651,696 research outputs found
Tongue Image Analysis for Diabetes Mellitus Diagnosis Based on SOM Kohonen
Tongue diagnosis is an important diagnostic method for
evaluating the condition of internal organ by looking at
the image of tongue . However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular
digital image processing techniques. Then, SOM
Kohonen are employed to model the relationship
between these quantitative features and diseases. The
effectiveness of the method is tested on 35 patients affected by Diabetes Mellitus as well as other 30 healthy volunteers, and the diagnostic results predicted by the previously trained SOM Kohonen classifiers are compared with the HOMA-B
Generating time series reference models based on event analysis
Creating a reference model that represents a given set of time series is a relevant problem as it can be applied to a wide range of tasks like diagnosis, decision support, fraud detection, etc. In some domains, like seismography or medicine, the relevant information contained in the time series is concentrated in short periods of time called events. In this paper, we propose a technique for generating time series reference models based on the analysis of the events they contain. The proposed technique has been applied to time series from two medical domains: Electroencephalography, a neurological procedure to record the electrical activity produced by the brain and Stabilometry, a branch of medicine studying balance-related functions in human beings
Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
The mechanical characterization of biological samples is a fundamental issue in biology
and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases.
In this paper, a novel approach for the identification of the stiffness and damping coefficients
of biosamples is introduced. According to the proposed method, a MEMS-based microgripper
in operational condition is used as a measurement tool. The mechanical model describing the
dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper,
and the KelvinâVoigt constitutive law of viscoelasticity for the sample. Then, two algorithms based
on recursive least square (RLS) methods are implemented for the estimation of the mechanical
coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS
algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach.
Results confirm the feasibility of the method that enables the ability to perform simultaneously two
tasks: sample manipulation and parameters identification
Based On K-means Disease Diagnosis Research
For the diagnosis of diseases, modern medicine usually searches for diseases in the disease database to find the type of disease that matches them. The diagnosis of diseases is the first step in treatment. Then the classification of diseases is the basis of disease diagnosis. Disease classification plays an extremely important role in the scientific management of medical records and the development of modern medicine, and is a bridge connecting modern medical science. Therefore, the classification of diseases is very necessary. Based on this, this article establishes a K-means model for disease diagnosis, and combines the internationally unified disease type code ICD statistics table to classify the sample data set into infectious and parasitic diseases, tumors, diabetes and circulatory diseases The training is perfect, and finally the diagnosis classification of the disease is realized
Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine
Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)
From laboratory bench to benchmark: technology transfer in laboratory medicine
Background: Life Sciences research, enhancing the occurrence of innovation, is able to impact clinical decision-making, both at diagnosis and therapy. Indeed, starting from the knowledge of specific needs and of technical-scientific demands, researchers can conceive and experiment innovative solutions. Despite these strengths, transferring research to the market in Life Sciences shows considerable criticalities. The aim of this paper is to provide concrete evidences on the processes of technology transfer based on the exploitation of the results obtained by KronosDNAsrl, an academic spin-off focused on reproductive medicine.
Methods: Different tools were used to evaluate the technical feasibility (validation of the results obtained with the prototype) and to manage the technology transfer process of One4TwoÂź.
Results: The different analyses we carried out showed the feasibility of the proposed solution. As a result, the One4TwoÂź prototype has been developed and validated.
Conclusions: Here, we provide a strength of evidences on how knowledge obtained by translational research on "bench" can be used to be transferred to the market on "benchmark" enabling innovation in Laboratory Medicine. In addition, the model described for One4TwoÂź can be easily transferred to other products
Integrative AI-Driven Strategies for Advancing Precision Medicine in Infectious Diseases and Beyond: A Novel Multidisciplinary Approach
Precision medicine, tailored to individual patients based on their genetics,
environment, and lifestyle, shows promise in managing complex diseases like
infections. Integrating artificial intelligence (AI) into precision medicine
can revolutionize disease management. This paper introduces a novel approach
using AI to advance precision medicine in infectious diseases and beyond. It
integrates diverse fields, analyzing patients' profiles using genomics,
proteomics, microbiomics, and clinical data. AI algorithms process vast data,
providing insights for precise diagnosis, treatment, and prognosis. AI-driven
predictive modeling empowers healthcare providers to make personalized and
effective interventions. Collaboration among experts from different domains
refines AI models and ensures ethical and robust applications. Beyond
infections, this AI-driven approach can benefit other complex diseases.
Precision medicine powered by AI has the potential to transform healthcare into
a proactive, patient-centric model. Research is needed to address privacy,
regulations, and AI integration into clinical workflows. Collaboration among
researchers, healthcare institutions, and policymakers is crucial in harnessing
AI-driven strategies for advancing precision medicine and improving patient
outcomes
Bayesian networks for disease diagnosis: What are they, who has used them and how?
A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem,
used to show dependencies or cause-and-effect relationships between variables.
They are widely applied in diagnostic processes since they allow the
incorporation of medical knowledge to the model while expressing uncertainty in
terms of probability. This systematic review presents the state of the art in
the applications of BNs in medicine in general and in the diagnosis and
prognosis of diseases in particular. Indexed articles from the last 40 years
were included. The studies generally used the typical measures of diagnostic
and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the
area under the ROC curve. Overall, we found that disease diagnosis and
prognosis based on BNs can be successfully used to model complex medical
problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape
Fast reconstruction of 3D blood flows from Doppler ultrasound images and reduced models
This paper deals with the problem of building fast and reliable 3D
reconstruction methods for blood flows for which partial information is given
by Doppler ultrasound measurements. This task is of interest in medicine since
it could enrich the available information used in the diagnosis of certain
diseases which is currently based essentially on the measurements coming from
ultrasound devices. The fast reconstruction of the full flow can be performed
with state estimation methods that have been introduced in recent years and
that involve reduced order models. One simple and efficient strategy is the
so-called Parametrized Background Data-Weak approach (PBDW). It is a linear
mapping that consists in a least squares fit between the measurement data and a
linear reduced model to which a certain correction term is added. However, in
the original approach, the reduced model is built a priori and independently of
the reconstruction task (typically with a proper orthogonal decomposition or a
greedy algorithm). In this paper, we investigate the construction of other
reduced spaces which are built to be better adapted to the reconstruction task
and which result in mappings that are sometimes nonlinear. We compare the
performance of the different algorithms on numerical experiments involving
synthetic Doppler measurements. The results illustrate the superiority of the
proposed alternatives to the classical linear PBDW approach
Vaccination is a suitable tool in the control of Aujeszky's disease outbreaks in pigs using a Population Dynamics P systems model
Aujeszky's disease is one of the main pig viral diseases and results in considerable economic losses in the pork production industry. The disease can be controlled using preventive measures such as improved stock management and vaccination throughout the pig-rearing period. We developed a stochastic model based on Population Dynamics P systems (PDP) models for a standard pig production system to differentiate between the effects of pig farm management regimes and vaccination strategies on the control of Aujeszky's disease under several different epidemiological scenarios. Our results suggest that after confirming the diagnosis, early vaccination of most of the population (>75%) is critical to decrease the spread of the virus and minimize its impact on pig productivity. The direct economic cost of an outbreak of Aujeszky's disease can be extremely high on a previously uninfected farm (from 352-792 Euros/sow/year) and highlights the positive benefits of investing in vaccination measures to control infections. We demonstrate the usefulness of computational models as tools in the evaluation of preventive medicine programs aimed at limiting the impact of disease on animal production.This work was partially supported by FEDER project COMRDI16-1-0035-03
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