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Artificial Intelligence in Radiation Therapy
This textbook covers a basis of mathematical algorithm in artificial intelligence and clinical adaptation and contribution of AI in radiotherapy. More experienced practitioners and researchers and members of medical physics communities, such as AAPM, ASTRO, and ESTRO, would find this book extremely useful.
eBook
Inside Mathforum.org : analysis of an Internet-based education community
The internet has dramatically transformed social space and time for many people in many different contexts. This dramatic warping of the social fabric has happened slowly over time as digital technologies have evolved and internet speeds have increased. While we are all aware of these changes, the impact is often little understood. There are few monographs about social groups made possible by the internet, and even fewer about educational communities made possible through digital technologies. 'Inside Mathforum.org' details the ways that digital media are used to enhance the practices that teachers and students of mathematics engage in. The book also shows how different kinds of mathematical conversations and interactions become possible through the digital media. Unlike many other educational uses of digital media, the Math Forum's community has provided online resources and sustained support for teachers and students, and it leads the way in showing the power of digital media for education.
Book
Monte Carlo Calculations in Nuclear Medicine (Second Edition)
The book provides a review of concepts and methodologies developed and adopted for quantitative imaging-guided radiation dosimetry calculations in targeted radionuclide. It also provides an overview of model design of anthropomorphic computational models and software packages developed for Monte Carlo-based dosimetry calculations.
eBook
Individual differences in online and computer-based learning : gifted and other diverse populations
\"In 1894 John Dewey established his experimental laboratory school at the University of Chicago, with a focus on teaching each student according to their individual differences. This concept indicated a shift away from the emphasis on communal, classroom teaching, which marked educational practices in the nineteenth century during the advent of widely available public education. With the introduction of computer-based online instruction in schools, curricula are able to be fully informed by individual difference, subtly and quickly tracking students' progress. In these courses, teachers play the role of troubleshooters instead of lecturers. Individual Differences examines a large number of studies on computer-based and online instruction, with special attention paid to gifted students in the fields of mathematics, science, technology, and engineering. Other chapters also focus on a wide variety of student populations: deaf students, American Indian rural students, and underachieving, impoverished students. \"-- Provided by publisher.
Book
Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors
The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults.
Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session.
Significant MRI site and vendor effects (p < .05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p < 1.39E-36). In particular, volumes larger than 200 mm3 (for amygdalar nuclei) and 300 mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε < 5% and DICE > 0.80).
Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.
•Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.
Journal Article
Implant Restorations
The fourth edition of Implant Restorations: A Step-by-Step Guide provides a wealth of updated and expanded coverage on detailed procedures for restoring dental implants. Focusing on the most common treatment scenarios, it offers concise literature reviews for each chapter and easy-to-follow descriptions of the techniques, along with high-quality clinical photographs demonstrating each step.
Comprehensive throughout, this practical guide begins with introductory information on incorporating implant restorative dentistry in clinical practice. It covers diagnosis and treatment planning and digital dentistry, and addresses advances in cone beam computerized tomography (CBCT), treatment planning software, computer generated surgical guides, rapid prototype printing and impression-less implant restorative treatments, intra-oral scanning, laser sintering, and printing/milling polymer materials. Record-keeping, patient compliance, hygiene regimes, and follow-up are also covered.
* Provides an accessible step-by-step guide to commonly encountered treatment scenarios, describing procedures and techniques in an easy-to-follow, highly illustrated format
* Offers new chapters on diagnosis and treatment planning and digital dentistry
* Covers advances in cone beam computerized tomography (CBCT), computer generated surgical guides, intra-oral scanning, laser sintering, and more
An excellent and accessible guide on a burgeoning subject in modern dental practice by one of its most experienced clinicians, Implant Restorations: A Step-by-Step Guide, Fourth Edition will appeal to prosthodontists, general dentists, implant surgeons, dental students, dental assistants, hygienists, and dental laboratory technicians.
eBook
Computer corpora and open source software for language learning : emerging research and opportunities
\"This book explores the use of free open source software (NoSketch Engine) for learning the Croatian, English, and German languages in Croatian primary and secondary schools\"-- Provided by publisher.
Book
Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including a computer-aided diagnosis are desired. This study aims to classify histopathological images of malignant lymphomas through deep learning, which is a computer algorithm and type of artificial intelligence (AI) technology. We prepared hematoxylin and eosin (H&E) slides of a lesion area from 388 sections, namely, 259 with diffuse large B-cell lymphoma, 89 with follicular lymphoma, and 40 with reactive lymphoid hyperplasia, and created whole slide images (WSIs) using a whole slide system. WSI was annotated in the lesion area by experienced hematopathologists. Image patches were cropped from the WSI to train and evaluate the classifiers. Image patches at magnifications of ×5, ×20, and ×40 were randomly divided into a test set and a training and evaluation set. The classifier was assessed using the test set through a cross-validation after training. The classifier achieved the highest levels of accuracy of 94.0%, 93.0%, and 92.0% for image patches with magnifications of ×5, ×20, and ×40, respectively, in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia. Comparing the diagnostic accuracies between the proposed classifier and seven pathologists, including experienced hematopathologists, using the test set made up of image patches with magnifications of ×5, ×20, and ×40, the best accuracy demonstrated by the classifier was 97.0%, whereas the average accuracy achieved by the pathologists using WSIs was 76.0%, with the highest accuracy reaching 83.3%. In conclusion, the neural classifier can outperform pathologists in a morphological evaluation. These results suggest that the AI system can potentially support the diagnosis of malignant lymphoma.
This study aims to classify histopathological images of malignant lymphoma through deep learning. The classifier achieved the high levels of accuracy in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia, which were higher than those of pathologists. Artificial intelligence can potentially support diagnosis of malignant lymphoma.
Journal Article