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The integration of generative AI and deep learning techniques for Alzheimer's disease detection significantly impacts the research community by advancing diagnostic accuracy and providing a comprehensive understanding of the disease. By combining multiple data modalities, including imaging, genetics, and clinical data, researchers can improve diagnostic precision and develop personalized treatment strategies. Generative AI facilitates efficient data utilization through dataset augmentation, fostering innovation and collaboration across interdisciplinary fields. These methodologies forward the exploration of new diagnostic tools while expediting their application in clinical practice, benefiting patients through early detection and intervention. The incorporation of generative AI may enhance research capabilities, promote collaboration, and improve Alzheimer's disease management and patient outcomes. Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers explores the integration of deep generative models in disease diagnosis, biomarking, and prediction. It examines the use of tools like data analysis, natural language processing, and machine learning for effective Alzheimer’s research. This book covers topics such as data analysis, biomedicine, and machine learning, and is a useful resource for computer engineers, biologists, scientists, medical professionals, healthcare workers, academicians, and researchers.
Provides a definitive overview of the complex ecosystem facilitating Alzheimer's Disease drug research and development. Demonstrates a drug's journey from in the lab, clinical trial testing, regulatory review, and marketing by pharmaceutical companies. Details the use of artificial intelligence, clinical trial management, and financing models.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
This book constitutes the refereed proceedings of the First International Workshop on PRedictive Intelligence in MEdicine, PRIME 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 20 full papers presented were carefully reviewed and selected from 23 submissions. The main aim of the workshop is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field.
Cognitive impairment, through Alzheimer’s disease or other related forms of dementia, is a serious concern for afflicted individuals and their caregivers. Understanding patients’ mental states and combatting social stigmas are important considerations in caring for cognitively impaired individuals. Technology is playing an increasing role in the lives of the elderly. One of the most prevalent developments for the aging population is the use of technological innovations for intervention and treatment of individuals with mental impairments. Research Anthology on Diagnosing and Treating Neurocognitive Disorders examines the treatment, diagnosis, prevention, and therapeutic and technological interventions of neurodegenerative disorders. It also describes programs and strategies that professional and family caregivers can implement to engage and improve the quality of life of persons suffering from cognitive impairment. Highlighting a range of topics such as dementia, subjective wellbeing, and cognitive decline, this publication is an ideal reference source for speech pathologists, social workers, occupational therapists, psychologists, psychiatrists, neurologists, pediatricians, researchers, clinicians, and academicians seeking coverage on neurocognitive disorder identification and strategies for clinician support and therapies.
It has become evident over the last years that abnormalities in RNA processing play a fundamental part in the pathogenesis of neurodegenerative diseases. Cellular viability depends on proper regulation of RNA metabolism and subsequent protein synthesis, which requires the interplay of many processes including transcription, pre--‐mRNA splicing, mRNA editing as well as mRNA stability, transport and translation. Dysfunction in any of these processes, often caused by mutations in the coding and non--‐ coding RNAs, can be very destructive to the cellular environment and consequently impair neural viability. The result of this RNA toxicity can lead to a toxic gain of function or a loss of function, depending on the nature of the mutation. For example, in repeat expansion disorders, such as the newly discovered hexanucleotide repeat expansion in theC9orf72 gene found in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), a toxic gain of function leads to the formation of RNA foci and the sequestration of RNA binding proteins (RBPs). This in return leads to a loss of function of those RBPs, which is hypothesized to play a significant part in the disease progression of ALS and FTD. Other toxicities arising from repeat expansions are the formation of RNA foci, bi--‐directional transcription and production of repeat associated non--‐ATG (RAN) translation products. This book will touch upon most of these disease mechanisms triggered by aberrant RNA metabolism and will therefore provide a broad perspective of the role of RNA processing and its dysfunction in a variety of neurodegenerative disorders, including ALS, FTD, Alzheimer’s disease, Huntington’s disease, spinal muscular atrophy, myotonic dystrophy and ataxias. The proposed authors are leading scientists in the field and are expected to not only discuss their own work, but to be inclusive of historic as well as late breaking discoveries. The compiled chapters will therefore provide a unique collection of novel studies and hypotheses aimed to describe the consequences of altered RNA processing events and its newest molecular players and pathways.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
A rapid development in diverse areas of molecular biology and genetic engineering resulted in emergence of variety of tools. These tools are not only applicable to basic researches being carried out world over, but also exploited for precise detection of abnormal conditions in plants, animals and human body. Although a basic researcher is well versed with few techniques used by him/her in the laboratory, they may not be well acquainted with methodologies, which can be used to work out some of their own research problems. The picture is more blurred when the molecular diagnostic tools are to be used by physicians, scientists and technicians working in diagnostic laboratories in hospitals, industry and academic institutions. Since many of them are not trained in basics of these methods, they come across several gray areas in understanding of these tools. The accurate application of molecular diagnostic tools demands in depth understanding of the methodology for precise detection of the abnormal condition of living body. To meet the requirements of a good book on molecular diagnostics of students, physicians, scientists working in agricultural, veterinary, medical and pharmaceutical sciences, it needs to expose the reader lucidly to: Give basic science behind commonly used tools in diagnostics Expose the readers to detailed applications of these tools and Make them aware the availability of such diagnostic tools The book will attract additional audience of pathologists, medical microbiologists, pharmaceutical sciences, agricultural scientists and veterinary doctors if the following topics are incorporated at appropriate places in Unit II or separately as a part of Unit-III in the book. Molecular diagnosis of diseases in agricultural crops Molecular diagnosis of veterinary diseases. Molecular epidemiology, which helps to differentiate various epidemic strains and sources of disease outbreaks. Even in different units of the same hospital, the infections could be by different strains of the same species and the information becomes valuable for infection control strategies. Drug resistance is a growing problem for bacterial, fungal and parasitic microbes and the molecular biology tools can help to detect the drug resistance genes without the cultivation and in vitro sensitivity testing. Molecular diagnostics offers faster help in the selection of the proper antibiotic for the treatment of tuberculosis, which is a major problem of the in the developing world. The conventional culture and drug sensitivity testing of tuberculosis bacilli is laborious and time consuming, whereas molecular diagnosis offers rapid drug resistant gene detection even from direct clinical samples. The same approach for HIV, malaria and many more diseases needs to be considered. Molecular diagnostics in the detection of diseases during foetal life is an upcoming area in the foetal medicine in case of genetic abnormalities and infectious like TORCH complex etc. The book will be equally useful to students, scientists and professionals working in the field of molecular diagnostics.
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.
BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.