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For: Valliani AA, Ranti D, Oermann EK. Deep Learning and Neurology: A Systematic Review. Neurol Ther 2019;8:351-65. [PMID: 31435868 DOI: 10.1007/s40120-019-00153-8] [Cited by in Crossref: 28] [Cited by in F6Publishing: 18] [Article Influence: 9.3] [Reference Citation Analysis]
Number Citing Articles
1 Reena MR, Ameer PM. A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach. Comput Biol Med 2022;145:105463. [PMID: 35421794 DOI: 10.1016/j.compbiomed.2022.105463] [Reference Citation Analysis]
2 Murphree DH, Puri P, Shamim H, Bezalel SA, Drage LA, Wang M, Pittelkow MR, Carter RE, Davis MDP, Bridges AG, Mangold AR, Yiannias JA, Tollefson MM, Lehman JS, Meves A, Otley CC, Sokumbi O, Hall MR, Comfere N. Deep learning for dermatologists: Part I. Fundamental concepts. J Am Acad Dermatol 2020:S0190-9622(20)30921-X. [PMID: 32434009 DOI: 10.1016/j.jaad.2020.05.056] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
3 Simpson S, Chen Y, Wellmeyer E, Smith LC, Aragon Montes B, George O, Kimbrough A. The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci 2021;15:595507. [PMID: 33967705 DOI: 10.3389/fnsys.2021.595507] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Philippsen A, Nagai Y. Deficits in Prediction Ability Trigger Asymmetries in Behavior and Internal Representation. Front Psychiatry 2020;11:564415. [PMID: 33329104 DOI: 10.3389/fpsyt.2020.564415] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021;11:1473. [PMID: 34441407 DOI: 10.3390/diagnostics11081473] [Reference Citation Analysis]
6 Dzianok P, Antonova I, Wojciechowski J, Dreszer J, Kublik E. The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults. Gigascience 2022;11:giac015. [PMID: 35254424 DOI: 10.1093/gigascience/giac015] [Reference Citation Analysis]
7 Puri P, Comfere N, Drage LA, Shamim H, Bezalel SA, Pittelkow MR, Davis MDP, Wang M, Mangold AR, Tollefson MM, Lehman JS, Meves A, Yiannias JA, Otley CC, Carter RE, Sokumbi O, Hall MR, Bridges AG, Murphree DH. Deep learning for dermatologists: Part II. Current applications. J Am Acad Dermatol 2020:S0190-9622(20)30918-X. [PMID: 32428608 DOI: 10.1016/j.jaad.2020.05.053] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
8 Smolyansky ED, Hakeem H, Ge Z, Chen Z, Kwan P. Machine learning models for decision support in epilepsy management: A critical review. Epilepsy Behav 2021;123:108273. [PMID: 34507093 DOI: 10.1016/j.yebeh.2021.108273] [Reference Citation Analysis]
9 Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2021;38:527-38. [PMID: 35136337 DOI: 10.12788/fp.0174] [Reference Citation Analysis]
10 Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020;4:041503. [PMID: 33094213 DOI: 10.1063/5.0011697] [Cited by in Crossref: 9] [Cited by in F6Publishing: 2] [Article Influence: 4.5] [Reference Citation Analysis]
11 di Biase L, Di Santo A, Caminiti ML, De Liso A, Shah SA, Ricci L, Di Lazzaro V. Gait Analysis in Parkinson's Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring. Sensors (Basel) 2020;20:E3529. [PMID: 32580330 DOI: 10.3390/s20123529] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
12 Saeidi M, Karwowski W, Farahani FV, Fiok K, Taiar R, Hancock PA, Al-Juaid A. Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sci 2021;11:1525. [PMID: 34827524 DOI: 10.3390/brainsci11111525] [Reference Citation Analysis]
13 Girard MJA, Schmetterer L. Artificial intelligence and deep learning in glaucoma: Current state and future prospects. Prog Brain Res 2020;257:37-64. [PMID: 32988472 DOI: 10.1016/bs.pbr.2020.07.002] [Reference Citation Analysis]
14 Ni YC, Tseng FP, Pai MC, Hsiao IT, Lin KJ, Lin ZK, Lin WB, Chiu PY, Hung GU, Chang CC, Chang YT, Chuang KS; Alzheimer’s Disease Neuroimaging Initiative. Detection of Alzheimer's disease using ECD SPECT images by transfer learning from FDG PET. Ann Nucl Med 2021;35:889-99. [PMID: 34076857 DOI: 10.1007/s12149-021-01626-3] [Reference Citation Analysis]
15 Cochen De Cock V, Dotov D, Lacombe S, Picot MC, Galtier F, Driss V, Giovanni C, Geny C, Abril B, Damm L, Janaqi S. Classifying Idiopathic Rapid Eye Movement Sleep Behavior Disorder, Controls, and Mild Parkinson's Disease Using Gait Parameters. Mov Disord 2022. [PMID: 35040193 DOI: 10.1002/mds.28894] [Reference Citation Analysis]
16 Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimed Tools Appl 2021;:1-34. [PMID: 33841033 DOI: 10.1007/s11042-021-10707-4] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Choi JH, Kim HA, Kim W, Lim I, Lee I, Byun BH, Noh WC, Seong MK, Lee SS, Kim BI, Choi CW, Lim SM, Woo SK. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep 2020;10:21149. [PMID: 33273490 DOI: 10.1038/s41598-020-77875-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
18 Attallah O, Ragab DA, Sharkas M. MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks. PeerJ 2020;8:e10086. [PMID: 33062453 DOI: 10.7717/peerj.10086] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
19 Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Giardina E, Cascella R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. J Pers Med 2021;11:280. [PMID: 33917161 DOI: 10.3390/jpm11040280] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Ienca M, Ignatiadis K. Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges. AJOB Neurosci 2020;11:77-87. [PMID: 32228387 DOI: 10.1080/21507740.2020.1740352] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
21 Sargolzaei S. Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children. Front Comput Neurosci 2021;15:670489. [PMID: 34025380 DOI: 10.3389/fncom.2021.670489] [Reference Citation Analysis]
22 Ni YC, Tseng FP, Pai MC, Hsiao IT, Lin KJ, Lin ZK, Lin CY, Chiu PY, Hung GU, Chang CC, Chang YT, Chuang KS, Alzheimer's Disease Neuroimaging Initiative. The Feasibility of Differentiating Lewy Body Dementia and Alzheimer's Disease by Deep Learning Using ECD SPECT Images. Diagnostics (Basel) 2021;11:2091. [PMID: 34829438 DOI: 10.3390/diagnostics11112091] [Reference Citation Analysis]
23 English M, Kumar C, Ditterline BL, Drazin D, Dietz N. Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia. Acta Neurochir Suppl 2022;134:349-61. [PMID: 34862559 DOI: 10.1007/978-3-030-85292-4_39] [Reference Citation Analysis]
24 Jiang H, Li X, Safara F. IoT-based Agriculture: Deep Learning in Detecting Apple Fruit Diseases. Microprocessors and Microsystems 2021. [DOI: 10.1016/j.micpro.2021.104321] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
25 An S, Kang C, Lee HW. Artificial Intelligence and Computational Approaches for Epilepsy. J Epilepsy Res 2020;10:8-17. [PMID: 32983950 DOI: 10.14581/jer.20003] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
26 Lemay A, Gros C, Zhuo Z, Zhang J, Duan Y, Cohen-Adad J, Liu Y. Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning. Neuroimage Clin 2021;31:102766. [PMID: 34352654 DOI: 10.1016/j.nicl.2021.102766] [Reference Citation Analysis]