Application In Medical Domain Research Articles

Green synthesis of multifunctional carbon dots from Crataegi Fructus for pH sensing, cell imaging and hemostatic effects

Multifunctional carbon dots with intrinsic biological activity and fluorescent properties exhibit tremendous potential for applications in various fields, especially in the biomedical field. This paper reports a green and facile method for the synthesis of multifunctional carbon dots (CFCDs) via pyrolysis using the herbal medicine Crataegi Fructus as a precursor, and the fluorescent properties and biological activities of CFCDs are investigated, while the corresponding mechanisms are explored. The obtained CFCDs present a graphitic structure with an average particle size of 2.35 nm, abundant functional groups on their surface, and possess high aqueous solubility and excellent stability. Furthermore, under acidic conditions, these CFCDs have pH-dependent fluorescence properties based on protonation and deprotonation, showing a high linear correlation with pH values ranging from 0.5 to 4.5. The low cytotoxicity of CFCDs and their efficient internalization by cells make them ideal for cell imaging. More importantly, CFCDs exert good hemostatic effects through the extrinsic and common pathway and promote platelet aggregation. In conclusion, CFCDs can simultaneously realize pH sensing, cell imaging, and hemorrhagic disease treatment and, thus, have potential for wide application in medical domain.

Artificial Intelligent Algorithm Methodology for the Human Brain Image Segmentation & Classifier in the Medical Application Domain

Artificial Neural Network has the application in classification domain. Intelligent algorithm will work in the practices where human can control the anger & it will work in the segmentation domain to check the level of the anger. Intelligent algorithm will extract the images of human brains for certain region where the brain image will classified in the certain area and the classifier will the area for the stimulation of the anger. Intelligent algorithm will give the solution on the segmentation & classification methods to reduce the anger of human being. The methodology is the classification & segmentation of human brain images. Key Words — Intelligent Algorithm, Human Brain Image, Segmentation, Classifier

Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

A wrist-based photoplethysmography (PPG) tool offers a simple and non-invasive approach for applications in vital sign monitoring and healthcare. However, in a telecare network, these physiological signals ensure the authorisation demands for the domain of medical applications. Hence, this study proposes an intelligent method for PPG signal encryption and decryption. This method combines chaotic map and radial basis function network (RBFN) into symmetric cryptography with an adaptive scheme. The sine-power chaotic map is used as a key generator of 256 non-ordered numbers (key space) to set the private cipher codes. Two RBFNs are used to train an encryptor and a decryptor with the authorised cipher codes. Its substitution-based infosecurity scheme can change the numerical values of PPG raw data for each encrypted communication, and the decrypted PPG data are further applied for time- and frequency-domain analyses in clinical applications, such as heart and respiration rate analysis and arterial stiffness and upper extremity vascular disease evaluations. Through experimental results, the security levels are validated using the number of pixel change rate (NPCR), unified averaged changed intensity (UACI), and correlation analysis. The average NPCR, UACI, and correlation coefficient (CC) are 96.57%, 35.43%, and 0.005, respectively, between the plain PPG and the encrypted PPG data against hacker attacks. The larger-the-better of NPCR and UACI indexes and the smaller-the-better of CC index are obtained to evaluate the efficiency of the proposed cryptography method. The encrypted PPG also guarantees physiological signals of good quality in clinical applications. In addition, the performance of RBFN-based method is superior in adaptive learning capability than that of the traditional learning method in real-time applications.

NLP Algorithms Endowed f or Automatic Extraction of Information from Unstructured Free Text Reports of Radiology Monarchy

Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations, various NLP algorithms are used . A rule-based NLP system is used in most of the automated IE applications in medical domain; whereas some applications are using Random Forest classifier, PageRank Algorithm, clustering algorithm, Conditional Random Fields (CRF) algorithm, and deep learning-based approaches. Some papers found with methods used for IE like, Support Vector Machines (SVMs), linear-chain conditional random fields (LC-CRFs), k-means or k-medoids algorithm ,Affinity Propagation (AP) clustering algorithm , supervised machine learning algorithm and many more. Thus through this survey we can say that, NLP methods used to extract information ,brings new insights into already known clinical evidences. It also helps to identify previously unknown treatment and causal relations between biomedical entities .Therefore NLP algorithms has empowered Radiology monarchy.

Facile preparation and strong adhesive strength of honeycomb polyurethane films with small pore diameter

AbstractWith the continuous development of bionics, such as, geckos and virginia creeper with both superhydrophobic and super‐adhesive, the surface wetting and super‐adhesive properties of various porous materials have attracted extensive attention of the scientific and medical communities. Here, the honeycomb polyurethane (PU) porous films with strong adhesion were successfully prepared by microphase separation method and the effects of growth parameters on their microstructure and adhesive strength to ice were investigated. It was found that a high relative humidity (e.g., 100%) and a low solution concentration (e.g., 2%) facilitated the formation of ordered honeycomb PU porous films, and as‐prepared PU pores with average pore diameter as small as 5 μm are better ordered and more uniform than these in related documents. Although the contact angle of water droplets on the surface of PU porous films increased from the premodification value of 85–130° to more than 160° after surface modification with polydopamine (PDA), the corresponding rolling angle remained approximately constant (180°), indicating that the surface of PU porous films has strong adhesion similar to geckos and virginia creeper. Furthermore, at lower temperature, the PU porous films exhibited the high adhesive strength of 142.13 kPa on ice, which was strongly dependent on the porous microstructures and surface compositions. The improved adhesive behavior to ice of honeycomb PU porous films modified with PDA provides new strategies for surface modification of materials and potential applications in medical domain.

Biosynthesized Silver Nanoparticles Using an Aqueous Root Extract of Iris germanica as a Reducing Agent and Its Antibacterial Efficacy

Biosynthesized silver nanoparticles (AgNPs) are environment friendly, cost-effective, bio-compatible and expanding research area due to their potential applications in medical domain. The present study focuses on biologically synthesized of AgNPs using aqueous root extract of Iris germanica as reducing agent as well as capping agent and examined their potential antibacterial efficacy. In reduction reaction it was observed that silver (Ag+1) ions associate in root extract and reduced in solution (Ag0) leads to formation of stable formation of spherical AgNPs. Biosynthesized AgNPs showed an effective and rapid antibacterial activity against both bacterial strain (gram+ve and gram-ve). Results exhibited that AgNPs were extremely toxic towards Bacillus subtilis and Escherichia coli pathogenic bacterial strains and can be utilized for the applications in biomedical science.

Bio Synthesis and Characterization of Silver Nanoparticles by Leaf Extracts of Moringa Oleifere Leaf, Azardica Indica (Neem) Leaf, Bamboo Leaf, and their Antibacterial Activity

Abstract Silver nanoparticles (AgNps) have a number of applications in medical domain. Since these nanoparticles have been used for infection, prevention in the Medical study. The plant leaves extracts are used in biosynthesis of nanostructure materials can be non-toxic & cost effective; it may be more relevant to reduce their size. A Green rapid biogenic synthesis of silver nanoparticles using moringa oleifere leaf extract to compare with A.indica leaf & bamboo leaf aqueous extracts was demonstrated in some of the cases. The obtained AgNps were synthesized using M. oleifere leaf extract have been characterized by X ray diffraction (XRD) analysis shows that particles average size 28 .45 nanometer, Fourier transform infrared spectroscopy (FTIR) studies suggests that the present of function group , Energy dispersive X-ray spectroscopy (EDS) results showed the compositional mass ratio, UV-visible spectrum reveals that resonance band at ∼ 420 nm. Scanning electron microscope (SEM) shows that the morphology of AgNps can be controlled by using different leaf extracts. The bacterial activity was evaluated using E-Coli and S.Aureus.from the disc diffusion results, the synthesized AgNps shows an excellent antibacterial property and compared to the different leaves extracts.

Research Review of Knowledge Graph and Its Application in Medical Domain

Research Review of Knowledge Graph and Its Application in Medical Domain

A comprehensive survey on content based image retrieval system and its application in medical domain

Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.

Fine-grained leukocyte classification with deep residual learning for microscopic images

Background and objective: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories.Methods: Based on deep learning theory, a systematic study is conducted on finer leukocyte classification in this paper. A deep residual neural network based leukocyte classifier is constructed at first, which can imitate the domain expert’s cell recognition process, and extract salient features robustly and automatically. Then the deep neural network classifier’s topology is adjusted according to the prior knowledge of white blood cell test. After that the microscopic image dataset with almost one hundred thousand labeled leukocytes belonging to 40 categories is built, and combined training strategies are adopted to make the designed classifier has good generalization ability.Results: The proposed deep residual neural network based classifier was tested on microscopic image dataset with 40 leukocyte categories. It achieves top-1 accuracy of 77.80%, top-5 accuracy of 98.75% during the training procedure. The average accuracy on the test set is nearly 76.84%.Conclusions: This paper presents a fine-grained leukocyte classification method for microscopic images, based on deep residual learning theory and medical domain knowledge. Experimental results validate the feasibility and effectiveness of our approach. Extended experiments support that the fine-grained leukocyte classifier could be used in real medical applications, assist doctors in diagnosing diseases, reduce human power significantly.

Classification of Imbalanced Malaria Disease Using Naïve Bayesian Algorithm

Malaria disease is one whose presence is rampant in semi urban and non-urban areas especially resource poor developing countries. It is quite evident from the datasets like malaria, dengue, etc., where there is always a possibility of having more negative patients (non-occurrence of the disease) compared to patients suffering from disease (positive cases). Developing a model based decision support system with such unbalanced datasets is a cause of concern and it is indeed necessary to have a model predicting the disease quite accurately. Classification of imbalanced malaria disease data become a crucial task in medical application domain because most of the conventional machine learning algorithms are showing very poor performance to classify whether a patient is affected by malaria disease or not. In imbalanced data, majority (unaffected) class samples are dominates the minority (affected) class samples leading to class imbalance. To overcome the nature of class imbalance problem, balancing the data samples is the best solution which produces the better accuracy in classification of minority samples. The aim of this research is to propose a comparative study on classifying the imbalanced malaria disease data using Naive Bayesian classifier in different environments like weka and using an R-language. We present here, clinical descriptive study on 165 patients of different age group people collected at medical wards of Narasaraopet from 2014-17. Synthetic Minority Oversampling Technique (SMOTE) technique has been used to balance the class distribution and then we performed a comparative study on the dataset using Naïve Bayesian algorithm in various platforms. Out of balanced class distribution data, 70% data was given to train the Naive Bayesian algorithm and the rest of the data was used for testing the model for both weka and R programming environments. Experimental results have indicated that, classification of malaria disease data in weka environment has highest accuracy of 88.5% than the Naive Bayesian algorithm accuracy of 87.5% using R programming language. The impact of vector borne disease is very high in medical applications. Prediction of disease like malaria is an hour of the need and this is possible only with a suitable model for a given dataset. Hence, we have developed a model with Naive Bayesian algorithm is used for current research.

Automatic approach to enrich databases using ontology: Application in medical domain

The enrichment of databases is fundamental to maintain them, as well as the consistency and accuracy of the data. The database becomes useless if it is not up to date. Since there are a large number of databases, an automatic enrichment approach is required. However, until now no efficient approach has been provided in order to cope with this problem. In this paper, we propose a new approach to automate the enrichment of databases. It is based on an ontology, which model domains through sets of concepts and semantic relationships established between them. The proposed approach presents a set of rules to analyze ontologies and databases components and filter subsequently the necessary ones for the database enrichment of databases. We applied our approach in the medical domain that is a renewable domain. Also, it is characterized by a large number of databases and ontologies, and a large volume of data. For experimentations, a platform is developed to test rules using medical databases and medical ontologies. As a result we obtain enriched databases with new components that are either tables, attributes, or records.

Plant-mediated biosynthesis and characterization of silver nanoparticles by leaf extracts of Tragia involucrata, Cymbopogon citronella, Solanum verbascifolium and Tylophora ovata

Evolution of an ecological and biogenic experimental process for synthesis of nanoparticles is evolving into a significant offshoot of nanotechnology. As an important metal, silver nanoparticles (AgNPs) have a number of applications in medical domain. Since these silver nanoparticles have been used for infection prevention in the medical study, it may be more relevant to reduce their size by using ancient Indian herbal plants. A green rapid biogenic synthesis of silver nanoparticles using Tragia involucrata, Cymbopogon citronella, Solanum verbascifolium and Tylophora ovata aqueous extracts was demonstrated in this present study. The pathway of nanoparticles formation is by means of reduction of AgNO3 by leaf extracts, which acts as both reducing and capping agents. The formation of the silver nanoparticles was observed within 15 min. The properties of prepared nanoparticles were characterized by visual examination (color change), UV absorption, Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM) and X-ray Diffraction (XRD). An intense surface plasmon resonance band at ∼454 nm, ∼452 nm, ∼458 nm and ∼447 nm in the UV–visible spectrum clearly reveals the formation of silver nanoparticles. FTIR spectrum confirms the existence of various functional groups of biomolecules capping the nanoparticles. XRD studies reveal a high degree of crystallinity and monophasic Ag nanoparticles of face-centered cubic structure. The morphology of the particles formed consists of varied shapes such as rod, flower-like, spherical and hexagonal pattern. The biosynthesis of nanoparticles has received increasing attention due to the growing demand to produce secure, cost-effective and environmentally friendly technologies for nanomaterials synthesis.

An Enhanced Risk Prediction System for Cardiovascular Disease in India using Fuzzy Classification

In recent times most of the people are very cautious about their health. Information and Communication Technology (ICT) is playing an extensive role in the development of medical domain application, which has been one of the most active research areas for the scientists and practitioners. Heart disease rates among the major cause of mortality in developing countries like India. Fuzzy System is a one of the promising method to provide insights for better evaluation of cardiovascular diseases. Over the years many developers built Knowledgebase Decision Support System (KDSS) on diseases, based on the medical expert’s information. This process is time consuming and KDSS depends on medical expert’s opinion which may be subjective. To overcome this problem, machine learning techniques have been developed to gain knowledge automatically from past information and raw data. This work propose a novel Fuzzy rule based Disease Decision Support System (FDDSS) for the diagnosis of heart disease, based on the score obtained, the knowledge automatically from the patient’s clinical data. The FDDSS predicts the Cardiovascular Diseases which was considered as the most important cause of mortality in India by the year 2015. The experimentation result proves that, the proposed FDDSS is better than existing manual and automation methods. A few FDDSS experimental outcomes will be helpful to the physicians to take accurate decisions.

Recent Developments of the Liège Intranuclear Cascade Model in View of its Use into High-energy Transport Codes

Recent extensions of the Liège Intranuclear Cascade model, INCL, at energies below 100 MeV and for light-ion (up to oxygen) induced reactions are reported. Comparisons with relevant experimental data are shown. The model has been implemented into several high-energy transport codes allowing simulations in a wide domain of applications. Examples of simulations performed for spallation targets with the model implemented into MCNPX and in the domain of medical applications with GEANT4 are presented.

Improving chemotherapy processes with a protocol-based information system: A pre and post-implementation study

BackgroundThe medical application domain has been a great challenge for information technology solutions for decades, especially when the target process has been complex and multidisciplinary such as chemotherapy processes. ObjectiveTo evaluate the impact of a homegrown protocol based information system on the efficiency of chemotherapy workflow processes in an outpatient setting. MethodsA day care unit of the Hematology/Oncology outpatient clinic of Erasmus Medical Center was the setting for this study. The study consisted of comparison of pre- and post-implementation of four workflow efficiency related external indicators: turn-around times of a commonly administered chemotherapy course (Paclitaxel–Carboplatin), chemotherapy course administration postponing rate, the rate of recording course administration time, and patient admission rate of the outpatient clinic. The data was gathered retrospectively from patient charts and information systems’ log files. For the purpose of turn-around-time 109 Paclitaxel–Carboplatin chemotherapy courses of pre-implementation were compared to 118 those of post-implementation. For the other indicators: 247 chemotherapy courses pre-implementation were compared to 324 courses post-implementation. The process maps of pre- and post-implementation were also compared to each other. ResultsThe implementation of the system improved the process by removing repetition and sequencing of the tasks. Following the implementation, chemotherapy postponing decreased by 17.2% (Z=−4.723, P=.000) and there were 5.7% less records with missing administration time (Z=−3.047, P=.002). The admission rate increased 1.9 patient per working day (t(94)=−5.974, P=.000). The overall turn-around-time reduced 18.9min following the implementation (t(169)=3.48, P=.001). In a multivariate multiple regression model the reduction in turn-around time was related to the implementation of the system (Pillai's Trace=0.159, F(4,161)=7.613, P=.000). ConclusionInformation systems based on treatment protocols can reduce communication and synchronization needs between the stakeholders in a complex workflow process. These systems can help reengineering the process and improve workflow efficiency by removing unnecessary sequencing and repetitions of tasks.

A novel ensemble approach for multicategory classification of DNA microarray data using biological relevant gene sets

An important emerging medical application domain for microarray technology is clinical decision support in the form of diagnosis of diseases. For this task, several computational methods ranging from statistical alternatives to more complex hybrid systems have been previously proposed in the literature. In this work we study the utilisation of several ensemble alternatives for the task of classifying microarray data by using prior knowledge known to be biologically relevant to the target disease. The experimental results using different datasets and several gene sets show that the proposal is able to outperform previous approaches by introducing diversity as different gene sets.

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Abstract The development of medical domain applications has been one of the most active research areas recently. One example of a medical domain application is a detection system for heart disease based on computer-aided diagnosis methods, where the data is obtained from some other sources and is evaluated by computer based applications. Up to now, computers have usually been used to build knowledge based clinical decision support systems which used the knowledge from medical experts, and transferring this knowledge into computer algorithms was done manually. This process is time consuming and really depends on the medical expert’s opinion, which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining the knowledge from the patient’s clinical data. The proposed clinical decision support system for risk prediction of heart patients consists of two phases, (1) automated approach for generation of weighted fuzzy rules and decision tree rules, and, (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules

As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer-based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts’ opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient’s clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

Applied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorder

Amblyopia, or lazy eye, is a neurological vision disorder that studies have shown to affect two to five percent of the population. Current methods of treatment produce the best visual outcome, if the condition is identified early in the patient's life. Several early screening procedures are aimed at finding the condition while the patient is a child, including an automated vision screening system. This paper aims to use artificial intelligence techniques to automatically identify children who are at risk for developing the amblyopic condition and should therefore be referred to a specialist, i.e., pediatric ophthalmologist. Three techniques, namely, decision tree learning, random forest, and artificial neural network, are studied in this paper in terms of their effectiveness, using metrics of sensitivity, specificity, and accuracy. The features used by the techniques are extracted from images of patient eyes and are based on the color information. The efficacy of pixel color data is investigated with respect to the measurement of the rate of change of the color in the iris and pupil, i.e., color slope features. A 10-fold stratified cross validation procedure is used to compare the effectiveness of the three AI techniques in this medical application domain.