Difficulties of Diagnosing Alzheimer's Disease: The Application of Clinical Decision Support Systems

Introduction: Alzheimer's disease is one of the most common causes of dementia, which gradually causes cognitive impairment. Diagnosis of Alzheimer's disease is a complicated process performed through several tests and examinations. Design and development of Clinical Decision Support System (CDSS) could be an appropriate approach for eliminating the existing difficulties of diagnosing Alzheimer's disease. Materials and Methods: This study reviews the current problems in the diagnosis of Alzheimer's disease with an approach to the application of CDSS. The study reviewed the articles published from 1990 to 2016. The articles were identified by searching electronic databases such as PubMed, Google Scholar, Science Direct. Considering the relevance of articles with the objectives of the study, 29 papers were selected. According to the performed investigations, various reasons cause difficulty in Alzheimer's diagnosis. Results: The complexity of diagnostic process and  the similarity of Alzheimer's disease with other causes of dementia are the most important of them. The results of studies about the application of CDSSs on Alzheimer's disease diagnosis indicated that the implementation of these systems could help to eliminate the existing difficulties in the diagnosis of Alzheimer's disease. Conclusion: Developing CDSSs based on diagnostic guidelines could be regarded as one of the possible approaches towards early and accurate diagnosis of Alzheimer's disease. Applying of computer-interpretable guideline (CIG) models such as GLIF, PROforma, Asbru, and EON can help to design CDSS with the capability of minimizing the burden of diagnostic problems with Alzheimer's disease

Information Technologies for Cognitive Decline

Information technology (IT) is used to establish a diagnosis and provide treatment for people with cognitive decline. The condition affects many before it becomes clear that more permanent changes, like dementia, could be noticed. Those who search for information are exposed to lots of information and different technologies which they need to make sense of and eventually use to help themselves. In this research literature and information available on the Internet were systematically analyzed to present methods used for diagnosis and treatment. Methods used for diagnosis are self-testing, sensors, Virtual Reality (VR), and brain imaging. Methods used for treatment are games, websites with information and media, Virtual Reality (VR), sensors, and robots. The resulting concept of knowledge was the basis of an artifact whose main goal was to present the facts to the broad public. This implied that a user-friendly artifact was developed through three iterations using the Design Science framework. A total of nine users and IT usability experts have evaluated the artifact returning the SUS score of 85,83 for users and 87,5 for IT usability experts. Nielsen´s heuristics were assessed by IT usability experts only, returning an average score of 4,28. The general response was positive regarding both the content and the attempt to present methods used in cognitive decline. It reminds to be seen how to bring this knowledge to those who are most affected by the decline.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

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

Heart and Soul: A Phenomenology of Dementia Spouse Caregivers’ Relationship Closeness

All persons with dementia exhibit combinations of cognitive impairment (memory loss and aphasia), functional losses (changes in activities of daily living), and neuropsychiatric disease manifestations (fluctuating moods, behaviors and psychoses). The spouse caregiver role and life are affected by these symptoms which can cause intimacy, reciprocity, and communication issues, and frequently result in the caregiver’s feelings of inadequacy, hopelessness, and embarrassment. Yet caregivers somehow manage to reconstruct and maintain a closeness as a couple. Relationship closeness (RC) found in all marriages is simply depicted as the quality of the communal and emotional bond between husband and wife. Limited research demonstrates some elements of RC are altered by the progression of dementia disease and links RC with the couple’s morbidity (ill health and depression) and caregiving outcomes (burden, coping, transcendence and efficacy). But RC is poorly defined in published studies, and not adequately or critically examined in context with dementia’s cognitive impairment and neuropsychiatric symptoms, whose very characteristics disrupt critical elements of interpersonal relationships. The purpose of this descriptive phenomenological study was to investigate the phenomenon of spouse caregiver RC; it focused on dementia and the sequelae of dementia symptoms and behaviors. The research questions were: 1) what is the fundamental structure of this phenomenon, and2) what is the meaning of the experience of this phenomenon. This inquiry uncovered a rich description of marital RC in couples, by focusing on their memories and lived-world experiences as caring partners, and as their spouse expressed dementia symptoms daily. Both theoretical underpinnings and qualitative research design used Colaizzi’s existential philosophy and Phenomenological Reflection method of data analysis. Data emerged from the author’s Individual Phenomenological Reflection and sixteen dialogal interviews. Findings revealed the meaning of the fundamental structure of RC in the face of dementia as a journey. Five essential constituents emerged: A past together, the present in which spouses see, feel and respond to the dementia, and lastly, a future together. The meaning expressed by spouses was woven in a tapestry of dementia symptomatology, and dyadic internal and external changes in RC described with important sub-themes such as loss and hurt, imperfect caregiving and compassionate love

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries