With high performance and an intuitive interactive user interface, OsiriX is the most widely used DICOM viewer in the world. It is the result of more than 10 years of research and development in digital imaging. It fully supports the DICOM standard for an easy integration in your workflow environment and an open platform for development of processing tools. It offers advanced post-processing techniques in 2D and 3D, exclusive innovative technique for 3D and 4D navigation and a complete integration with any PACS. OsiriX supports 64-bit computing and multithreading for the best performances on the most modern processors. OsiriX MD, the commercial version, is certified for medical use (FDA cleared and CE II labeled).
Medical Image Viewer (MIView) is a simple image viewer for Dicom, NEMA, Papyrus, Analyze 7.5, Nifti 1, and raster images, including JPEG, GIF, TIFF, PNG, and BMP.
. Kamada, Kohji; Hamada, Yasuji 1982-01-01 A new reacting plasma machine is designed, and will be constructed at the Institute of Plasma Physics, Nagoya University. It is important to avoid the activation of the materials for the machine, accordingly, aluminum alloy has been considered as the material since the induced activity of aluminum due to 14 MeV neutrons is small. The vacuum chamber of the new machine consists of four modules, and the remote control of each module is considered. However, the cost of the remote control of modules is expensive. To minimize the dependence on the remote control, the use of aluminum alloy is considered as the first step. The low electrical resistivity, over-ageing, weak mechanical strength and eddy current characteristics of aluminum alloy must be improved.
The physical and electrical properties of various aluminum alloys have been investigated. Permeability of hydrogen through aluminum, the recycling characteristics and surface coating materials have been also studied. (Kato, T.). Weng, Wei-Hung; Wagholikar, Kavishwar B; McCray, Alexa T; Szolovits, Peter; Chueh, Henry C 2017-12-01 The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets.
The convolutional recurrent neural network with neural word embeddings trained- medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier using hybrid bag-of-words and clinically relevant UMLS concepts as the feature representation, with term frequency-inverse document frequency (tf-idf)-weighting, outperformed other shallow learning classifiers on iDASH and MGH datasets with AUC of 0.957 and 0.964, and F1 scores of 0.932 and 0.934 respectively. We trained classifiers on one dataset, applied to the other dataset and yielded the threshold of F1 score of 0.7 in classifiers for half of the medical subdomains we studied.
Our study shows that a supervised. Lavesson, N. 2010-01-01 This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning. Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng 2016-01-01 Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance.
To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on- machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on- machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program.
With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results. Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela 2018-01-01 Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs.
We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008).
Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo. Assmann, R W 2012-01-01 This document summarizes the talks and discussion that took place in the second session of the Chamonix 2012 workshop concerning results from machine studies performed in 2011. The session consisted of the following presentations: “LHC experience with different bunch spacings” by G. Rumolo; “Observations of beam-beam effects in MDs in 2011” by W. Herr; “Beam-induced heating/ bunch length/RF and lessons for 2012” by E. Metral; “Lessons in beam diagnostics” by R.
Jones; “Quench margins” by M. Sapinski; “First demonstration with beam of the Achromatic Telescopic Squeeze (ATS)” by S. Assmann, R W; Papotti, G European Organization for Nuclear Research, Geneva (Switzerland) 2012-07-01 This document summarizes the talks and discussion that took place in the second session of the Chamonix 2012 workshop concerning results from machine studies performed in 2011. The session consisted of the following presentations: “LHC experience with different bunch spacings” by G. Rumolo; “Observations of beam-beam effects in MDs in 2011” by W.
Herr; “Beam-induced heating/ bunch length/RF and lessons for 2012” by E. Metral; “Lessons in beam diagnostics” by R. Jones; “Quench margins” by M. Sapinski; “First demonstration with beam of the Achromatic Telescopic Squeeze (ATS)” by S. (author). Sismondo, Sergio 2009-04-01 Publication of pharmaceutical company-sponsored research in medical journals, and its presentation at conferences and meetings, is mostly governed by 'publication plans' that extract the maximum amount of scientific and commercial value out of data and analyses through carefully constructed and placed papers.
Clinical research is typically performed by contract research organizations, analyzed by company statisticians, written up by independent medical writers, approved and edited by academic researchers who then serve as authors, and the whole process organized and shepherded through to journal publication by publication planners. This paper reports on a conference of an international association of publication planners. It describes and analyzes their work in an ecological framework that relates it to marketing departments of pharmaceutical companies, medical journals and publishers, academic authors, and potential audiences. The medical research described here forms a new kind of corporate science, designed to look like traditional academic work, but performed largely to market products.
Brattain, Laura J; Telfer, Brian A; Dhyani, Manish; Grajo, Joseph R; Samir, Anthony E 2018-04-01 Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances.
We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization. Foster, Kenneth R; Koprowski, Robert; Skufca, Joseph D 2014-07-05 A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique. Chen, Kan; Stafford, Frank P.
A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. ( Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as. Mindock, J.; Myers, J.; Latorella, K.; Cerro, J.; Hanson, A.; Hailey, M.; Middour, C. 2018-01-01 ExMC is creating an ecosystem of tools to enable well-informed medical system trade studies. The suite of tools address important system implementation aspects of the space medical capabilities trade space and are being built using knowledge from the medical community regarding the unique aspects of space flight. Two integrating models, a systems engineering model and a medical risk analysis model, tie the tools together to produce an integrated assessment of the medical system and its ability to achieve medical system target requirements. This presentation will provide an overview of the various tools that are a part of the tool ecosystem.
Initially, the presentation's focus will address the tools that supply the foundational information to the ecosystem. Specifically, the talk will describe how information that describes how medicine will be practiced is captured and categorized for efficient utilization in the tool suite. For example, the talk will include capturing what conditions will be planned for in-mission treatment, planned medical activities (e.g., periodic physical exam), required medical capabilities (e.g., provide imaging), and options to implement the capabilities (e.g., an ultrasound device). Database storage and configuration management will also be discussed. The presentation will include an overview of how these information tools will be tied to parameters in a Systems Modeling Language (SysML) model, allowing traceability to system behavioral, structural, and requirements content.
The discussion will also describe an HRP-led enhanced risk assessment model developed to provide quantitative insight into each capability's contribution to mission success. Key outputs from these various tools, to be shared with the space medical and exploration mission development communities, will be assessments of medical system implementation option satisfaction of requirements and per-capability contributions toward achieving requirements.