Early Diagnosis of Alzheimer's Disease by NIRF Spectroscopy\ud and Nuclear Medicine\ud

Novel approaches to Early Diagnosis of Alzheimer's Disease by NIRF Spectroscopy and Nuclear Medicine are presented and related cognitive, as well as molecular and cellular, models are critically evaluated.\u

Early Diagnosis of Alzheimer's disease by NIRF Spectroscopy and Nuclear Medicine-v.4.0

There is an urgent need for the early detection of diseases such as Alzheimer’s (AD) and Cancers in order to enable their successful treatment. Cancer is the second major cause of death after Heart Disease, and AD is the third major cause of death with major, human and financial/economics trillion dollar consequences for the society. Nuclear Medicine is concerned with applications in Medicine of Nuclear Science and Engineering techniques and knowledge. Three major Nuclear Medicine techniques that are established for diagnostic and research purposes are: Positron Emission Tomography (PET) and CAT/CT, Nuclear Magnetic Resonance Imaging (NMRI/MRI). However, these three techniques have also major limitations in terms of either cost or image resolution, as well as patient irradiation in the case of CAT/CT and PET. On the other hand, Near Infrared Chemical Imaging Microspectroscopy and certain Fluorescence spectroscopic techniques are capable of single cancer cell and/or single molecule detection and/or imaging. Such powerful capabilities, combined with low cost of diagnostics, make these novel techniques very attractive means for early detection of diseases such as cancer and Alzheimer’s, that are promising to reduce the fatality rate of patients through adequate diagnosis and treatment of such diseases at early stages. 
Currently NIH provides only inadequate funding for the clinical and research aspects of these novel investigation and clinical diagnostic techniques by FT-NIRS and Fluorescence spectrocopy for early detection of Alzheimer’s and Cancers.
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Early Diagnosis of Alzheimer's disease by NIRF Spectroscopy and Nuclear Medicine

There is an urgent need for the early detection of diseases such as Alzheimer’s (AD) and Cancers in order to enable their successful treatment. Cancer is the second major cause of death after Heart Disease, and AD is the third major cause of death with major, human and financial/economics trillion dollar consequences for the society. Nuclear Medicine is concerned with applications in Medicine of Nuclear Science and Engineering techniques and knowledge. Three major Nuclear Medicine techniques that are established for diagnostic and research purposes are: Positron Emission Tomography (PET) and CAT/CT, Nuclear Magnetic Resonance Imaging (NMRI/MRI). However, these three techniques have also major limitations in terms of either cost or image resolution, as well as patient irradiation in the case of CAT/CT and PET. On the other hand, Near Infrared Chemical Imaging Microspectroscopy and certain Fluorescence spectroscopic techniques are capable of single cancer cell and/or single molecule detection and/or imaging. Such powerful capabilities, combined with low cost of diagnostics, make these novel techniques very attractive means for early detection of diseases such as cancer and Alzheimer’s, that are promising to reduce the fatality rate of patients through adequate diagnosis and treatment of such diseases at early stages. 
Currently NIH provides only inadequate funding for the clinical and research aspects of these novel investigation and clinical diagnostic techniques by FT-NIRS and Fluorescence spectrocopy for early detection of Alzheimer's and Cancers

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol

INTRODUCTION: Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case-control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history. METHODS AND ANALYSIS: We will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared. ETHICS AND DISSEMINATION: This study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients' records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities' Action in Response to Dementia project (https://www.tip-card.hku.hk/)

Classification and treatment of different stages of alzheimer’s disease using various machine learning methods

There has been a steady rise in the number of patients suffering from Alzheimer’s disease (AD) all over the world. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. The patient’s records are collected from National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control or as patients. Patients were concerned about their memory at the National Institute on Aging. It also consisted of patients and caregiver interviews. This research work presents different models for the classification of different stages of Alzheimer’s disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to other classification methods. Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in diagnosis and treatment of AD

Feature selective temporal prediction of Alzheimer’s disease progression using hippocampus surface morphometry

IntroductionPrediction of Alzheimer’s disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.MethodsPrevious work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.ResultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.ConclusionsBy combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.In this work, we present our results of using machine learning to predict temporal behavior changes in Alzheimers Disease using entire topological feature maps of the hippocampus surface (2100 feature points). Our paper demonstrates that it is possible to use an entire topological map instead of just imaging derived volumetric measurements for predicting behavioral changes. We compare these results with previous results using only volumetric MR imaging features (309 features points) and show through repeated cross‐validation rounds that we are able to get better predictive power.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/1/brb3733_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/2/brb3733.pd

Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique

The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order among the variables --- an expert may not always be available, or may be unable to provide the order. Nevertheless, if a good order is available, these order-based algorithms have demonstrated good performance. More recent approaches attempt to ``learn'' a good order then use the order-based algorithm to discover the structure. To eliminate the need for order information during construction, we propose a search in the entire space of Bayesian network structures --- we present a novel approach for carrying out this task, and we demonstrate its performance against existing algorithms that search in the entire space and the space of orders. Finally, we employ the hand-crafting framework to construct models for the task of diagnosis in a ``real-life'' medical domain, dementia diagnosis. We collect real dementia data from clinical practice, and we apply the data-driven algorithms developed to assess the concordance between the reference models developed by hand and the models derived from real clinical data

Hypothesis Testing with Classifier Systems

This thesis presents a new ML algorithm, HCS, taking inspiration from Learning Classifier Systems, Decision Trees and Statistical Hypothesis Testing, aimed at providing clearly understandable models of medical datasets. Analysis of medical datasets has some specific requirements not always fulfilled by standard Machine Learning methods. In particular, heterogeneous and missing data must be tolerated, the results should be easily interpretable. Moreover, often the combination of two or more attributes leads to non-linear effects not detectable for each attribute on its own. Although it has been designed specifically for medical datasets, HCS can be applied to a broad range of data types, making it suitable for many domains. We describe the details of the algorithm, and test its effectiveness on five real-world datasets