Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease

[EN] The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.This work was financially supported by INBIO 2019 (DEEPBRAIN), INNVA1/2020/83(DEEPPET) funded by Generalitat Valenciana, and PID2019-107790RB-C22 funded by MCIN/AEI/10.13039/501100011033/. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Prats-Climent, J.; Gandia-Ferrero, MT.; Torres-Espallardo, I.; Álvarez-Sanchez, L.; Martinez-Sanchis, B.; Cháfer-Pericás, C.; Gómez-Rico, I.... (2022). Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease. Journal of Medical Systems. 46(8):1-13. https://doi.org/10.1007/s10916-022-01836-w11346

Artificial intelligence techniques support nuclear medicine modalities to improve the diagnosis of Parkinson's disease and Parkinsonian syndromes

Abstract Purpose The aim of this review is to discuss the most significant contributions about the role of Artificial Intelligence (AI) techniques to support the diagnosis of movement disorders through nuclear medicine modalities. Methods The work is based on a selection of papers available on PubMed, Scopus and Web of Sciences. Articles not written in English were not considered in this study. Results Many papers are available concerning the increasing contribution of machine learning techniques to classify Parkinson's disease (PD), Parkinsonian syndromes and Essential Tremor (ET) using data derived from brain SPECT with dopamine transporter radiopharmaceuticals. Other papers investigate by AI techniques data obtained by 123I-MIBG myocardial scintigraphy to differentially diagnose PD and other Parkinsonian syndromes. Conclusion The recent literature provides strong evidence that AI techniques can play a fundamental role in the diagnosis of movement disorders by means of nuclear medicine modalities, therefore paving the way towards personalized medicine

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Background and Objectives 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. Methods We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. Results The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). Conclusion In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Aprendizagem profunda para o diagnóstico da doença de Alzheimer com neuroimagem 18F-FDG PET

Neurodegenerative disease is the term used for a range of incurable and debilitating conditions affecting the human's nervous system. Amongst these conditions, Alzheimer's Disease (AD) is responsible for the greatest burden both for the number of people affected and for the high costs in medical care. The challenges of the disease are related to the subtle symptoms, the increasing pace of disability and the long period of time over which patients will require special care. Recent research efforts have been dedicated to the development of computational tools that can be integrated into the workflow of doctors as a complement to support early diagnosis and targeted treatments. This dissertation aims to study the application of Deep Learning (DL) techniques for the automated classification of AD. The study focuses on the role of PET neuroimaging as a biomarker of neurodegenerative diseases, namely in classifying healthy versus AD patients. PET images of the cerebral metabolism of glucose with fluorine 18 (18F) fluorodeoxyglucose (18F FGD) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The pre-processed dataset is used to train two Convolutional Neural Networks (CNNs). The first CNN architecture aims to explore transfer learning as a promising solution to the data challenge by using a 2D Inception V3 model, from Google, previously trained on a large dataset. This approach requires a preprocessing step in which the PET volumetric data is converted into a two-dimensional input image which is the input to the pre-trained model. The second approach involves a custom 3D-CNN to take advantage of spatial patterns on the full PET volumes by using 3D filters and 3D pooling layers. The comparative study highlights the performance and robustness of these two models in dealing with the limited availability of the labelled data. The performance of the estimators is evaluated through a cross-validation procedure, giving a score of 83.62% for the 2D-CNN and 86.80% for the 3D-CNN. The results achieved contribute to the understanding of the effectiveness of these methods in the diagnosis of AD. Given the expected margin for improvements, they can be considered promising and in line with the current state of the art.Doença neurodegenerativa é um termo utilizado para uma série de condições incuráveis e debilitantes que afetam o sistema nervoso humano. Destas condições, a doença de Alzheimer (DA) é a mais preocupante, tanto pelo número de pessoas afetadas como pelos elevados custos em tratamento medico. Os principais desafios associados a esta doença estão relacionados com os sintomas subtis, o rápido desenvolvimento de incapacidade e ao longo período de tempo durante o qual os pacientes necessitarão de cuidados especiais. Pesquisas recentes têm sido dedicadas ao desenvolvimento de ferramentas computacionais capazes de ser integradas nos procedimentos médicos como complemento para apoiar o diagnóstico precoce e tratamentos adequados. Esta dissertação procura estudar a aplicação de técnicas de aprendizagem profunda (AP) na classificação automatizada da DA. Este estudo tem como foco principal o papel da neuroimagem PET como biomarcador de doenças neurodegenerativas, especialmente na classificação de pacientes saudáveis em comparação com pacientes com DA. Imagens PET do metabolismo cerebral de glucose com flúor-18 (18F) fluorodesoxiglucose (18F FGD) foram obtidas através da base de dados da Alzheimer's Dissesse Neuroimaging Initiative (ADNI). O dataset pré-processado é usado para treinar duas redes neurais convulsionais (RNCs). A arquitetura da primeira RNC procura explorar a transferência de aprendizagem como uma solução promissora para o problema dos dados através da utilização de um modelo Inception V3 2D, da Google, previamente treinado num dataset maior. Esta abordagem requer um passo de pré -processamento onde dados volumétricos PET são convertidos numa imagem bidimensional que por sua vez será os dados de entrada do modelo pré-treinado. A segunda abordagem involve uma RNC 3D personalizada de maneira a utilizar os padrões espaciais presentes nos volumes PET através de filtros 3D e camadas de pooling 3D. O estudo comparativo foca-se no desempenho e robustez dos dois modelos ao lidar com a disponibilidade limitada de dados classificados. O desempenho dos classificadores é avaliado através de um processo de validação cruzada, atribuindo uma pontuação de 83.62% à RNC 2D e de 86.80% à RNC 3D. Os resultados obtidos contribuem para análise da eficácia destes métodos no diagnóstico da DA. Tendo em conta as melhorias expectáveis, estas poderam ser consideradas abordagens promissoras e de acordo com o atual estado da arte.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones