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Cited by in F6Publishing
For: 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]
Number Citing Articles
1 Helaly HA, Badawy M, Haikal AY. Deep Learning Approach for Early Detection of Alzheimer's Disease. Cognit Comput 2021;:1-17. [PMID: 34745371 DOI: 10.1007/s12559-021-09946-2] [Reference Citation Analysis]
2 Silva RDCD, Jenkyn TR, Carranza VA. Enhanced Pre-Processing for Deep Learning in MRI Whole Brain Segmentation using Orthogonal Moments. Brain Multiphysics 2022. [DOI: 10.1016/j.brain.2022.100049] [Reference Citation Analysis]
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4 Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC, Soar J, Palmer EE, Acharya UR. Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021). Sensors (Basel) 2021;21:7034. [PMID: 34770340 DOI: 10.3390/s21217034] [Reference Citation Analysis]
5 Lim MJR. Letter: Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021;89:E333-4. [PMID: 34498686 DOI: 10.1093/neuros/nyab337] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Das S, Nayak G, Saba L, Kalra M, Suri JS, Saxena S. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105273] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
7 Sun J, Yuan X, M.a B. Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis. Journal of Healthcare Engineering 2022;2022:1-10. [DOI: 10.1155/2022/7247549] [Reference Citation Analysis]
8 Rahman Z, Pasam T, Kr R, Dandekar MP. Binary Classification Model of Machine Learning Detected Altered Gut Integrity in Controlled-Cortical Impact Model of Traumatic Brain Injury. Int J Neurosci 2022;:1-14. [PMID: 35758006 DOI: 10.1080/00207454.2022.2095271] [Reference Citation Analysis]
9 Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel) 2021;11:1402. [PMID: 34441336 DOI: 10.3390/diagnostics11081402] [Reference Citation Analysis]
10 Mládek A, Gerla V, Skalický P, Vlasák A, Zazay A, Lhotská L, Beneš V Sr, Beneš V Jr, Bradáč O. Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach. Neurosurgery 2022. [PMID: 35080523 DOI: 10.1227/NEU.0000000000001838] [Reference Citation Analysis]
11 Kim KH, Koo H, Lee B, Yoon S, Sohn M. Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning. J Korean Phys Soc 2021;79:321-7. [DOI: 10.1007/s40042-021-00202-2] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Afrasiabi A, Keane JT, Heng JI, Palmer EE, Lovell NH, Alinejad-Rokny H. Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 2021:BST20200732. [PMID: 34282824 DOI: 10.1042/BST20200732] [Reference Citation Analysis]