Displaying publications 1 - 20 of 254 in total

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  1. Baharuddin MY, Salleh ShH, Hamedi M, Zulkifly AH, Lee MH, Mohd Noor A, et al.
    Biomed Res Int, 2014;2014:478248.
    PMID: 24800230 DOI: 10.1155/2014/478248
    Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.
    Matched MeSH terms: Artificial Intelligence
  2. Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Artificial Intelligence
  3. Salih QA, Ramli AR, Mahmud R, Wirza R
    MedGenMed, 2005;7(2):1.
    PMID: 16369380
    Different approaches to gray and white matter measurements in magnetic resonance imaging (MRI) have been studied. For clinical use, the estimated values must be reliable and accurate when, unfortunately, many techniques fail on these criteria in an unrestricted clinical environment. A recent method for tissue clusterization in MRI analysis has the advantage of great simplicity, and it takes the account of partial volume effects. In this study, we will evaluate the intensity of MR sequences known as T1-weighted images in an axial sliced section. Intensity group clustering algorithms are proposed to achieve further diagnosis for brain MRI, which has been hardly studied. Subjective study has been suggested to evaluate the clustering group intensity in order to obtain the best diagnosis as well as better detection for the suspected cases. This technique makes use of image tissue biases of intensity value pixels to provide 2 regions of interest as techniques. Moreover, the original mathematic solution could still be used with a specific set of modern sequences. There are many advantages to generalize the solution, which give far more scope for application and greater accuracy.
    Matched MeSH terms: Artificial Intelligence
  4. Gan HS, Tan TS, Wong LX, Tham WK, Sayuti KA, Abdul Karim AH, et al.
    Biomed Mater Eng, 2014;24(6):3145-57.
    PMID: 25227024 DOI: 10.3233/BME-141137
    In medical image segmentation, manual segmentation is considered both labor- and time-intensive while automated segmentation often fails to segment anatomically intricate structure accordingly. Interactive segmentation can tackle shortcomings reported by previous segmentation approaches through user intervention. To better reflect user intention, development of suitable editing functions is critical. In this paper, we propose an interactive knee cartilage extraction software that covers three important features: intuitiveness, speed, and convenience. The segmentation is performed using multi-label random walks algorithm. Our segmentation software is simple to use, intuitive to normal and osteoarthritic image segmentation and efficient using only two third of manual segmentation's time. Future works will extend this software to three dimensional segmentation and quantitative analysis.
    Matched MeSH terms: Artificial Intelligence
  5. Logeswaran R
    Comput Methods Programs Biomed, 2012 Sep;107(3):404-12.
    PMID: 21194781 DOI: 10.1016/j.cmpb.2010.12.002
    This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.
    Matched MeSH terms: Artificial Intelligence
  6. Ihtatho D, Fadzil MH, Affandi AM, Hussein SH
    PMID: 18002738
    Psoriasis is a skin disorder which is caused by genetic fault. There is no cure for psoriasis, however, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the calculation of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameter, the lesion area. The method isolates healthy (or healed) skin areas from lesion areas by analyzing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. Euclidean distance of all pixels from each centroid is calculated. Each pixel is assigned to the class with minimum Euclidean distance. The study involves patients from three different ethnic origins having different skin tones. Results obtained show that the proposed method is comparable to the dermatologist visual approach.
    Matched MeSH terms: Artificial Intelligence
  7. Zain JM, Fauzi AM, Aziz AA
    Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:5459-62.
    PMID: 17946306
    Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.
    Matched MeSH terms: Artificial Intelligence
  8. Jamal N, Ng KH, Looi LM, McLean D, Zulfiqar A, Tan SP, et al.
    Phys Med Biol, 2006 Nov 21;51(22):5843-57.
    PMID: 17068368
    We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.
    Matched MeSH terms: Artificial Intelligence
  9. Gatellier L, Ong SK, Matsuda T, Ramlee N, Lau FN, Yusak S, et al.
    Asian Pac J Cancer Prev, 2021 Sep 01;22(9):2945-2950.
    PMID: 34582666 DOI: 10.31557/APJCP.2021.22.9.2945
    The COVID-pandemic has shown significant impact on cancer care from early detection, management plan to clinical outcomes of cancer patients. The Asian National Cancer Centres Alliance (ANCCA) has put together the 9 "Ps" as guidelines for cancer programs to better prepare for the next pandemic. The 9 "Ps" are Priority, Protocols and Processes, Patients, People, Personal Protective Equipments (PPEs), Pharmaceuticals, Places, Preparedness, and Politics. Priority: to maintain cancer care as a key priority in the health system response even during a global infectious disease pandemic. Protocol and processes: to develop a set of Standard Operating Procedures (SOPs) and have relevant expertise to man the Disease Outbreak Response (DORS) Taskforce before an outbreak. Patients: to prioritize patient safety in the event of an outbreak and the need to reschedule cancer management plan, supported by tele-consultation and use of artificial intelligence technology. People: to have business continuity planning to support surge capacity. PPEs and Pharmaceuticals: to develop plan for stockpiles management, build local manufacturing capacity and disseminate information on proper use and reduce wastage. Places: to design and build cancer care facilities to cater for the need of triaging, infection control, isolation and segregation. Preparedness: to invest early on manpower building and technology innovations through multisectoral and international collaborations. Politics: to ensure leadership which bring trust, cohesion and solidarity for successful response to pandemic and mitigate negative impact on the healthcare system.
    Matched MeSH terms: Artificial Intelligence
  10. Kamel NS, Sayeed S, Ellis GA
    IEEE Trans Pattern Anal Mach Intell, 2008 Jun;30(6):1109-13.
    PMID: 18421114 DOI: 10.1109/TPAMI.2008.32
    Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line signature verification system using the Singular Value Decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to recognize forgery signatures with a false acceptance rate of less than 1.2%.
    Matched MeSH terms: Artificial Intelligence*
  11. Bhuiyan MS, Choudhury IA, Dahari M
    Biol Cybern, 2015 Apr;109(2):141-62.
    PMID: 25491411 DOI: 10.1007/s00422-014-0635-1
    Development of an advanced control system for prostheses (artificial limbs) is necessary to provide functionality, effectiveness, and preferably the feeling of a sound living limb. The development of the control system has introduced varieties of control strategies depending on the application. This paper reviews some control systems used for prosthetics, orthotics, and exoskeletons. The advantages and limitations of different control systems for particular applications have been discussed and presented in a comparative manner to help in deciding the appropriate method for pertinent application.
    Matched MeSH terms: Artificial Intelligence
  12. Cacha LA, Poznanski RR
    J Integr Neurosci, 2014 Jun;13(2):253-92.
    PMID: 25012712 DOI: 10.1142/S0219635214400081
    A theoretical framework is developed based on the premise that brains evolved into sufficiently complex adaptive systems capable of instantiating genomic consciousness through self-awareness and complex interactions that recognize qualitatively the controlling factors of biological processes. Furthermore, our hypothesis assumes that the collective interactions in neurons yield macroergic effects, which can produce sufficiently strong electric energy fields for electronic excitations to take place on the surface of endogenous structures via alpha-helical integral proteins as electro-solitons. Specifically the process of radiative relaxation of the electro-solitons allows for the transfer of energy via interactions with deoxyribonucleic acid (DNA) molecules to induce conformational changes in DNA molecules producing an ultra weak non-thermal spontaneous emission of coherent biophotons through a quantum effect. The instantiation of coherent biophotons confined in spaces of DNA molecules guides the biophoton field to be instantaneously conducted along the axonal and neuronal arbors and in-between neurons and throughout the cerebral cortex (cortico-thalamic system) and subcortical areas (e.g., midbrain and hindbrain). Thus providing an informational character of the electric coherence of the brain - referred to as quantum coherence. The biophoton field is realized as a conscious field upon the re-absorption of biophotons by exciplex states of DNA molecules. Such quantum phenomenon brings about self-awareness and enables objectivity to have access to subjectivity in the unconscious. As such, subjective experiences can be recalled to consciousness as subjective conscious experiences or qualia through co-operative interactions between exciplex states of DNA molecules and biophotons leading to metabolic activity and energy transfer across proteins as a result of protein-ligand binding during protein-protein communication. The biophoton field as a conscious field is attributable to the resultant effect of specifying qualia from the metabolic energy field that is transported in macromolecular proteins throughout specific networks of neurons that are constantly transforming into more stable associable representations as molecular solitons. The metastability of subjective experiences based on resonant dynamics occurs when bottom-up patterns of neocortical excitatory activity are matched with top-down expectations as adaptive dynamic pressures. These dynamics of on-going activity patterns influenced by the environment and selected as the preferred subjective experience in terms of a functional field through functional interactions and biological laws are realized as subjectivity and actualized through functional integration as qualia. It is concluded that interactionism and not information processing is the key in understanding how consciousness bridges the explanatory gap between subjective experiences and their neural correlates in the transcendental brain.
    Matched MeSH terms: Artificial Intelligence
  13. Syed Abdul Mutalib SN, Juahir H, Azid A, Mohd Sharif S, Latif MT, Aris AZ, et al.
    Environ Sci Process Impacts, 2013 Sep;15(9):1717-28.
    PMID: 23831918 DOI: 10.1039/c3em00161j
    The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
    Matched MeSH terms: Artificial Intelligence
  14. Hannan MA, Arebey M, Begum RA, Basri H
    Waste Manag, 2012 Dec;32(12):2229-38.
    PMID: 22749722 DOI: 10.1016/j.wasman.2012.06.002
    An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions.
    Matched MeSH terms: Artificial Intelligence
  15. Muda HM, Saad P, Othman RM
    Comput Biol Med, 2011 Aug;41(8):687-99.
    PMID: 21704312 DOI: 10.1016/j.compbiomed.2011.06.004
    Remote protein homology detection and fold recognition refer to detection of structural homology in proteins where there are small or no similarities in the sequence. To detect protein structural classes from protein primary sequence information, homology-based methods have been developed, which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. Support Vector Machines (SVM) and Neural Networks (NN) are two popular discriminative methods. Recent studies have shown that SVM has fast speed during training, more accurate and efficient compared to NN. We present a comprehensive method based on two-layer classifiers. The 1st layer is used to detect up to superfamily and family in SCOP hierarchy using optimized binary SVM classification rules. It used the kernel function known as the Bio-kernel, which incorporates the biological information in the classification process. The 2nd layer uses discriminative SVM algorithm with string kernel that will detect up to protein fold level in SCOP hierarchy. The results obtained were evaluated using mean ROC and mean MRFP and the significance of the result produced with pairwise t-test was tested. Experimental results show that our approaches significantly improve the performance of remote protein homology detection and fold recognition for all three different version SCOP datasets (1.53, 1.67 and 1.73). We achieved 4.19% improvements in term of mean ROC in SCOP 1.53, 4.75% in SCOP 1.67 and 4.03% in SCOP 1.73 datasets when compared to the result produced by well-known methods. The combination of first layer and second layer of BioSVM-2L performs well in remote homology detection and fold recognition even in three different versions of datasets.
    Matched MeSH terms: Artificial Intelligence
  16. Dehzangi A, Phon-Amnuaisuk S
    Protein Pept Lett, 2011 Feb;18(2):174-85.
    PMID: 21054271
    One of the most important goals in bioinformatics is the ability to predict tertiary structure of a protein from its amino acid sequence. In this paper, new feature groups based on the physical and physicochemical properties of amino acids (size of the amino acids' side chains, predicted secondary structure based on normalized frequency of β-Strands, Turns, and Reverse Turns) are proposed to tackle this task. The proposed features are extracted using a modified feature extraction method adapted from Dubchak et al. To study the effectiveness of the proposed features and the modified feature extraction method, AdaBoost.M1, Multi Layer Perceptron (MLP), and Support Vector Machine (SVM) that have been commonly and successfully applied to the protein folding problem are employed. Our experimental results show that the new feature groups altogether with the modified feature extraction method are capable of enhancing the protein fold prediction accuracy better than the previous works found in the literature.
    Matched MeSH terms: Artificial Intelligence
  17. Loo CK, Rajeswari M, Rao MV
    IEEE Trans Neural Netw, 2004 Nov;15(6):1378-95.
    PMID: 15565767
    This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models.
    Matched MeSH terms: Artificial Intelligence
  18. Kumar R, Khan FU, Sharma A, Aziz IB, Poddar NK
    Curr Med Chem, 2021 Apr 04.
    PMID: 33820515 DOI: 10.2174/0929867328666210405114938
    There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.
    Matched MeSH terms: Artificial Intelligence
  19. Burki TK
    Lancet Haematol, 2021 Aug;8(8):e551.
    PMID: 34329575 DOI: 10.1016/S2352-3026(21)00215-5
    Matched MeSH terms: Artificial Intelligence
  20. Ahmad MH, Zezi AU, Anafi SB, Alhassan Z, Mohammed M, Danraka RN
    Data Brief, 2021 Jun;36:107155.
    PMID: 34041327 DOI: 10.1016/j.dib.2021.107155
    This article describes the dataset for the elucidation of the possible mechanisms of antidiarrhoeal actions of methanol leaves extract of Combretum hypopilinum (Diels) Combretaceae in mice. The plant has been used in traditional medicine to treat diarrhoea in Nigeria and other African countries. We introduce the data for the antidiarrhoeal activity of the methanol leaf extract of Combretum hypopilinum at 1,000 mg/kg investigated using charcoal meal test in mice with loperamide (5 mg/kg) as the standard antidiarrhoeal agent. To elucidate the possible mechanisms of its antidiarrhoeal action, naloxone (2 mg/kg), prazosin (1 mg/kg), yohimbine (2 mg/kg), propranolol (1 mg/kg), pilocarpine (1 mg/kg) and isosorbide dinitrate (150 mg/kg) were separately administered to different groups of mice 30 minutes before administration of the extract. Each mouse was dissected using dissecting set, and the small intestine was immediately removed from pylorus to caecum, placed lengthwise on moist filter paper and measured the distance travelled by charcoal relative to the length of the intestine using a calibrated ruler in centimetre. Besides, the peristaltic index and inhibition of charcoal movement of each animal were calculated and recorded. The methods for the data collection is similar to the one used to investigate the possible pathways involved in the antidiarrhoeal action of Combretum hypopilinum in mice in the research article by Ahmad et al. (2020) "Mechanisms of Antidiarrhoeal Activity of Methanol Leaf Extract of Combretum hypopilinum Diels (Combretaceae): Involvement of Opioidergic and (α1 and β)-Adrenergic Pathways" (https://doi.org/10.1016/j.jep.2020.113750) [1]. Therefore, this datasets could form a basis for in-depth research to elucidate further the pharmacological properties of the plant Combretum hypopilinum and its bioactive compounds to develop standardized herbal product and novel compound for management of diarrhoea. It could also be instrumental for evaluating the plant's pharmacological potentials using other computational-based and artificial intelligence approaches, including predictive modelling and simulation.
    Matched MeSH terms: Artificial Intelligence
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