DETERMINING EFFECTIVE LEVEL OF DEMENTIA DISEASE USING MRI IMAGES

Abstract The prevalence of dementia is growing as the world's population ages, making it a major public health issue. The key to successful management and treatment of dementia is an early and precise diagnosis. In this work, we will investigate the Dementia detection model DenseNet-169 in depth. The DenseNet-169 model has been used to classify almost 7,000 magnetic resonance imaging (MRI) scans of the brain. Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia are all categorized using this Convolution Neural Network (CNN) model. The use of deep learning and image processing presents intriguing new directions for the diagnosis and treatment of dementia, with the ultimate goal of enhancing the quality of life for those with the disease

Gait classification of patients with Fabry's disease based on normalized gait features obtained using multiple regression models

Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to inter-subject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies - Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs) - were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing (p < 0.05), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21% after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD.This work was partially supported by the projects NORTE-01-0145- FEDER- 000026 (DeM-Deus Ex Machina) financed by NORTE2020 and FEDER, and the Pluriannual Funding Programs of the research centres CMAT and Algoritm

Deep neural architectures for prediction in healthcare

This paper presents a novel class of systems assisting diagnosis and personalised assessment of diseases in healthcare. The targeted systems are end-to-end deep neural architectures that are designed (trained and tested) and subsequently used as whole systems, accepting raw input data and producing the desired outputs. Such architectures are state-of-the-art in image analysis and computer vision, speech recognition and language processing. Their application in healthcare for prediction and diagnosis purposes can produce high accuracy results and can be combined with medical knowledge to improve effectiveness, adaptation and transparency of decision making. The paper focuses on neurodegenerative diseases, particularly Parkinson’s, as the development model, by creating a new database and using it for training, evaluating and validating the proposed systems. Experimental results are presented which illustrate the ability of the systems to detect and predict Parkinson’s based on medical imaging information

On pattern recognition of brain connectivity in resting-state functional MRI

Dissertação de mestrado integrado em Biomedical Engineering (specialization on Medical Informatics)The human urge and pursuit for information have led to the development of increasingly complex technologies, and new means to study and understand the most advanced and intricate biological system: the human brain. Large-scale neuronal communication within the brain, and how it relates to human behaviour can be inferred by delving into the brain network, and searching for patterns in connectivity. Functional connectivity is a steady characteristic of the brain, and it has been proved to be very useful for examining how mental disorders affect connections within the brain. The detection of abnormal behaviour in brain networks is performed by experts, such as physicians, who limit the process with human subjectivity, and unwittingly introduce errors in the interpretation. The continuous search for alternatives to obtain faster and robuster results have put Machine Learning and Deep Learning in the leading position of computer vision, as they enable the extraction of meaningful patterns, some beyond human perception. The aim of this dissertation is to design and develop an experiment setup to analyse functional connectivity at a voxel level, in order to find functional patterns. For the purpose, a pipeline was outlined to include steps from data download to data analysis, resulting in four methods: Data Download, Data Preprocessing, Dimensionality Reduction, and Analysis. The proposed experiment setup was modeled using as materials resting state fMRI data from two sources: Life and Health Sciences Research Institute (Portugal), and Human Connectome Project (USA). To evaluate its performance, a case study was performed using the In-House data for concerning a smaller number of subjects to study. The pipeline was successful at delivering results, although limitations concerning the memory of the machine used restricted some aspects of this experiment setup’s testing. With appropriate resources, this experiment setup may support the process of analysing and extracting patterns from any resting state functional connectivity data, and aid in the detection of mental disorders.O desejo e a busca intensos do ser humano por informação levaram ao desenvolvimento de tecnologias cada vez mais complexas e novos meios para estudar e entender o sistema biológico mais avançado e intrincado: o cérebro humano. A comunicação neuronal em larga escala no cérebro, e como ela se relaciona com o comportamento humano, pode ser inferida investigando a rede neuronal cerebral e procurando por padrões de conectividade. A conectividade funcional é uma característica constante do cérebro e provou ser muito útil para examinar como os distúrbios mentais afetam as conexões cerebrais. A deteção de anormalidades em imagens de ressonância magnética é realizada por especialistas, como médicos, que limitam o processo com a subjetividade humana e, inadvertidamente, introduzem erros na interpretação. A busca contínua de alternativas para obter resultados mais rápidos e robustos colocou as técnicas de machine learning e deep learning na posição de liderança de visão computacional, pois permitem a extração de padrões significativos e alguns deles para além da percepção humana. O objetivo desta dissertação é projetar e desenvolver uma configuração experimental para analisar a conectividade funcional ao nível do voxel, a fim de encontrar padrões funcionais. Nesse sentido, foi delineado um pipeline para incluir etapas a começar no download de dados até à análise desses mesmos dados, resultando assim em quatro métodos: Download de Dados, Pré-processamento de Dados, Redução de Dimensionalidade e Análise. A configuração experimental proposta foi modelada usando dados de ressonância magnética funcional de resting-state de duas fontes: Instituto de Ciências da Vida e Saúde (Portugal) e Human Connectome Project (EUA). Para avaliar o seu desempenho, foi realizado um estudo de caso usando os dados internos por considerar um número menor de participantes a serem estudados. O pipeline foi bem-sucedido em fornecer resultados, embora limitações relacionadas com a memória da máquina usada tenham restringido alguns aspetos do teste desta configuração experimental. Com recursos apropriados, esta configuração experimental poderá servir de suporte para o processo de análise e extração de padrões de qualquer conjunto de dados de conectividade funcional em resting-state e auxiliar na deteção de transtornos mentais

Role of deep learning in predicting aging-related diseases:A scoping review

Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging)

Artificial intelligence-based software for recognizing parkinsonian gait patterns based on wearable miniaturized sensors

Dissertação de mestrado em Informatics EngineeringA Doença de Parkinson (DP) é uma doença degenerativa do sistema nervoso central, geralmente caracterizada por prejudicar vários aspetos da marcha dos pacientes, como bradicinesia, comprimento do passo encurtado e congelamento da marcha. As escalas de avaliação clínica são tipicamente usadas com base em exames para monitorizar esses sintomas motores associados à marcha. Além disso, estas avaliações são baseadas na memória dos pacientes e pesquisas subjetivas, fornecendo dados tendenciosos. Assim, são necessários dados de longo prazo sobre as atividades motoras diárias do paciente. Avanços tecnológicos forneceram dispositivos sensores pequenos e vestíveis capazes de capturar dados de longo prazo, podendo ser utilizados em ambientes domiciliares permitindo a captura de dados precisos. A combinação desses sensores com inteligência artificial (IA) produz modelos capazes de biomarcar os níveis de doença, condições motoras e bem-estar dos pacientes, e de fornecer dados não tendenciosos sobre os padrões de marcha dos pacientes. A integração destes modelos num aplicativo para médicos facilitará gerir o estado de DP e tratamentos mais personalizados serão alcançados. Tendo isto em conta, esta tese tem como objetivo usar dados de pacientes que apresentam deficiências de marcha para treinar modelos baseados em IA que sejam capazes de classificar níveis de doença, condições motoras e qualidade de vida desses pacientes. Para isso, foram adquiridos dados de 40 pacientes com DP, com o objetivo de desenvolver 3 modelos de IA diferentes, um usado para classificar o nível de doença de um paciente na escala UPDRS-III, outro para classificar as condições motoras escala H&Y e outro usado para classificar a qualidade de vida. Esses modelos foram implementados numa APP para auxiliar os médicos durante as suas consultas. Os resultados obtidos foram positivos. O modelo UPDRS-III conseguiu uma acurácia de 91,67%, uma sensibilidade de 90,43% e uma especificidade de 93,98%, enquanto o modelo H&Y alcançou uma acurácia de 88,98%, uma sensibilidade de 88,71%, e especificidade de 92,79%, sendo que o modelo PDQ-39 obteve acurácia de 84,19%, sensibilidade de 82,13% e especificidade de 90,24%.Parkinson’s Disease (PD) is a degenerative disease of the central nervous system, usually characterized by causing several gait impairment symptoms, such as bradykinesia, shortened stride length, shuffling gait and freezing of gait. Clinical assessment scales are typically used based on observational examinations to monitor these motor symptoms associated with gait. Further, these assessments are based on patients’ memory recall, subjective surveys, medication phase, and mood during the appointment, providing biased data. Thus, long-term data regarding the patient’s daily motor activities is required. Technological advancements provided small and wearable sensor devices able to capture long-term acquisitions of data. Given their miniaturized size and portability, these sensors can be used in domiciliary environments enabling to capture accurate data. Combining these sensors with artificial intelligence (AI) produces models able to biomark patients’ disease levels, motor conditions and well-being. These AI models can provide non-biased data about patients’ gait-associated patterns. Integrating these AI-based solutions in a user-friendly clinic APP for physicians will facilitate PD management, and more personalized treatments will be achieved. Taking this in mind, this thesis aims to use data from patients who show developed gait impairments to train AI-based models that are able to classify disease levels, motor conditions and the quality of life of said patients. For that, data from 40 patients with PD was gathered. This data was then used to develop 3 different AI models, one used to classify a patient’s disease level on the Unified Parkinson’s Disease Rating Scale (UPDRS-III) scale, another to classify a patient’s motor conditions on the Hoehn and Yahr (H&Y) scale, and another one used to classify a patient’s quality of life (QoL). These models were then implemented in an easy to use APP to help the physicians during their appointments with the patients. Positive results were obtained, being observed that. The UPDRS-III model manged to achieve achieve an accuracy of 91.67%, a sensitivity of 90.43%, and a specificity of 93.98%, while the H&Y model achieved an an accuracy of 88.98%, a sensitivity of 88.71%, and a specificity of 92.79%, and the Parkinson’s Disease Questionnaire (PDQ-39) model achieved an accuracy of 84.19%, a sensitivity of 82.13%, and a specificity of 90.24%

Intraoperative Localization of Subthalamic Nucleus during Deep Brain Stimulation Surgery using Machine Learning Algorithms

This thesis presents a novel technique for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery. DBS is an accepted treatment for individuals living with Parkinson\u27s Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. The STN is a small grey matter structure within the brain, which makes accurate placement a challenging task for the surgical team. Prior to placement of the permanent electrode, intraoperative microelectrode recordings (MERs) of neural activity are used to localize the STN. The placement of the permanent electrode and the success of the stimulation therapy depend on accurate localization. In this study, an objective approach was implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning algorithm for defining the electrophysiological borders of the STN. A classification approach that can detect the borders of the STN during the operation is proposed. MER signals from 100 PD patients were recorded and used to validate the performance of the proposed method. The results show that by extracting wavelet transformation features from MER signals and using a deep neural network architecture, it is possible to detect the border of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to assist the surgical team with the goal of enhancing the accuracy and consistency of electrode placement in the STN

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic