Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data

Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features. Support vector machine classifiers are then built upon different sets of features, including the transfer learning features, conventional imaging features, and their combination. Experimental results have demonstrated that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their US kidney images.Comment: Accepted paper in IEEE International Symposium on Biomedical Imaging (ISBI), 201

Abordagem CNN 2D estendida para o diagnóstico da doença de Alzheimer através de imagens de ressonância magnética estrutural

Orientadores: Leticia Rittner, Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A doença de Alzheimer (AD - Alzheimer's disease) é um tipo de demência que afeta milhões de pessoas em todo o mundo. Até o momento, não há cura para a doença e seu diagnóstico precoce tem sido uma tarefa desafiadora. As técnicas atuais para o seu diagnóstico têm explorado as informações estruturais da Imagem por Ressonância Magnética (MRI - Magnetic Resonance Imaging) em imagens ponderadas em T1. Entre essas técnicas, a rede neural convolucional (CNN - Convolutional Neural Network) é a mais promissora e tem sido usada com sucesso em imagens médicas para uma variedade de aplicações devido à sua capacidade de extração de características. Antes do grande sucesso do aprendizado profundo e das CNNs, os trabalhos que objetivavam classificar os diferentes estágios de AD exploraram abordagens clássicas de aprendizado de máquina e uma meticulosa extração de características, principalmente para classificar testes binários. Recentemente, alguns autores combinaram técnicas de aprendizagem profunda e pequenos subconjuntos do conjunto de dados públicos da Iniciativa de Neuroimagem da Doença de Alzheimer (ADNI - Alzheimer's Disease Neuroimaging Initiative) para prever um estágio inicial da doença explorando abordagens 3D CNN geralmente combinadas com arquiteturas de auto-codificador convolucional 3D. Outros também exploraram uma abordagem de CNN 3D combinando-a ou não com uma etapa de pré-processamento para a extração de características. No entanto, a maioria desses trabalhos focam apenas na classificação binária, sem resultados para AD, comprometimento cognitivo leve (MCI - Mild Cognitive Impairment) e classificação de sujeitos normais (NC - Normal Control). Nosso principal objetivo foi explorar abordagens de CNN 2D para a tarefa de classificação das 3 classes usando imagens de MRI ponderadas em T1. Como objetivo secundário, preenchemos algumas lacunas encontradas na literatura ao investigar o uso de arquiteturas CNN 2D para o nosso problema, uma vez que a maioria dos trabalhos explorou o aprendizado de máquina clássico ou abordagens CNN 3D. Nossa abordagem CNN 2D estendida explora as informações volumétricas dos dados de ressonância magnética, mantendo baixo custo computacional associado a uma abordagem 2D, quando comparados às abordagens 3D. Além disso, nosso resultado supera as outras estratégias para a classificação das 3 classes e comparando o desempenho de nosso modelo com os métodos tradicionais de aprendizado de máquina e 3D CNN. Também investigamos o papel de diferentes técnicas amplamente utilizadas em aplicações CNN, por exemplo, pré-processamento de dados, aumento de dados, transferência de aprendizado e adaptação de domínio para um conjunto de dados brasileiroAbstract: Alzheimer's disease (AD) is a type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer's and its early-diagnosis has been a challenging task. The current techniques for Alzheimer's disease diagnosis have explored the structural information of Magnetic Resonance Imaging (MRI) in T1-weighted images. Among these techniques, deep convolutional neural network (CNN) is the most promising one and has been successfully used in medical images for a variety of applications due to its ability to perform features extraction. Before the great success of deep learning and CNNs, the works that aimed to classify the different stages of AD explored classic machine learning approaches and a meticulous feature engineering extraction, mostly to classify binary tasks. Recently, some authors have combined deep learning techniques and small subsets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public dataset, to predict an early-stage of AD exploring 3D CNN approaches usually combined with 3D convolutional autoencoder architectures. Others have also investigated a 3D CNN approach combining it or not with a pre-processing step for the extraction of features. However, the majority of these papers focus on binary classification only, with no results for Alzheimer's disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) classification. Our primary goal was to explore 2D CNN approaches to tackle the 3-class classification using T1-weighted MRI. As a secondary goal, we filled some gaps we found in the literature by investigating the use of 2D CNN architectures to our problem, since most of the works either explored traditional machine learning or 3D CNN approaches. Our extended-2D CNN explores the MRI volumetric data information while maintaining the low computational costs associated with a 2D approach when compared to 3D-CNNs. Besides, our result overcomes the other strategies for the 3-class classification while analyzing the performance of our model with traditional machine-learning and 3D-CNN methods. We also investigated the role of different widely used techniques in CNN applications, for instance, data pre-processing, data augmentation, transfer-learning, and domain-adaptation to a Brazilian datasetMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica168468/2017-4  CNP

Cerebral Small Vessel Disease and Cerebral Amyloid Angiopathy

Sporadic cerebral small vessel disease (SVD) is considered to be among the most commonly known neuropathological processes in the brain, hosting a crucial role in stroke, cognitive impairment, and functional loss in elderly subjects. We investigated clinical (neuroimaging and cognitive) biomarkers in the SVD, through a series of analyses from our five studies. Sporadic cerebral SVD is a complex ‘micro-world’ to be globally considered. All the relevant lesion types and SVD neuroimaging burden should be taken into account. The cumulative effects of microangiopathy burden in the brain of patients affected by SVD are crucial. Cognitive rehabilitation could represent a promising approach to prevent vascular dementia or to improve cognitive performances in patients with cerebral SVD. Longitudinal studies may provide more robust information about the progression and prognostic significance of our findings

Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records

Introduction Clustering algorithms are a class of algorithms that can discover groups of observations in complex data and are often used to identify subtypes of heterogeneous diseases in electronic health records (EHR). Evaluating clustering experiments for biological and clinical significance is a vital but challenging task due to the lack of consensus on best practices. As a result, the translation of findings from clustering experiments to clinical practice is limited. Aim The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of clustering experiments using EHR. Methods We conducted a scoping review of clustering studies in EHR to identify common evaluation approaches. We systematically investigated the performance of the identified approaches using a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER) that tested whether clusterable structures exist in EHR. To develop this method we tested several cluster validation indexes and methods of generating null data to see which are the best at discovering clusters. In order to enable the robust benchmarking of evaluation approaches, we created a tool that generated synthetic EHR data that contain known cluster labels across a range of clustering scenarios. Results Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing cluster results across multiple algorithms (30% of studies). We examined this approach conducting a clustering experiment on AD patients using a population of 10,065 AD patients and 21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means 4 was found to have the best clustering solution with the highest silhouette score (0.19) and was more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD (n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of mental health issues, smoking and early disease onset (n=1528), which has been found in previous research as well as in the results of other clustering methods. We created a synthetic data generation tool which allows for the generation of realistic EHR clusters that can vary in separation and number of noise variables to alter the difficulty of the clustering problem. We found that decreasing cluster separation did increase cluster difficulty significantly whereas noise variables increased cluster difficulty but not significantly. To develop the tool to assess clusters existence we tested different methods of null dataset generation and cluster validation indices, the best performing null dataset method was the min max method and the best performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters were identified using the Calinski Harabasz index they were more likely to have significantly different outcomes between clusters. Lastly we repeated the initial clustering experiment, comparing 10 different pre-processing methods. The three best performing methods were RBF kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters; heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory loss (n = 1823), female with more problem (n=2244). Conclusion We have developed and tested a series of methods and tools to enable the evaluation of EHR clustering experiments. We developed and proposed a novel cluster evaluation metric and provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people

Making it count : novel behavioural tasks to quantify symptoms of dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a neurodegenerative disease and a common cause of dementia in the elderly. The primary pathology of DLB is the mis-folding of the α-synuclein protein, classifying DLB as a synucleinopathy. However, concomitant pathologies are commonly found in post-mortem examination of DLB patients that may complicate diagnosis. Furthermore, DLB is a relatively new disease, first discovered in 1976, while the first official diagnostic criteria released in 1996. Consequently, the diagnostic criteria for DLB have evolved as more is learnt about the clinical and neuropathological profile. Synucleinopathies are also known to be heterogeneous, with no single symptom or biomarker present in all DLB cases. Instead, combinations of common symptoms lead to a diagnosis of probable DLB. Two of the most prominent and debilitating symptoms of DLB are visual hallucinations and cognitive fluctuations. Visual hallucinations (VH) in DLB patients are typically vivid, well-formed percepts and are a major cause of patient and caregiver stress as well as a risk factor for the patient being placed into professional care. Cognitive fluctuations (CF) involve a cycling change in attention and alertness and may occur on a daily or monthly basis, while drops in awareness may last seconds or hours. Currently, the only tools to measure cognitive fluctuations or visual hallucinations are scales or questionnaires that rely on responses from the patient or informant. Furthermore, severity of the symptom is then ranked on an arbitrary ranking system. While this method has advantages in a clinical setting, the subjective nature of the scales combined with the ranking of scores results in a loss of sensitivity. In a research setting, especially imaging or clinical trials, objective measures that are sensitive to changes in symptom severity are highly valued. This allows researchers to assess the relationship between behavioural and fMRI data and clinicians to observe subtle changes in severity. Furthermore, the measures need to be easy to conduct as patients are often severely impaired. The aim of this thesis is to test cognitive function using three paradigms that are novel to DLB patients: Sustained Attention Response Task (SART), the Mental Rotation (MR) task and the Bistable Percept Paradigm (BPP). Overall, this thesis provided the groundwork needed before these three tasks can be utilised in a clinical or research setting. Moreover, as each task was accessible to DLB patients and provided a measure associated with VH or CF, they may prove useful for future neuroimaging/neuropsychological studies

Neurocognitive patterns and progression of mild cognitive impairment with Lewy bodies or Alzheimer’s disease

PhD ThesisMild cognitive impairment is a heterogeneous condition; while typically viewed as the transition stage between healthy cognitive function and dementia, the cognitive and clinical patterns of this condition vary, as do its prognosis. The two most common neurodegenerative dementias, dementia with Lewy bodies and Alzheimer’s disease, differ in their clinical features, patterns of cognitive impairment, and prognosis. It is not known whether the respective mild cognitive impairment stages preceding onset of dementia also differ in these characteristics. Two cohorts of people with recent mild cognitive impairment diagnosis were assessed, undergoing annual review of cognition, diagnosis and presence of clinical features of Lewy body disease by an expert panel of old age psychiatrists, and repeated imaging, to reach a consensus diagnosis of either mild cognitive impairment due to Alzheimer’s disease, or with Lewy bodies, in line with current consensus criteria for these. Making use of annually repeated cognitive assessment and clinical diagnostic information, the longitudinal progression of these two conditions was characterised with flexible statistical methods, using the first cohort for model development, and the second for validation: different trajectories of decline in specific cognitive domains were observed in the diagnostic groups, reflecting typical patterns of impairment in their respective dementia syndromes. Mild cognitive impairment with Lewy bodies was also observed to have a worse prognosis, in keeping with its dementia stage, with a greater risk of progressive cognitive decline, and faster onset of dementia. Individuals with neuropsychiatric symptoms (cognitive fluctuations and visual hallucinations) were also at more risk of decline than those with slower-developing features (REM sleep behaviour disorder or parkinsonism) or those with Alzheimer’s disease. Mild cognitive impairment with Lewy bodies may therefore feature early cognitive, clinical, and prognostic differences from Alzheimer’s disease, reflecting its eventual dementia syndrome