Page 38 - AIH-2-4
P. 38

Artificial Intelligence in Health                                  Early Parkinson’s detection through CNNs



               doi: 10.1016/j.ijmedinf.2016.03.001                doi: 10.1212/wnl.17.5.427
            27.  Prashanth R, Roy SD, Mandal PK, Ghosh S. High-accuracy   37.  LeCun Y, Bengio Y, Hinton G. Deep learning.  Nature.
               classification of Parkinson’s disease through shape analysis   2015;521(7553):436-444.
               and surface fitting in 123I-Ioflupane SPECT imaging. IEEE J
               Biomed Health Inform. 2017;21(2):794-802.          doi: 10.1038/nature14539
               doi: 10.1109/JBHI.2016.2547901                  38.  Cortes C, Vapnik V. Support-vector networks. Mach Learn.
                                                                  1995;20(3):273-297.
            28.  Segovia F,  Gorriz  JM,  Ramirez  J,  Alvarez  I,  Jimenez-
               Hoyuela JM, Ortega SJ. Improved parkinsonism diagnosis      doi: 10.1007/BF00994018
               using a partial least squares based approach.  Med Phys.   39.  Lee  SI,  Lee  H,  Abbeel  P,  Ng  AY.  Efficient  l-1  regularized
               2012;39(7):4395-4403.                              logistic regression. In:  Proceedings of the 21   National
                                                                                                     st
               doi: 10.1118/1.4730289                             Conference on Artificial Intelligence (AAAI). 2006. p. 401-408.
            29.  Illan IA, Gorrz JM, Ramirez J, Segovia F, Jimenez-Hoyuela JM,   40.  Bergstra JS, Bardenet R, Bengio Y, Kégl B. Algorithms for
               Ortega Lozano SJ. Automatic assistance to Parkinson’s disease   hyper-parameter optimization. In:  Advances in Neural
               diagnosis in DaTSCAN SPECT imaging.  Med Phys.     Information Processing Systems 24. 2011. p. 2546-2554.
               2012;39(10):5971-5980.
                                                               41.  Srivastava N, Hinton G, Krizhevsky A, Sutskever  I,
               doi: 10.1118/1.4742055                             Salakhutdinov  R.  Dropout:  A  simple  way  to  prevent
            30.  Rojas A, Górriz JM, Ramírez J,  et  al. Application of   neural networks from overfitting.  J  Mach Learn Res.
               empirical  mode  decomposition  (EMD)  on  DaTSCAN   2014;15(1):1929-1958.
               SPECT images to explore Parkinson disease.  Expert Syst   42.  Perez L, Wang J. The Effectiveness of Data Augmentation in
               Appl. 2013;40(7):2756-2766.
                                                                  Image Classification using Deep Learning. [arXiv Preprint];
               doi: 10.1016/j.eswa.2012.11.017                    2017.
            31.  Towey DJ, Bain PG, Nijran KS. Automatic classification   43.  Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative
               of 123I-FP-CIT (DaTSCAN) SPECT images.  Nucl Med   adversarial nets. In:  Advances in Neural Information
               Commun. 2011;32(8):699-707.                        Processing Systems 27. [Preprint]; 2014. p. 2672-2680
               doi: 10.1097/MNM.0b013e328347cd09               44.  Muller R, Kornblith S, Hinton GE. When does label
            32.  Huertas-Fernandez I, Garcia-Gomez F, Garcia-Solis D,   smoothing help? In:  Advances in Neural Information
               et al. Machine learning models for the differential diagnosis   Processing Systems 32. 2019. p. 4696-4705.
               of vascular parkinsonism and Parkinson’s disease using   45.  Shiiba T, Takano K, Takaki A, Suwazono S. Dopamine
               [(123) I] FP-CIT SPECT.  Eur J Nucl Med Mol Imaging.   transporter single-photon emission computed tomography-
               2015;42(1):112-119.
                                                                  derived radiomics signature for detecting Parkinson’s
               doi: 10.1007/s00259-014-2882-8                     disease. EJNMMI Res. 2022;12(1):39.
            33.  Oliveira FP, Faria DB, Costa DC, Castelo-Branco M,      doi: 10.1186/s13550-022-00910-1
               Tavares JMR. Extraction, selection and comparison of
               features for an effective automated computer-aided diagnosis   46.  Tufail AB, Ma YK, Zhang QN, et al. 3D convolutional neural
               of Parkinson’s disease based on [ I] FP-CIT SPECT images.   networks-based multiclass classification of Alzheimer’s and
                                      123
               Eur J Nucl Med Mol Imaging. 2018;45(6):1052-1062.  Parkinson’s diseases using PET and SPECT neuroimaging
                                                                  modalities. Brain Inform. 2021;8(1):23.
               doi: 10.1007/s00259-017-3918-7
                                                                  doi: 10.1186/s40708-021-00144-2
            34.  Zhang X, Chou J, Liang J, et al. Data-driven subtyping of
               Parkinson’s disease using longitudinal clinical records: A   47.  Majhi B, Kashyap A, Mohanty SS, et al. An improved method
               cohort study. Sci Rep. 2019;9(1):797.              for diagnosis of Parkinson’s disease using deep learning
               doi: 10.1038/s41598-018-37545-z                    models enhanced with metaheuristic algorithm. BMC Med
                                                                  Imaging. 2024;24(1):156.
            35.  Marek K, Chowdhury S, Siderowf A, et al. The Parkinson’s
               progression markers initiative (PPMI)  -  establishing a      doi: 10.1186/s12880-024-01335-z
               PD  biomarker  cohort.  Ann Clin Transl Neurol.  2018;   48.  Khachnaoui H, Chikhaoui B, Khlifa N, Mabrouk R. Enhanced
               5(12):1460-1477.                                   Parkinson’s disease diagnosis through convolutional neural
               doi: 10.1002/acn3.644                              network models applied to spect datscan images.  IEEE
                                                                  Access. 2023;11:91157-91172.
            36.  Hoehn MM, Yahr MD. Parkinsonism: Onset, progression
               and mortality. Neurology. 1967;17(5):427-442.      doi: 10.1109/ACCESS.2023.3308075



            Volume 2 Issue 4 (2025)                         32                          doi: 10.36922/AIH025040005
   33   34   35   36   37   38   39   40   41   42   43