcome in Parkinson’s disease.Comput. Biol. Med.201911110334710.1016/j.compbiomed.2019.10334731284154
     [Google Scholar]
  114. DrotárP. MekyskaJ. RektorováI. MasarováL. SmékalZ. Faundez-ZanuyM. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease.Artif. Intell. Med.201667394610.1016/j.artmed.2016.01.00426874552
     [Google Scholar]
  115. Ahmadi RastegarD. HoN. HallidayG.M. DzamkoN. Parkinson’s progression prediction using machine learning and serum cytokines.NPJ Parkinsons Dis.2019511410.1038/s41531‑019‑0086‑431372494
     [Google Scholar]
  116. EskofierB.M. LeeS.I. DaneaultJ.F. GolabchiF.N. Ferreira-CarvalhoG. Vergara-DiazG. SapienzaS. CostanteG. KluckenJ. KautzT. BonatoP. 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, EMBSInstitute of Electrical and Electronics Engineers Inc.201665565810.1109/EMBC.2016.7590787
     [Google Scholar]
  117. BeghiE. GiussaniG. NicholsE. Abd-AllahF. AbdelaJ. AbdelalimA. AbrahaH.N. AdibM.G. AgrawalS. AlahdabF. AwasthiA. AyeleY. BarbozaM.A. BelachewA.B. BiadgoB. BijaniA. BitewH. CarvalhoF. ChaiahY. DaryaniA. DoH.P. DubeyM. EndriesA.Y.Y. EskandariehS. FaroA. FarzadfarF. FereshtehnejadS-M. FernandesE. FijabiD.O. FilipI. FischerF. GebreA.K. TsadikA.G. GebremichaelT.G. GezaeK.E. Ghasemi-KasmanM. WeldegwergsK.G. DegefaM.G. GnedovskayaE.V. HagosT.B. Haj-MirzaianA. Haj-MirzaianA. HassenH.Y. HayS.I. JakovljevicM. KasaeianA. KassaT.D. KhaderY.S. KhalilI. KhanE.A. KhubchandaniJ. KisaA. KrohnK.J. KulkarniC. NirayoY.L. MackayM.T. MajdanM. MajeedA. ManhertzT. MehndirattaM.M. MekonenT. MelesH.G. MengistuG. MohammedS. NaghaviM. MokdadA.H. MustafaG. IrvaniS.S.N. NguyenL.H. NixonM.R. OgboF.A. OlagunjuA.T. OlagunjuT.O. OwolabiM.O. PhillipsM.R. Pinilla-MonsalveG.D. QorbaniM. RadfarA. RafayA. Rahimi-MovagharV. ReinigN. SachdevP.S. SafariH. SafariS. SafiriS. SahraianM.A. SamyA.M. SarviS. SawhneyM. ShaikhM.A. SharifM. SinghG. SmithM. SzoekeC.E.I. Tabarés-SeisdedosR. TemsahM.H. TemsahO. Tortajada-GirbésM. TranB.X. TsegayA.A.T. UllahI. VenketasubramanianN. WestermanR. WinklerA.S. YimerE.M. YonemotoN. FeiginV.L. VosT. MurrayC.J.L. GBD 2016 Epilepsy Collaborators Global, regional, and national burden of epilepsy, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.Lancet Neurol.201918435737510.1016/S1474‑4422(18)30454‑X30773428
     [Google Scholar]
  118. BenivalD. SalaveS. RanaD. PardheR. BuleP. Unravelling micro and nano vesicular system in intranasal drug delivery for epilepsy.Pharm. Nanotechnol.202210318219310.2174/221173851066622042611534035473543
     [Google Scholar]
  119. AnyanwuC. MotamediG. K. Diagnosis and surgical treatment of drug-resistant epilepsy.Brain Sci.2018844910.3390/brainsci8040049
     [Google Scholar]
  120. AlotaibyT. N. AlshebeiliS. A. AlshawiT. AhmadI. Abd El-SamieF. E. EEG seizure detection and prediction algorithms: A survey.Eurasip J. Adva. Sig. Proc.201418310.1186/1687‑6180‑2014‑183
     [Google Scholar]
  121. AlturkiF.A. AlSharabiK. AbdurraqeebA.M. AljalalM. EEG Signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques.Sensors (Basel)2020209250510.3390/s2009250532354161
     [Google Scholar]
  122. UsmanS.M. UsmanM. FongS. Epileptic seizures prediction using machine learning methods.Comput. Math. Methods Med.20172017907475910.1155/2017/907475929410700
     [Google Scholar]
  123. RamadhaniI. SaputroD. MaryatiN. D. SolihatiS. R. WijayantoI. HadiyosoS. PatmasariR. 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 Machine20191206510.1088/1742‑6596/1201/1/012065
     [Google Scholar]
  124. RajK. RajagopalanS. S. BhardwajS. PandaR. ReddamV. R. ChaitanyaG. RaghavendraK. MundlamuriR. C. ThennarasuK. MajumdarK. K. SatishchandraP. SinhaS. BharathR. D. Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.Seizures20186181310.1016/j.seizure.2018.07.007
     [Google Scholar]
  125. Machine Learning for Seizure Type ClassificationAvailable from: https://www.researchgate.net/publication/330871133_Machine_Learning_for_Seizure_Type_Classification_Setting_the_benchmark (Accessed on: May 12, 2021).
  126. LiuT. TruongN.D. NikpourA. ZhouL. KaveheiO. Epileptic seizure classification with symmetric and hybrid bilinear models.IEEE J. Biomed. Health Inform.202024102844285110.1109/JBHI.2020.298412832248133
     [Google Scholar]
  127. Ahmedt-AristizabalD. NguyenK. DenmanS. SridharanS. DionisioS. FookesC. Deep motion analysis for epileptic seizure classification.Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSInstitute of Electrical and Electronics Engineers Inc.20183578358110.1109/EMBC.2018.8513031
     [Google Scholar]
  128. BaudM.O. KleenJ.K. AnumanchipalliG.K. HamiltonL.S. TanY.L. KnowltonR. ChangE.F. Unsupervised learning of spatiotemporal interictal discharges in focal epilepsy.Neurosurgery201883468369110.1093/neuros/nyx48029040672
     [Google Scholar]
  129. AkterM.S. IslamM.R. IimuraY. SuganoH. FukumoriK. WangD. TanakaT. CichockiA. Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG.Sci. Rep.2020101704410.1038/s41598‑020‑62967‑z32341371
     [Google Scholar]
  130. OkazakiE.M. YaoR. SirvenJ.I. CrepeauA.Z. NoeK.H. DrazkowskiJ.F. HoerthM.T. SalinasE. CsernakL. MehtaN. Usage of EpiFinder clinical decision support in the assessment of epilepsy.Epilepsy Behav.20188214014310.1016/j.yebeh.2018.03.01829625364
     [Google Scholar]
  131. PohM.Z. LoddenkemperT. SwensonN.C. GoyalS. MadsenJ.R. PicardR.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’1020104415441810.1109/IEMBS.2010.5625988
     [Google Scholar]
  132. RukashaT. WoolleyS. I. KyriacouT. CollinsT. Evaluation of wearable electronics for epilepsy: A systematic review.Electronics (Switzerland)20209696810.3390/electronics9060968
     [Google Scholar]
  133. AlkanA. SahinY.G. KarlikB. A novel mobile epilepsy warning system.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Springer Verlag200692292810.1007/11941439_99
     [Google Scholar]
  134. OusleyO. CermakT. Autism spectrum disorder: Defining dimensions and subgroups.Curr. Dev. Disord. Rep.201411202810.1007/s40474‑013‑0003‑125072016
     [Google Scholar]
  135. FarasH. Al AteeqiN. TidmarshL. Autism spectrum disorders.Ann. Saudi Medi.201030429530010.4103/0256‑4947.65261
     [Google Scholar]
  136. AnzulewiczA. SobotaK. Delafield-ButtJ.T. Toward the autism motor signature: Gesture patterns during smart tablet gameplay identify children with autism.Sci. Rep.2016613110710.1038/srep3110727553971
     [Google Scholar]
  137. EkinsS. GerlachJ. ZornK.M. AntonioB.M. LinZ. GerlachA. Repurposing approved drugs as inhibitors of Kv7.1 and Nav1.8 to treat pitt hopkins syndrome.Pharm. Res.201936913710.1007/s11095‑019‑2671‑y31332533
     [Google Scholar]
  138. HeyneH.O. Baez-NietoD. IqbalS. PalmerD.S. BrunklausA. MayP. JohannesenK.M. LauxmannS. LemkeJ.R. MøllerR.S. Pérez-PalmaE. SchollU.I. SyrbeS. LercheH. LalD. CampbellA.J. WangH.R. PanJ. DalyM.J. Epi25 Collaborative Predicting functional effects of missense variants in voltage-gated sodium and calcium channels.Sci. Transl. Med.202012556eaay684810.1126/scitranslmed.aay684832801145
     [Google Scholar]
  139. CummingsJ. LeeG. RitterA. SabbaghM. ZhongK. Alzheimer’s disease drug development pipeline: 2020.Alzheimers Dement. (N. Y.)202061e1205010.1002/trc2.1205032695874
     [Google Scholar]
  140. SwerdlowR.H. Pathogenesis of Alzheimer’s Disease. Clinical interventions in aging.Dove Press2007347359
     [Google Scholar]
  141. SprinzC. ZanonM. AltmayerS. WatteG. IrionK. MarchioriE. HochheggerB. Effects of blood glucose level on 18F fluorodeoxyglucose (18F-FDG) uptake for PET/CT in normal organs: an analysis on 5623 patients.Sci. Rep.201881212610.1038/s41598‑018‑20529‑429391555
     [Google Scholar]
  142. SarikayaI. AlbatinehA.N. SarikayaaA. Effect of various blood glucose levels on regional FDG uptake in the brain.Asia Ocean. J. Nucl. Med. Biol.202081465310.22038/aojnmb.2019.1441832064282
     [Google Scholar]
  143. MosconiL. 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.20053248651010.1007/s00259‑005‑1762‑7
     [Google Scholar]
  144. AnY. VarmaV.R. VarmaS. CasanovaR. DammerE. PletnikovaO. ChiaC.W. EganJ.M. FerrucciL. TroncosoJ. LeveyA.I. LahJ. SeyfriedN.T. Legido-QuigleyC. O’BrienR. ThambisettyM. Evidence for brain glucose dysregulation in Alzheimer’s disease.Alzheimers Dement.201814331832910.1016/j.jalz.2017.09.01129055815
     [Google Scholar]
  145. DingY. SohnJ.H. KawczynskiM.G. TrivediH. HarnishR. JenkinsN.W. LituievD. CopelandT.P. AboianM.S. Mari ApariciC. BehrS.C. FlavellR.R. HuangS.Y. ZalocuskyK.A. NardoL. SeoY. HawkinsR.A. Hernandez PampaloniM. HadleyD. FrancB.L. A deep learning model to predict a diagnosis of alzheimer disease by using 18 F-FDG PET of the Brain.Radiology2019290245646410.1148/radiol.201818095830398430
     [Google Scholar]
  146. DuffyI. R. BoyleA. J. VasdevN. Improving PET imaging acquisition and analysis with machine learning: A narrative review with focus on Alzheimer’s Disease and oncology.Mol. Imaging201918153601211986907010.1177/1536012119869070
     [Google Scholar]
  147. SinghS. SrivastavaA. MiL. ChenK. WangY. CaselliR.J. GoradiaD. ReimanE.M. Deep-learning-based classification of FDG-PET data for alzheimer’s disease categories.Proceedings of SPIE-The Int. Soc. Optical Eng.20178410.1117/12.2294537
     [Google Scholar]
  148. WangL. LiuZ.P. Detecting diagnostic biomarkers of alzheimer’s disease by integrating gene expression data in six brain regions.Front. Genet.201910MAR15710.3389/fgene.2019.0015730915100
     [Google Scholar]
  149. HuoZ. ShenD. HuangH. Genotype-phenotype association study via new multi-task learning model.Pacific Symposium on Biocomputing201835336410.1142/9789813235533_0033
     [Google Scholar]
  150. XuL. LiangG. LiaoC. ChenG.D. ChangC.C. An efficient classifier for alzheimer’s disease genes identification.Molecules20182312314010.3390/molecules2312314030501121
     [Google Scholar]
  151. KongW. MouX. HuX. Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data.BMC Bioinformatics201112S5S710.1186/1471‑2105‑12‑S5‑S721989140
     [Google Scholar]
  152. ScheubertL. LuštrekM. SchmidtR. RepsilberD. FuellenG. Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets.BMC Bioinformatics201213126610.1186/1471‑2105‑13‑26623066814
     [Google Scholar]
  153. MiaoY. JiangH. LiuH. YaoY. An Alzheimers disease related genes identification method based on multiple classifier integration.Comput. Methods Programs Biomed.201715010711510.1016/j.cmpb.2017.08.00628859826
     [Google Scholar]
  154. HanB. ChenX. TalebizadehZ. XuH. Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks.BMC Syst. Biol.20126S3S1410.1186/1752‑0509‑6‑S3‑S1423281790
     [Google Scholar]
  155. ZieselmanA.L. FisherJ.M. HuT. AndrewsP.C. GreeneC.S. ShenL. SaykinA.J. MooreJ.H. Computational genetics analysis of grey matter density in Alzheimer’s disease.BioData Min.2014711710.1186/1756‑0381‑7‑1725165488
     [Google Scholar]
  156. HibarD.P. SteinJ.L. JahanshadN. KohannimO. HuaX. TogaA.W. McMahonK.L. de ZubicarayG.I. MartinN.G. WrightM.J. WeinerM.W. ThompsonP.M. Alzheimer’s Disease Neuroimaging Initiative Genome-wide interaction analysis reveals replicated epistatic effects on brain structure.Neurobiol. Aging201536Suppl. 1S151S15810.1016/j.neurobiolaging.2014.02.03325264344
     [Google Scholar]
  157. RanaD. SalaveS. LongareS. AgarwalR. KaliaK. BenivalD. Nanotherapeutics in tumour microenvironment for cancer therapy.Nanosci. Nanotechnol. Asia2022121e08092119628310.2174/2210681211666210908144839
     [Google Scholar]
  158. Abd-EllahM.K. AwadA.I. KhalafA.A.M. HamedH.F.A. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned.Magn. Reson. Imaging20196130031810.1016/j.mri.2019.05.02831173851
     [Google Scholar]
  159. DandilE. ÇakiroǧluM. EkşiZ. Computer-aided diagnosis of malign and benign brain tumors on MR images.Advances in Intelligent Systems and Computing.Springer Verlag201515716610.1007/978‑3‑319‑09879‑1_16
     [Google Scholar]
  160. MishraR. MRI based brain tumor detection using wavelet packet feature and artificial neural networks.Conf. Proceed.201065665910.1145/1741906.1742054
     [Google Scholar]
  161. TandelG.S. BalestrieriA. JujarayT. KhannaN.N. SabaL. SuriJ.S. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.Comput. Biol. Med.202012210380410.1016/j.compbiomed.2020.10380432658726
     [Google Scholar]
  162. SultanH.H. SalemN.M. Al-AtabanyW. Multi-classification of brain tumor images using deep neural network.IEEE Access20197692156922510.1109/ACCESS.2019.2919122
     [Google Scholar]
  163. ChenC. OuX. WangJ. GuoW. MaX. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors.Front. Oncol.2019980610.3389/fonc.2019.0080631508366
     [Google Scholar]
  164. MohsenH. El-DahshanE.S.A. El-HorbatyE.S.M. SalemA.B.M. Classification using deep learning neural networks for brain tumors.Future Comput. Inform. J.201831687110.1016/j.fcij.2017.12.001
     [Google Scholar]
  165. PollyF.P. ShilS.K. HossainM.A. AymanA. JangY.M. Detection and classification of HGG and LGG brain tumor using machine learning.Int. Conf. Information NetworkingIEEE Computer Society201881381710.1109/ICOIN.2018.8343231
     [Google Scholar]
  166. BadžaM.M. BarjaktarovićM.Č. Classification of brain tumors from mri images using a convolutional neural network.Appl. Sci. (Basel)2020106199910.3390/app10061999
     [Google Scholar]
  167. BadranE.F. MahmoudE.G. HamdyN. An algorithm for detecting brain tumors in MRI images.Proceedings, ICCES’2010 - 2010 International Conference on Computer Engineering and Systems201036837310.1109/ICCES.2010.5674887
     [Google Scholar]
  168. AbdelazizI.S.A. MohammedA. HefnyH. An enhanced deep learning approach for brain cancer MRI images classification using residual networks.Artif. Intell. Med.202010210177910.1016/j.artmed.2019.10177931980109
     [Google Scholar]
  169. ZacharakiE.I. WangS. ChawlaS. Soo YooD. WolfR. MelhemE.R. DavatzikosC. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.Magn. Reson. Med.20096261609161810.1002/mrm.2214719859947
     [Google Scholar]
  170. ChandraS. BhatR. SinghH. A PSO based method for detection of brain tumors from MRI.2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings200966667110.1109/NABIC.2009.5393455
     [Google Scholar]
  171. MarshkoleN. SinghK. ThokeA. S. Texture and shape based classification of brain tumors using linear vector quantization2011302123
     [Google Scholar]
  172. KausM.R. WarfieldS.K. NabaviA. BlackP.M. JoleszF.A. KikinisR. Automated segmentation of MR images of brain tumors.Radiology2001218258659110.1148/radiology.218.2.r01fe4458611161183
     [Google Scholar]
  173. Fletcher-HeathL.M. HallL.O. GoldgofD.B. MurtaghF.R. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images.Artif. Intell. Med.2001211-3436310.1016/S0933‑3657(00)00073‑711154873
     [Google Scholar]
  174. SchmidtM. LevnerI. GreinerR. MurthaA. BistritzA. Segmenting brain tumors using alignment-based features.ICMLA 2005: Fourth International Conference on Machine Learning and Applications200521822010.1109/ICMLA.2005.56
     [Google Scholar]
  175. DasA. BhattacharyaM. 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, IJCBS2009200934835110.1109/IJCBS.2009.129
     [Google Scholar]
  176. NalepaJ. Ribalta LorenzoP. MarcinkiewiczM. Bobek-BillewiczB. WawrzyniakP. WalczakM. KawulokM. DudzikW. KotowskiK. BurdaI. MachuraB. MrukwaG. UlrychP. HayballM.P. Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.Artif. Intell. Med.202010210176910.1016/j.artmed.2019.10176931980106
     [Google Scholar]
  177. ManogaranG. ShakeelP.M. HassaneinA.S. MalarvizhiK.P. ChandraB.G. Machine learning approach-based gamma distribution for brain tumor detection and data sample imbalance analysis.IEEE Access20197121910.1109/ACCESS.2018.2878276
     [Google Scholar]
  178. HashimotoD.A. WitkowskiE. GaoL. MeirelesO. RosmanG. Artificial intelligence in anesthesiology.Anesthesiology2020132237939410.1097/ALN.000000000000296031939856
     [Google Scholar]
  179. KobayashiN. ShigaT. IkumiS. WatanabeK. MurakamiH. YamauchiM. Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.Sci. Rep.2021111522910.1038/s41598‑021‑84714‑833664391
     [Google Scholar]
  180. WangR. WangS. DuanN. WangQ. From patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: Practices and perspectives.Front. Med. (Lausanne)2020714510.3389/fmed.2020.0014532671076
     [Google Scholar]
  181. BenivalD. RanaD. SalaveS. Emerging trends in abuse-deterrent formulations: Technological insights and regulatory considerations.Curr. Drug Deliv.202219884685910.2174/156720181866621120810103534879799
     [Google Scholar]
  182. LoW.L.A. LeiD. LiL. HuangD.F. TongK.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 uhealth2018611e19810.2196/mhealth.812730478019
     [Google Scholar]
  183. AgboolaS. KamdarM. FlanaganC. SearlM. TraegerL. KvedarJ. JethwaniK. Pain management in cancer patients using a mobile app: study design of a randomized controlled trial.JMIR Res. Protoc.201434e7610.2196/resprot.395725500281
     [Google Scholar]
/content/journals/cis/10.2174/2210299X01666230223104508
Loading
Decoding Artificial Intelligence in Neuroscience: Applications Beyond Diagnosis
Curr. Indian Sci. 1, e230223213949 (2023); https://doi.org/10.2174/2210299X01666230223104508
/content/journals/cis/10.2174/2210299X01666230223104508
/content/journals/cis/10.2174/2210299X01666230223104508
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): AI in diagnosis; Artificial intelligence; Machine learning; Neurological disorder; Neurology; Neuroscience

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test