Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimers disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The time orientation and 3-word recall score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752)

Avaliação do potencial de técnicas de machine learning no diagnóstico diferencial da doença de Parkinson com base em imagem molecular

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia Biomédica.A doença de Parkinson (DP) é uma doença neurodegenerativa que resulta da perda de neurónios dopaminérgicos na substância negra. É um grave problema de saúde pública que afeta 1-1,5% da população idosa a nível mundial. A perda dos neurónios dopaminérgicos devido à DP é um processo lento e que, de uma forma geral, pode demorar mais de uma década até que os primeiros sintomas sejam detetáveis, realçando a importância de um diagnóstico precoce para iniciar a terapêutica mais adequada o mais rapidamente possível [1]. O diagnóstico da DP é baseado na observação de sinais clínicos, nomeadamente a caracterização de uma variedade de sintomas motores, a resposta aos fármacos dopaminérgicos e a avaliação do padrão de captação (imagens) de radiofármacos específicos para avaliar a integridade do sistema dopaminérgico, usando equipamentos de SPECT (do inglês single-photon emission computed tomography) ou PET (do inglês positron emission tomography) [2]. Em grande parte dos casos, a avaliação visual destas imagens é suficiente para a caracterização do sistema dopaminérgico. No entanto, noutros casos, esta avaliação tem de ser complementada com uma análise quantitativa. Mesmo assim, por vezes ainda surgem dúvidas, que podem ser clarificadas com a utilização de técnicas de classificação baseadas em machinelearning [3]. As redes neuronais convolucionais (CNN, do inglês convolutional neural network) têm vindo a mostrar potencial na classificação de diversos tipos de imagens médicas, especialmente na área da oncologia [4],[5],[6] mas também existem exemplos de aplicação na área da neuroimagem [7],[8],[9]. Deste modo, pretendeu-se com este estudo avaliar o potencial das CNN, em comparação com outras técnicas muito populares, no diagnóstico diferencial da DP com base em imagens moleculares do cérebro obtidas com [123I] FP-CIT SPECT. Este trabalho incluiu um conjunto de 806 imagens cerebrais volumétricas obtidas com [123I]FP-CIT SPECT (208 controlos saudáveis e 598 doentes com DP). Os dados foram obtidos a partir da base de dados da Parkinson's Progression Markers Initiative (PPMI) (www.ppmi-info.org/data). Para cada sujeito, apenas foi considerado o primeiro exame [123I]FP-CIT SPECT (baseline ou screening). O protocolo de aquisição e pré-processamento de imagens encontra-se disponível em http://www.ppmi- info.org/study-design/research-documents-and-sops/. A técnica de classificação baseada em CNN foi comparada com os classificadores: k-vizinhos mais próximos (kNN, do inglês k-nearest neighbor), regressão logística (RL), árvores de decisão (AD), support vector machine (SVM) e redes neuronais artificiais (ANN, do inglês artificial neural networks). O classificador baseado em CNN foi treinado com imagens bidimensionais (dimensões: 88 mm × 82 mm) contendo a região do estriado, nomeadamente a projeção de intensidade máxima superior-inferior da cabeça. Os restantes classificadores foram treinados com cinco características extraídas da região do estriado tridimensional: potencial de ligação do caudato, potencial de ligação do putamen, rácio putamen para caudato, volume da região do estriado com "captação normal" e comprimento do eixo maior dessa região. Foram utilizados apenas os valores mínimos inter-hemisférios cerebral. Os dados foram divididos na razão 75:25 (75% para treino e 25% para teste). Cada uma das cinco características foi também estudada individualmente para avaliar o seu potencial de classificação em termos de desempenho (precisão, sensibilidade e especificidade). No conjunto de dados do teste, a precisão, sensibilidade, e especificidade da CNN para diferenciar imagens de doentes com DP das imagens de controlos saudáveis foi 96%, 98%, e 91%, respetivamente. Estes resultados foram muito semelhantes aos obtidos com os outros classificadores (kNN: 95%, 99%, 85%; RL: 94%, 97%, 86%; AD: 94%, 97%, 84%; SVM: 94%, 98%, 88%; e ANN: 94%, 97%, 86%). II. As diferenças de precisão não são estatisticamente significativas (teste Q de Cochran, p = 0,592). Individualmente, a característica que melhor diferenciou as imagens de doentes com DP das imagens dos controlos saudáveis foi o potencial de ligação do putamen com 93% de precisão, 93% de sensibilidade e 94% de especificidade no conjunto de dados do teste, usando o valor de corte que maximizou o coeficiente de Younden obtido do conjunto de dados de treino (valor de corte de 1,716). O classificador baseado em CNN provou ser tão robusto e preciso como os outros classificadores utilizados neste trabalho, com a vantagem de utilizar imagens como entrada direta, minimizando os passos iniciais de pré-processamento. Todos os classificadores aqui utilizados atingiram valores de precisão de classificação superiores aos frequentemente reportados na literatura para avaliação visual qualitativa. Assim, sugere-se a sua utilização como complemento à avaliação visual qualitativa e como ferramenta de treino para médicos especialista com reduzida experiência.Parkinson's disease (PD) is a neurodegenerative disease that results from the loss of dopaminergic neurons in the substantia nigra. It is a serious public health problem that affects 1 to 1.5% of the elderly population worldwide. The loss of dopaminergic neurons is a slow process that takes decades to happen, highlighting the importance of an early diagnosis to start the most adequate therapeutic regimen as soon as possible [1]. The diagnosis of PD is based on the observation of clinical signs, namely the characterization of a variety of motor symptoms, the response to dopaminergic drugs and evaluation of the uptake pattern (images) of specific radiopharmaceuticals to assess the integrity of the dopaminergic system [2]. In most cases, a visual assessment of these images is sufficient to characterize the dopaminergic system. However, in other cases this assessment must be complemented with a quantitative analysis. Even so, sometimes doubts still arise, which can be clarified with the use of classification techniques based on artificial intelligence, being machine learning the most frequently used [3]. In the context of artificial intelligence, convolutional neural networks (CNN) have been showing potential in various types of medical images, especially in the field of oncology [4],[5],[6], but there are also examples of application in the field of neuroimaging [7],[8],[9]. Thus, the aim of this study is to evaluatethe potential of CNN, in comparison to other popular techniques, in the differential diagnosis of PD based on [123I]FP-CIT SPECT images of the central nervous system, in particular the basal ganglia. This work included 806 [123I]FP-CIT SPECT brain images (208 health controls and 598 with PD). Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi- info.org/data). For each subject, only the first scan [123I]FP-CIT SPECT was considered (baseline or screening). The protocol of image acquisition and pre-processing is available at http://www.ppmi- info.org/study-design/research-documents-and-sops/. CNN was compared against k-nearest neighbour (kNN), logistic regression (LR), decision trees (DT), support vector machines (SVM) and artificial neural networks (ANN) classifiers. The CNN classifier was trained with 2-dimensional image patches (dimensions: 88 mm × 82 mm) containing the striatal region, extracted from the head superior-inferior maximum intensity projection. The remaining classifiers were trained with five features extracted from 3-dimensional striatal region: caudate binding potential, putamen binding potential, putamen to caudate ratio, volume of the striatal region with “normal uptake”, and the length of major axis of that region. Only the inter-hemisphere minimum was used. The split ratio of the dataset was 75:25 (75% for training and 25% for testing). Each of the five features was also considered individually to assess its potential for classification in terms of performance (accuracy, sensitivity, and specificity). In the test dataset, accuracy, sensitivity, and specificity of the CNN were 96%, 98%, and 91%, respectively. This finding was very similar to what we obtained with the other classifiers (kNN: 95%, 99%, 85%; LR: 94%, 97%, 86%, DT: 94%, 97%, 84%, SVM: 94%, 98%, 88% and ANN: 94%, 97%, 86%). The accuracy differences were not statistically significant (Cochran Q test, p = 0.592). Individually, the feature that best differentiated PD from normal scans was the putamen binding potential with 93% accuracy, 93% sensitivity and 94% specificity in the test dataset, based on the optimal cut-off (1.716) that maximizes Younden’s coefficient in the training dataset. IV CNN classifier proved to be as robust and accurate as the other classifiers frequently used in the type of problems, with the great advantage of using images as direct input. All machine learning-based classifiers tested are robust and very accurate in the classification of brain [123I]FP-CIT SPECT scans. Standard visual clinical evaluation should be complemented with quantification classification, and also used as a training tool.N/

Radiomic Features to Predict Overall Survival Time for Patients with Glioblastoma Brain Tumors Based on Machine Learning and Deep Learning Methods

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for patients with Glioblastoma brain tumors from medical imaging and non-imaging data. This system is developed to enhance and speed-up the diagnosis process, as well as to increase understanding of the behavior of Glioblastoma brain tumors. The proposed OST prediction system is developed based on a classification process to categorize a GBM patient into one of the following three survival time groups: short-term (months), mid-term (10-15 months), and long-term (\u3e15 months). The Brain Tumor Segmentation challenge (BraTS) dataset is used to develop the automatic OST prediction system. This dataset consists of multimodal preoperative Magnetic Resonance Imaging (mpMRI) data, and clinical data. The training data is relatively small in size to train an accurate OST prediction model based on DL method. Therefore, traditional ML methods such as Support Vector Machine (SVM), Neural Network, K-Nearest Neighbor (KNN), Decision Tree (DT) were used to develop the OST prediction model for GBM patients. The main contributions in the perspective of ML field include: developing and evaluating five novel radiomic feature extraction methods to produce an automatic and reliable OST prediction system based on classification task. These methods are volumetric, shape, location, texture, histogram-based, and DL features. Some of these radiomic features can be extracted directly from MRI images, such as statistical texture features and histogram-based features. However, preprocessing methods are required to extract automatically other radiomic features from MRI images such as the volume, shape, and location information of the GBM brain tumors. Therefore, a three-dimension (3D) segmentation DL model based on modified U-Net architecture is developed to identify and localize the three glioma brain tumor subregions, peritumoral edematous/invaded tissue (ED), GD-enhancing tumor (ET), and the necrotic tumor core (NCR), in multi MRI scans. The segmentation results are used to calculate the volume, location and shape information of a GBM tumor. Two novel approaches based on volumetric, shape, and location information, are proposed and evaluated in this dissertation. To improve the performance of the OST prediction system, information fusion strategies based on data-fusion, features-fusion and decision-fusion are involved. The best prediction model was developed based on feature fusions and ensemble models using NN classifiers. The proposed OST prediction system achieved competitive results in the BraTS 2020 with accuracy 55.2% and 55.1% on the BraTS 2020 validation and test datasets, respectively. In sum, developing automatic CADiag systems based on robust features and ML methods, such as our developed OST prediction system, enhances the diagnosis process in terms of cost, accuracy, and time. Our OST prediction system was evaluated from the perspective of the ML field. In addition, preprocessing steps are essential to improve not only the quality of the features but also boost the performance of the prediction system. To test the effectiveness of our developed OST system in medical decisions, we suggest more evaluations from the perspective of biology and medical decisions, to be then involved in the diagnosis process as a fast, inexpensive and automatic diagnosis method. To improve the performance of our developed OST prediction system, we believe it is required to increase the size of the training data, involve multi-modal data, and/or provide any uncertain or missing information to the data (such as patients\u27 resection statuses, gender, etc.). The DL structure is able to extract numerous meaningful low-level and high-level radiomic features during the training process without any feature type nominations by researchers. We thus believe that DL methods could achieve better predictions than ML methods if large size and proper data is available