96,075 research outputs found
Decision support system for cardiovascular problems
The DISHEART project aims at developing a new computer based decision support system (DSS) integrating medical image data, modelling, simulation, computational Grid technologies and artificial intelligence methods for assisting clinical diagnosis and intervention in cardiovascular problems. The RTD goal is to improve and link existing state of the art technologies in order to build a computerised cardiovascular model for the analysis of the heart and blood vessels. The resulting DISHEART DSS interfaces computational biomechanical analysis tools with the information coming from multimodal medical images. The computational model is coupled to an artificial neural network (ANN) based decision model that can be educated for each particular patient with data coming from his/her images and/or analyses. The DISHEART DSS system is validated in trials of clinical diagnosis, surgical intervention and subject-specific design of medical devices in the cardiovascular domain. The DISHEART DSS also contributes to a better understanding of cardiovascular morphology and function as inferred from routine imaging examinations. Four reputable medical centers in Europe took an active role in the validation and dissemination of the DISHEART DSS as well as the elaboration of computational material and medical images. The integrated DISHEART DSS supports health professionals in taking promptly the best possible decision for prevention, diagnosis and treatment. Emphasis was put in the development of userfriendly, fast and reliable tools and interfaces providing access to heterogeneous health information sources, as well as on new methods for decision support and risk analysis. The use of Grid computing technology is essential in order to optimise and distribute the heavy computational work required for physical modelling and numerical simulations and especially for the parametric analysis required for educating the DSS for every particular application. The four end user SMEs participating in the project benefits from the new DISHEART DSS. The companies COMPASS, QUANTECH and Heartcore will market the DSS among public and private organizations related to the cardiovascular field. EndoArt will exploit the DISHEART DSS as a support for enhanced design and production of clinical devices. The partnership was sought in order to gather the maximum complementary of skills for the successful development of the project Disheart DSS, requiring experts in Mechanical sciences, Medical sciences, Informatic, and FEM technique to grow up the testes.Postprint (published version
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Analysis of Three-Dimensional Protein Images
A fundamental goal of research in molecular biology is to understand protein
structure. Protein crystallography is currently the most successful method for
determining the three-dimensional (3D) conformation of a protein, yet it
remains labor intensive and relies on an expert's ability to derive and
evaluate a protein scene model. In this paper, the problem of protein structure
determination is formulated as an exercise in scene analysis. A computational
methodology is presented in which a 3D image of a protein is segmented into a
graph of critical points. Bayesian and certainty factor approaches are
described and used to analyze critical point graphs and identify meaningful
substructures, such as alpha-helices and beta-sheets. Results of applying the
methodologies to protein images at low and medium resolution are reported. The
research is related to approaches to representation, segmentation and
classification in vision, as well as to top-down approaches to protein
structure prediction.Comment: See http://www.jair.org/ for any accompanying file
Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications
This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone’s edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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