87,000 research outputs found
Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach.
No full textIn this paper, we present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines. GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques at different levels in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed, providing a set of representation primitives. Second, a user-friendly acquisition tool has been designed and implemented, on the basis of the knowledge representation formalism. The acquisition tool provides various forms of help for the expert physicians, including different levels of syntactic and semantic tests in order to check the well-formedness of the guidelines being acquired. Third, a tool for executing guidelines on a specific patient has been made available. The execution module provides a hypothetical reasoning facility, to support physicians in the comparison of alternative diagnostic and/or therapeutic strategies. Moreover, advanced and extended AI techniques for temporal reasoning and temporal consistency checking are used both in the acquisition and in the execution phase. The GLARE approach has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure
Uncertainty Analysis of Feature Extraction from Expired Gas Traces
No full textNoninvasive medical analyses are a convenient method to study several pathologies even though their indirect nature often requires a complex processing to determine the relevant health "indicators". The usefulness of such indicators depends on the employed model, but also on the uncertainty that is connected to the complex processing involved in the indicator determination. This paper deals with the problems related to the estimation of the uncertainty when the indicators are computed by means of a nontrivial processing on recorded traces of clinical parameters. The paper is focused on the analysis of expired gas traces, but the procedure can also be applied to many other cases where the processing involves manual or automatic selection of suitable "key points" on repetitive traces
Integral Equation-Based Topology Optimization of an EMI Filter
No full textTopology Optimization (TO) stands as a powerful instrument for elevating the design paradigm of electromagnetic devices, particularly with the integration of advanced additive manufacturing methodologies. Through the coupling of the integral equation method and binary Topology Optimization, a tool is proposed for the purposeful design of filters tailored to efficaciously mitigate Electromagnetic Interference (EMI)
Comparative Analysis of Hierarchical Matrix Formats for Electromagnetic Device Modeling: A Preliminary Study
No full textThis paper investigates the performance of three hierarchical matrix (H-matrix) formats for modeling electromagnetic devices using the Electric Field Integral Equation (EFIE) and the Augmented EFIE (A-EFIE) formulation. These methods are applied to a benchmark problem, the single-ended microstrip transmission line, to evaluate their efficiency in terms of memory usage and accuracy
TABELLE di Termodinamica Applicata e Trasmissione del Calore. Proprietà delle sostanze di uso frequente
No full textSi tratta di un volume didattico di supporto agli insegnamenti di Termodinamica applicata, Trasmissione del calore e Fisica Tecnica per gli allievi dei corsi di ingegneria. Nel volume sono raccolte le proprietà termodinamiche e termofisiche per una selezione di materiali utili per affrontare un significativo numero di casi applicativi all'interno dei sopracitati insegnament
Hierarchical Matrices Accelerated Topology Optimization of Patch Antenna
No full textIntegral equation method-based topology optimization (IEM-TO) may result in excessive computational burden due to the fully populated matrices generated during the discretization process. To extend the applicability of IEM-TO to large-scale problems, with many degrees of freedom, hierarchical matrices (H-matrices) can be used to drastically reduce the overall computational burden. Exploiting this concept, high-resolution topologies can be obtained at the end of the optimization process
Notes on the biology of Hesperapis regularis (Cresson) (Hymenoptera: Melittidae)
No full textThis investigation started with the discovery of numerous Hesperapis regularis (Cresson) adults in May 1955, by P. F. Torchio and D. W. Ribble, within a restricted area near the Arroyo Seco Campground, Monterey Co., California. During the ensuing years the authors have made observations on the general activity around the nesting area, the sleeping habits, burrow and cell construction, flower relationships, and mating attempts. In addition larvae were collected for study and comparisons made with the larval description of H. ruficeps (Ashmead) (Michener 1953)
Energy-Exergy, Environmental and Economic Criteria in Combined Heat and Power (CHP) Plants: Indexes for the Evaluation of the Cogeneration Potential
No full textIn the first part of this work, combined heat and power (CHP) criteria pertaining to energy, exergy, environmental (pollutant emission) and economic aspects, have been investigated and compared. Although the constraints in legislation usually refer to energy efficiency, primary energy savings and greenhouse gas savings, other criteria should also be taken into account in order to obtain a better evaluation of a cogeneration plant. Here particular attention has been paid to saving indexes for both an individual CHP-unit and for a CHP-system, that is the complete system with all the cogeneration units and the auxiliary plants necessary to cover the users’ demand. Five indexes, named potential indexes, have been introduced to evaluate the cogeneration potential: one for energy saving, one for exergy, two for environmental aspects (global and local scale) and one for economic aspects. Finally, some indexes analysed in the paper have been applied to a case study concerning a district heating cogeneration system, and the different behaviour of the energy-exergy, environmental and economic aspects has been discussed
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
No full textBackground and objective:
Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes.
Methods:
We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts.
Results:
Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://torchio.rtfd.io/. The package can be installed from the Python Package Index (PyPI) running pip install torchio. It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
Conclusion:
TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment reproducibility and is version-controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with medical images
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