Gary Tse – författare

Internet of Things and Machine Learning for?Type I and Type II Diabetes: Use Cases provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.

This book provides a comprehensive overview of cutting-edge technologies in the diagnosis of multiple cardiovascular disease. Readers will understand technical advancements and research gaps, which will help them to optimize the design of algorithms and devices.

Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare provides details on the adoption and implementation of advanced diagnostic techniques in different clinical settings, including population screening, clinical diagnosis, risk prediction, data-driven diagnosis, and remote healthcare monitoring. It also covers various cardiovascular diseases, from the macrovascular to microvascular levels, where early and accurate diagnosis is a high clinical need, for example, stroke, intracranial atherosclerosis, coronary artery disease, and microvascular dysfunction. The book is a practical guide with case studies, and the authors cover a wide range of cutting-edge diagnostic techniques, including artificial intelligence, radiological imaging, wearable sensors, genetic biomarkers, and multi-omics data integration. It summarizes the potentials, challenges, and ethical concerns in the implementation of these techniques under current clinical settings. It also discusses the future directions and perspectives for next-generation diagnostics based on AI-enhanced multimodal data fusion.

This book targets a mixed audience of data scientists, engineers, clinicians, researchers, academics, and students interested in cutting-edge technologies, methodologies, and practices in the diagnostics of cardiovascular diseases to resolve the challenging gaps in healthcare and clinical applications.

This book provides a comprehensive overview of cutting-edge technologies in the diagnosis of multiple cardiovascular disease. Readers will understand technical advancements and research gaps, which will help them to optimize the design of algorithms and devices.

Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare provides details on the adoption and implementation of advanced diagnostic techniques in different clinical settings, including population screening, clinical diagnosis, risk prediction, data-driven diagnosis, and remote healthcare monitoring. It also covers various cardiovascular diseases, from the macrovascular to microvascular levels, where early and accurate diagnosis is a high clinical need, for example, stroke, intracranial atherosclerosis, coronary artery disease, and microvascular dysfunction. The book is a practical guide with case studies, and the authors cover a wide range of cutting-edge diagnostic techniques, including artificial intelligence, radiological imaging, wearable sensors, genetic biomarkers, and multi-omics data integration. It summarizes the potentials, challenges, and ethical concerns in the implementation of these techniques under current clinical settings. It also discusses the future directions and perspectives for next-generation diagnostics based on AI-enhanced multimodal data fusion.

This book targets a mixed audience of data scientists, engineers, clinicians, researchers, academics, and students interested in cutting-edge technologies, methodologies, and practices in the diagnostics of cardiovascular diseases to resolve the challenging gaps in healthcare and clinical applications.

This book provides a detailed update on all aspects of fine-needle aspiration cytology of breast lesions. It will serve as an up-to-date reference and atlas on new entities in breast pathology, including classic benign and malignant lesions, in accordance with current classification schemes and staging protocols. Emphasis is placed on description of characteristic diagnostic features as well as common cytological pitfalls. Correlation of cytology with histology is achieved through cyto-histological illustrations, enabling readers to gain a good understanding of the morphologic features of various lesions. Core-needle biopsy and fine-needle aspiration cytology are compared in depth, with attention to their relative strengths and weaknesses and indications. In addition, the impact of molecular classification of breast cancers on cytologic diagnosis is explored, and the use of molecular techniques and artificial intelligence in diagnostic cytology is discussed.