Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside
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Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside
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Author : Jiahan Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-12
Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside written by Jiahan Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Medical categories.
Improving Cone Beam Computed Tomography Based Adaptive Radiation Therapy With Deep Learning
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Author : Xiao Liang
language : en
Publisher:
Release Date : 2023
Improving Cone Beam Computed Tomography Based Adaptive Radiation Therapy With Deep Learning written by Xiao Liang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Deep Learning For Online Adaptive Radiotherapy
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Author :
language : en
Publisher:
Release Date : 2022
Deep Learning For Online Adaptive Radiotherapy written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Investigation Of Adaptive Radiation Therapy Including Deformable Image Registration Treatment Planning Modification Strategies Machine Learning Deep Learning
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Author : Pawel Siciarz
language : en
Publisher:
Release Date : 2021
Investigation Of Adaptive Radiation Therapy Including Deformable Image Registration Treatment Planning Modification Strategies Machine Learning Deep Learning written by Pawel Siciarz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
The goal of this research was to propose and evaluate solutions to four important aspects of adaptive radiation therapy in order to make it more reliable, accurate, and efficient in clinical environment. The first study focused on the evaluation of several deformable image registration algorithms. Results demonstrated that the Dense Anatomical Block Matching registration outperformed the other methods making it a very promising alternative to the existing registration methods for challenging CT-to-CBCT registration and its applications for radiation dose calculation, dose mapping and contour propagation in adaptive radiation therapy (ART) of the pelvic region. The second study focused on the quantitative evaluation of eight proposed adaptive radiation therapy approaches for prostate cancer patients treated with hypofractionated VMAT. The ART strategies included online and offline methods. The comprehensive analysis showed that daily on-line adaptation approaches were the most impactful. The findings of this study provided applicable insights into the selection of the optimal ART strategy, improving the quality of the decision-making process based on the quantitatively evaluated dosimetric benefits. The third study aimed to utilize a deep learning network to automatically contour critical organs on the computed tomography (CT) scans of head and neck cancer patients. Proposed model achieved expert level accuracy and was able to segment 25 critical organs on unseen CT images in approximately 7 seconds per patient. High accuracy and short contouring time allow for the implementation of the model within a clinical ART workflow, which would lead to a significant decrease in the time required to create a new adapted treatment plan. The objective of the fourth study was to use artificial intelligence methods to build a decision making support system that would classify previously delivered plans of brain tumor patients into those that met treatment planning objectives and those for which objectives were not met due to the priority given to one or more organs-at-risk. Among evaluated machine learning algorithms, the Logistic Regression model achieved the highest accuracy and can be used by radiation oncologists to support their decision-making process in terms of treatment plan adaptations and plan approvals in a data-driven quality assurance program.
Machine Learning In Radiation Oncology
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Author : Issam El Naqa
language : en
Publisher: Springer
Release Date : 2015-06-19
Machine Learning In Radiation Oncology written by Issam El Naqa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Medical categories.
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Artificial Intelligence In Medicine
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Author : David Riaño
language : en
Publisher: Springer
Release Date : 2019-06-19
Artificial Intelligence In Medicine written by David Riaño and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-19 with Computers categories.
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Who List Of Priority Medical Devices For Cancer Management
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Author : World Health Organization
language : en
Publisher:
Release Date : 2017-05-09
Who List Of Priority Medical Devices For Cancer Management written by World Health Organization and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-09 with Medical instruments and apparatus categories.
This is the model list and clearing house of appropriate, basic, and priority medical devices based on the list of clinical interventions selected from clinical guidelines on prevention, screening, diagnosis, treatment, palliative care, monitoring, and end of life care. This publication addresses medical devices that can be used for the management of cancer and specifically describes medical devices for six types of cancer: breast, cervical, colorectal, leukemia, lung, and prostate. This book is intended for ministries of health, public health planners, health technology managers, disease management, researchers, policy makers, funding, and procurement agencies and support and advocacy groups for cancer patients.
Machine Learning With Health Care Perspective
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Author : Vishal Jain
language : en
Publisher: Springer Nature
Release Date : 2020-03-09
Machine Learning With Health Care Perspective written by Vishal Jain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-09 with Technology & Engineering categories.
This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
Artificial Intelligence In Medical Imaging
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Author : Erik R. Ranschaert
language : en
Publisher: Springer
Release Date : 2019-01-29
Artificial Intelligence In Medical Imaging written by Erik R. Ranschaert and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with Medical categories.
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.
Artificial Intelligence In Medicine
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Author : Lei Xing
language : en
Publisher: Academic Press
Release Date : 2020-09-03
Artificial Intelligence In Medicine written by Lei Xing and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-03 with Medical categories.
Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. Provides history and overview of artificial intelligence, as narrated by pioneers in the field Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach