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Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Review Article

Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases

Author(s): Silvia Cascianelli, Michele Scialpi, Serena Amici, Nevio Forini, Matteo Minestrini, Mario Luca Fravolini, Helmut Sinzinger, Orazio Schillaci and Barbara Palumbo

Volume 14, Issue 2, 2017

Page: [198 - 207] Pages: 10

DOI: 10.2174/1567205013666160620122926

Price: $65

Abstract

Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer’s Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis – PCA – or Partial Least Squares – PLS – are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

Keywords: Alzheimer’s Disease, computer aided diagnosis, dementia, machine learning, molecular imaging, Parkinson’s Disease, PET, SPECT.

[1]
Bishop CM. Pattern Recognition and Machine Learning (Information Science and Statistics) New York: Springer-Verlag. (2006)
[2]
Alpaydin E. Introduction to Machine Learning (Adaptive Computation and Machine Learning) USA The MIT Press. (2004)
[3]
Haller S, Lovblad KO, Giannakopoulos P, Van De Ville D. Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends. Brain Topogr 27(3): 329-37. (2014)
[4]
Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4-5): 198-211. (2007)
[5]
Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med 41(6): 449-62. (2011)
[6]
Palumbo B, Fravolini ML. To what extent can artificial neural network support nuclear medicine? Hell J Nucl Med 15(3): 180-3. (2012)
[7]
Preis O, Blake MA, Scott JA. Neural network evaluation of PET scans of the liver: a potentially useful adjunct in clinical interpretation. Radiology 258(3): 714-21. (2011)
[8]
Lemm S, Blankertz B, Dickhaus T, Müller KR. Introduction to machine learning for brain imaging. Neuroimage 56(2): 387-99. (2011)
[9]
Sayeed A, Petrou M, Spyrou N, Kadyrov A, Spinks T. Diagnostic features of Alzheimer’s disease extracted from PET sinograms. Phys Med Biol 47(1): 137. (2002)
[10]
Udomhunsakul S, Wongsit P. Feature extraction in medical MRI images. Cybernet Intell Syst 2004 IEEE Conf 1 340-4. (2004)
[11]
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4): 441-6. (2012)
[12]
Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst 2(1): 37-52. (1987)
[13]
Chu C, Hsu AL, Chou KH, Bandettini P, Lin C. Alzheimer’s Disease Neuroimaging Initiative. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60(1): 59-70. (2012)
[14]
Ayhan MS, Benton RG, Raghavan VV, Choubey S. Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer’s disease. Int J Data Min Bioinform 7(2): 146-65. (2013)
[15]
Padilla P, López M, Górriz JM, Ramírez J, Salas-Gonzalez D. NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Trans Med Imaging 31(2): 207-16. (2012)
[16]
Ramírez J, Górriz JM, Salas-Gonzalez D, Romero A, López M, Álvarez I, et al. Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Inf Sci 237: 59-72. (2013)
[17]
Park H, Yang JJ, Seo J, Lee JM. Dimensionality reduced cortical features and their use in predicting longitudinal changes in Alzheimer’s disease. Neurosci Lett 550: 17-22. (2013)
[18]
Liu F, Wee CY, Chen H, Shen D. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s Disease and mild cognitive impairment identification. Neuroimage 84: 466-75. (2014)
[19]
Ramírez J, Górriz JM, Segovia F, Chaves R, Salas-Gonzalez D, López M, et al. Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neurosci Lett 472(2): 99-103. (2010)
[20]
Wold H. Estimation of principal components and related models by iterative least squares. In: Krishnaiaah PR, Ed. Multivariate Analysis New York: Academic Press. 391-420. (1966)
[21]
Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Eds. Statistical parametric mapping: the analysis of functional brain images: the analysis of functional brain images. USA Academic Press. (2011)
[22]
Segovia F, Bastin C, Salmon E, Górriz JM, Ramírez J, Phillips C. Combining pet images and neuropsychological test data for automatic diagnosis of Alzheimer’s disease. PLoS One 9(2)e88687 (2014)
[23]
Fravolini ML, Campa G. Design of a neural network adaptive controller via a constrained invariant ellipsoids technique. IEEE Trans Neural Netw 22(4): 627-38. (2011)
[24]
Dawson MRW, Dobbs A, Hooper HR, McEwan AJ, Triscott J, Cooney J. Artificial neural networks that use single-photon emission tomography to identify patients with probable Alzheimer’s disease. Eur J Nucl Med 21: 1303-11. (1994)
[25]
Beale MH, Hagan MT, Demuth HN. The MathWorks Inc., Neural Networks Toolbox Users’s Guide 2009. www.mathworks.com/ help/pdf_doc/nnet/nnet_ug.pdf
[26]
Page MPA, Howard RJ, O’Brien JT, Buxton-Thomas MS, Pickering AD. Use of Neural Networks in Brain SPECT to Diagnose Alzheimer’s Disease. J NucI Med 37: 195-200. (1996)
[27]
Hamilton D, O’Mahony D, Coffey J, Murphy J, O’Hare N, Freyne P, et al. Classification of mild Alzheimer’s disease by artificial neural network analysis of SPET data. Nucl Med Commun 18(9): 805-10. (1997)
[28]
Hamilton D, List A, Butler T, Hogg S, Cawley M. Discrimination between parkinsonian syndrome and essential tremor using artificial neural network classification of quantified DaTSCAN data. Nucl Med Commun 27(12): 939-44. (2006)
[29]
Palumbo B, Fravolini ML, Nuvoli S, Spanu A, Paulus KS, Schillaci O, et al. Comparison of two neural network classifiers in the differential diagnosis of Essential tremor and Parkinson’s disease by 123I-FP-CIT brain SPET. Eur J Nucl Med Mol Imaging 37: 2146-53. (2010)
[30]
Chang CC, Lin CJ. LIBSVM: A library for support vector machines. ACM Trans on Intelligent Systems and Technol 2(3): 27.(2001); Software available at. http://www.csie.ntu. edu.tw/~cjlin/libsvm
[31]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 20(3): 273-97. (1995)
[32]
Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. Neural Netw IEEE Transet 13(2): 415-25. (2002)
[33]
Pagani M, De Carli F, Morbelli S, Öberg J, Chincarini A, Frisoni GB, et al. Volume of interest-based [18 F] fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls. A European Alzheimer’s Disease Consortium (EADC) study. Neuroimage Clin 7: 34-42. (2015)
[34]
Salas-Gonzalez D, Górriz JM, Ramírez J, López M, Illan IA, Segovia F, et al. Analysis of SPECT brain images for the diagnosis of Alzheimer’s disease using moments and support vector machines. Neurosci Lett 461(1): 60-4. (2009)
[35]
Vandenberghe R, Nelissen N, Salmon E, Ivanoiu A, Hasselbalch S, Andersen A, et al. Binary classification of 18 F-flutemetamol PET using machine learning: Comparison with visual reads and structural MRI. Neuroimage 64: 517-25. (2013)
[36]
Palumbo B, Fravolini ML, Buresta T, Pompili F, Forini N, Nigro P, et al. Diagnostic accuracy of Parkinson disease by support vector machine (SVM) analysis of 123I-FP-CIT brain SPECT data: implications of putaminal findings and age. Medicine (Baltimore) 93(27)e228 (2014)
[37]
Loh WY. Classification and regression trees. Wiley Interdiscip Rev Data Min Knowl Discov 1(1): 14-23. (2011)
[38]
Salas-Gonzalez D, Górriz JM, Ramírez J, López M, Álvarez I, Segovia F, et al. Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Phys Med Biol 55(10): 2807. (2010)
[39]
Criminisi A, Shotton J, Konukoglu E. Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. The Netherlands: Now Publishers. (2012)
[40]
Friedman J, Hastie T, Tibshirani R. The elements of statistical learning Vol 1. Berlin Springer Series in Statistics. (2001)
[41]
Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. USA CRC Press. (1984)
[42]
Breiman L. Random forests. Mach Learn 45(1): 5-32. (2001)
[43]
Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D. Alzheimer’s Disease Neuroimaging Initiative. Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. Neuroimage 65: 167-75. (2013)

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