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Author: Tommaso Mansi
Publisher: Academic Press
ISBN: 0128168951
Category : Science
Languages : en
Pages : 276
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Book Description
Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient's heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications. - Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications - Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data - Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
Author: Tommaso Mansi
Publisher: Academic Press
ISBN: 0128168951
Category : Science
Languages : en
Pages : 276
Get Book Here
Book Description
Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient's heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications. - Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications - Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data - Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
Author: Esther Puyol Antón
Publisher: Springer Nature
ISBN: 3030937224
Category : Computers
Languages : en
Pages : 397
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Book Description
This book constitutes the proceedings of the 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021, as well as the M&Ms-2 Challenge: Multi-Disease, Multi-View and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. The 25 regular workshop papers included in this volume were carefully reviewed and selected after being revised. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. In addition, 15 papers from the M&MS-2 challenge are included in this volume. The Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge (M&Ms-2) is focusing on the development of generalizable deep learning models for the Right Ventricle that can maintain good segmentation accuracy on different centers, pathologies and cardiac MRI views. There was a total of 48 submissions to the workshop.
Author: Hayit Greenspan
Publisher: Springer Nature
ISBN: 3031439902
Category : Computers
Languages : en
Pages : 841
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Book Description
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
Author: Daniel B. Ennis
Publisher: Springer Nature
ISBN: 3030787109
Category : Computers
Languages : en
Pages : 697
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Book Description
This book constitutes the refereed proceedings of the 11th International Conference on Functional Imaging and Modeling of the Heart, which took place online during June 21-24, 2021, organized by the University of Stanford. The 65 revised full papers were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: advanced cardiac and cardiovascular image processing; cardiac microstructure: measures and models; novel approaches to measuring heart deformation; cardiac mechanics: measures and models; translational cardiac mechanics; modeling electrophysiology, ECG, and arrhythmia; cardiovascular flow: measures and models; and atrial microstructure, modeling, and thrombosis prediction.
Author: Mihaela Pop
Publisher: Springer
ISBN: 3030120295
Category : Computers
Languages : en
Pages : 497
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Book Description
This book constitutes the thoroughly refereed post-workshop proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 52 revised full workshop papers were carefully reviewed and selected from 60 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.
Author: Rafael Sebastian
Publisher: Frontiers Media SA
ISBN: 2889761509
Category : Science
Languages : en
Pages : 356
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Book Description
Author: Subhi J. Al'Aref
Publisher: Academic Press
ISBN: 0128202734
Category : Science
Languages : en
Pages : 454
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Book Description
Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine.
Author: Kevin Daimi
Publisher: Springer Nature
ISBN: 3031421124
Category : Technology & Engineering
Languages : en
Pages : 379
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Book Description
This book is comprehensive with most of its contents following the recommendations of international health and medical informatics associations and societies. To this extent it covers the areas of healthcare and medical information systems, management of healthcare and medical information systems, healthcare/medical information systems supporting patients and the public, healthcare/medical networking and care sharing, medical imaging and 3D/4D surgical visualization, design and analysis of health/medical records, health/medical data representation and analysis, simulation and modeling in healthcare, and health and medical informatics education. The book provides an excellent professional development resource for educators and practitioners on the state-of-the-art Health and Medical Informatics. It covers many areas and topics of Health and Medical Informatics and contributes toward the enhancement of the community outreach and engagement component of Health and Medical Informatics. Various techniques, methods, and approaches adopted by Health and Medical Informatics experts in the field are introduced. Furthermore, it provides detailed explanation of the Health and Medical Informatics concepts that are aptly reinforced by applications and some practical examples and a road map of future trends that are suitable for innovative Health and Medical Informatics.
Author: Carlo N. De Cecco
Publisher: Springer Nature
ISBN: 3030920879
Category : Medical
Languages : en
Pages : 582
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Book Description
This book provides an overview of current and potential applications of artificial intelligence (AI) for cardiothoracic imaging. Most AI systems used in medical imaging are data-driven and based on supervised machine learning. Clinicians and AI specialists can contribute to the development of an AI system in different ways, focusing on their respective strengths. Unfortunately, communication between these two sides is far from fluent and, from time to time, they speak completely different languages. Mutual understanding and collaboration are imperative because the medical system is based on physicians’ ability to take well-informed decisions and convey their reasoning to colleagues and patients. This book offers unique insights and informative chapters on the use of AI for cardiothoracic imaging from both the technical and clinical perspective. It is also a single comprehensive source that provides a complete overview of the entire process of the development and use of AI in clinical practice for cardiothoracic imaging. The book contains chapters focused on cardiac and thoracic applications as well more general topics on the potentials and pitfalls of AI in medical imaging. Separate chapters will discuss the valorization, regulations surrounding AI, cost-effectiveness, and future perspective for different countries and continents. This book is an ideal guide for clinicians (radiologists, cardiologists etc.) interested in working with AI, whether in a research setting developing new AI applications or in a clinical setting using AI algorithms in clinical practice. The book also provides clinical insights and overviews for AI specialists who want to develop clinically relevant AI applications.
Author: Pablo Martinez-Cañada
Publisher: Frontiers Media SA
ISBN: 2832557120
Category : Science
Languages : en
Pages : 136
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Book Description
Computational models and machine-learning methods are increasingly valuable tools to shed light on the dynamics that govern information processing in the nervous system, as well as their disruption in pathological conditions. A variety of techniques has been used to understand how networks of neurons in the brain encode, elaborate and transmit information about the external world, and how this information influences decision-making and behavior. Structural and functional abnormalities in the above-mentioned networks can lead to a wide range of brain disorders. Recent advances in brain simulation and machine-learning techniques, together with progress in the neuroimaging field, have been essential for bridging the different spatial scales in the brain and uncovering the processes underlying cognitive, motor and behavioral impairment in neurodevelopmental and neurodegenerative disorders.
