Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

Sistema computacional para el diagnóstico asistido de deterioro cognitivo leve en personas mayores de 60 años

El Deterioro Cognitivo Leve es una entidad nosológica de suma importancia; fue introducida para definir la situación clínica de declive o pérdida de las capacidades cognitivas que supone una fase previa a un trastorno demencial grave y que se estima no estar originado por el envejecimiento, sino por una patología subyacente. No obstante, la detección de dicho deterioro presenta dificultades en términos de costo monetario, tiempo y personal calificado en el proceso de diagnóstico, dado que este deterioro puede involucrar la toma de exámenes médicos de alta complejidad (ej. resonancias magnéticas) que a su vez requieren equipos clínicos especializados (ej. escáneres de resonancia magnética). En el presente trabajo, con el fin de asistir al diagnóstico de Deterioro Cognitivo Leve, se construye un sistema computacional híbrido basado en técnicas simbólicas y subsimbólicas de aprendizaje de máquina; éste permite analizar los resultados de diferentes pruebas cognitivas para apoyar el dictamen realizado por los profesionales de la salud respecto al estado mental de las personas. El proceso de desarrollo del sistema computacional se lleva a cabo mediante una metodología orientada a prototipos evolutivos. Finalmente, se valida la efectividad en el diagnóstico asistido de Deterioro Cognitivo Leve a través de un esquema de cross-validation o validación cruzada.Mild cognitive impairment is a crucial nosological entity. It was introduced to define the clinical state of decline or loss of cognitive abilities which represents a preliminary stage to severe dementia disorders and is thought not to be caused by aging, but by an underlying pathology. However, diagnosis of such impairment is a challenging task facing difficulties in terms of monetary costs, time as well as finding qualified experts on this topic, since this may involve taking medical tests of high complexity (e.g. magnetic resonance imaging), which in turn require specialized clinical equipment (e.g. magnetic resonance imaging scanners). In this work, in order to assist the diagnosis of mild cognitive impairment, a hybrid system based on symbolic and subsymbolic machine learning techniques is built. The proposed system will be able to analyze the results of different cognitive tests to support the decisions-making by the health staff service regarding the mental state of patients. Particularly, the system development process is conducted by an evolutionary prototyping methodology. Finally, the computer-aided effectiveness is validated through a crossvalidation scheme