Displaying publications 21 - 40 of 249 in total

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  1. Koh JEW, Ng EYK, Bhandary SV, Hagiwara Y, Laude A, Acharya UR
    Comput Biol Med, 2018 01 01;92:204-209.
    PMID: 29227822 DOI: 10.1016/j.compbiomed.2017.11.019
    Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.
  2. Wong EW, Yusof MY, Mansor MB, Anbazhagan D, Ong SY, Sekaran SD
    Singapore Med J, 2009 Aug;50(8):822-6.
    PMID: 19710984
    The AdeABC pump of Acinetobacter spp. confers resistance to various antibiotic classes. This pump is composed of the AdeA, AdeB, and AdeC proteins where AdeB is a member of the resistance-nodulation-division efflux pump superfamily. The adeA, adeB, and adeC genes are contiguous and adjacent to adeS and adeR, which are transcribed in the opposite direction and which specify proteins homologous to sensors and regulators of two-component systems, respectively. In this study, an attempt is made to elucidate the role of the AdeABC efflux pump in carbapenem resistance in Acinetobacter spp.
  3. Omar WEW, Singh G, McBain AJ, Cruickshank F, Radhakrishnan H
    Invest Ophthalmol Vis Sci, 2024 May 01;65(5):2.
    PMID: 38691091 DOI: 10.1167/iovs.65.5.2
    PURPOSE: To identify compositional differences in the gut microbiome of nonmyopes (NM) and myopes using 16S ribosomal RNA sequencing and to investigate whether the microbiome may contribute to the onset or progression of the condition.

    METHODS: Faecal samples were collected from 52 adult participants, of whom 23 were NM, 8 were progressive myopes (PM), and 21 were stable myopes (SM). The composition of the gut microbiota in each group was analysed using 16S ribosomal RNA gene sequencing.

    RESULTS: There were no significant differences in alpha and beta diversity between the three groups (NM, PM, and SM). However, the distributions of Bifidobacterium, Bacteroides, Megamonas, Faecalibacterium, Coprococcus, Dorea, Roseburia, and Blautia were significantly higher in the myopes (SM and PM combined) when compared with emmetropes. The myopes exhibited significantly greater abundance of bacteria that are linked to the regulation of dopaminergic signalling, such as Clostridium, Ruminococcus, Bifidobacterium, and Bacteroides. Individuals with stable myopia were found to have a significantly higher proportion of Prevotella copri than those with progressive myopia. Bifidobacterium adolescentis, a gamma-aminobutyric acid (GABA)-producing bacterium, was significantly higher in all myopes than in NM and, in the comparison between SM and PM, it is significantly higher in SM. B. uniformis and B. fragilis, both GABA-producing Bacteroides, were present in relatively high abundance in all myopes and in SM compared with PM, respectively.

    CONCLUSIONS: The presence of bacteria related to dopamine effect and GABA-producing bacteria in the gut microbiome of myopes may suggest a role of these microorganisms in the onset and progression of myopia.

  4. Chai EW, H'ng PS, Peng SH, Wan-Azha WM, Chin KL, Chow MJ, et al.
    Environ Technol, 2013 Sep-Oct;34(17-20):2859-66.
    PMID: 24527651
    In Malaysia, large amounts of organic materials, which lead to disposal problems, are generated from agricultural residues especially from palm oil industries. Increasing landfill costs and regulations, which limit many types of waste accepted at landfills, have increased the interest in composting as a component of waste management. The objectives of this study were to characterize compost feedstock properties of common organic waste materials available in Malaysia. Thus, a ratio modelling of matching ingredients for empty fruit bunches (EFBs) co-composting using different organic materials in Malaysia was done. Organic waste materials with a C/N ratio of < 30 can be applied as a nitrogen source in EFB co-composting. The outcome of this study suggested that the percentage of EFB ranged between 50% and 60%, which is considered as the ideal mixing ratio in EFB co-composting. Conclusively, EFB can be utilized in composting if appropriate feedstock in term of physical and chemical characteristics is coordinated in the co-composting process.
  5. Chan KF, Tan CW, Yeo DS, Tan HS, Tan FL, Tan EW, et al.
    J Occup Rehabil, 2011 Mar;21 Suppl 1:S69-76.
    PMID: 21328063 DOI: 10.1007/s10926-011-9289-1
    INTRODUCTION: Asia is the new and favored magnet of economic attention and foreign investments after it made an almost uneventful rebound from the depths of financial crisis of 2008/2009. Not many Western observers fully understand the diversity that is Asia other than perhaps its 2 growing economic giants of China and India. Indeed many smaller countries like Singapore and Malaysia in South East Asia along with Australia and Hong Kong (a Special Administrative Region within China) look to symbiotic relationships with these two economic giants. The purpose of this discussion paper is to examine the current issues related to the development and provision of occupational rehabilitation services in Singapore and Malaysia with a forward-looking view of how Asia's different developing societies could potentially benefit from better alignment of occupational rehabilitation practices and sharing of expertise through international collaboration and dialogue platforms.

    METHODS: Seven therapists and one physician who are frequently involved in occupational rehabilitation services in their home countries critically reviewed the current issues in Singapore and Malaysia which included analysis of the prevalence and cost of occupational injury; overview of workers' compensation system; current practices, obstacles, and challenges in providing occupational rehabilitation and return to work practices. They also offered opinions about how to improve the occupational rehabilitation programs of their two home countries.

    CONCLUSION: Even though Malaysia and Singapore are two different countries, in many ways their current provision of occupational rehabilitation services and the problems they face with are very similar. There is a lot of room for systemic improvements that require government support and action. Most prominently, the training of more healthcare professionals in the assessment and rehabilitation of the injured worker should be encouraged. There could be better liaison between the many stakeholders and more funding made available to develop resources and to jump start strategic programs. As these two countries are witnessing rapid economic growth, more resources should be allocated to establish holistic care of the injured workers emphasizing early interventions and prevention of chronic disabilities.

  6. Wong WZ, H'ng PS, Chin KL, Sajap AS, Tan GH, Paridah MT, et al.
    Environ Entomol, 2015 Oct;44(5):1367-74.
    PMID: 26314017 DOI: 10.1093/ee/nvv115
    The lower termite, Coptotermes curvignathus, is one of the most prominent plantation pests that feed upon, digest, and receive nourishment from exclusive lignocellulose diets. The objective of this study was to examine the utilization of sole carbon sources by isolated culturable aerobic bacteria among communities from the gut and foraging pathway of C. curvignathus. We study the bacteria occurrence from the gut of C. curvignathus and its surrounding feeding area by comparing the obtained phenotypic fingerprint with Biolog's extensive species library. A total of 24 bacteria have been identified mainly from the family Enterobacteriaceae from the identification of Biolog Gen III. Overall, the bacteria species in the termite gut differ from those of foraging pathway within a location, except Acintobacter baumannii, which was the only bacteria species found in both habitats. Although termites from a different study area do not have the same species of bacteria in the gut, they do have a bacterial community with similar role in degrading certain carbon sources. Sugars were preferential in termite gut isolates, while nitrogen carbon sources were preferential in foraging pathway isolates. The preferential use of specific carbon sources by these two bacterial communities reflects the role of bacteria for regulation of carbon metabolism in the termite gut and foraging pathway.
  7. Mookiah MR, Acharya UR, Chandran V, Martis RJ, Tan JH, Koh JE, et al.
    Med Biol Eng Comput, 2015 Dec;53(12):1319-31.
    PMID: 25894464 DOI: 10.1007/s11517-015-1278-7
    Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39% for MESSIDOR dataset and 95.93 and 93.33% for local dataset, respectively.
  8. Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, et al.
    Comput Biol Med, 2017 05 01;84:59-68.
    PMID: 28343061 DOI: 10.1016/j.compbiomed.2017.03.016
    The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME.
  9. M H, Chong EWN, Jafarzadeh S, Paridah MT, Gopakumar DA, Tajarudin HA, et al.
    Polymers (Basel), 2019 Jan 26;11(2).
    PMID: 30960194 DOI: 10.3390/polym11020210
    This study aimed to compare the performance of fabricated microbially induced precipitated calcium carbonate⁻ (MB⁻CaCO₃) based red seaweed (Kappaphycus alvarezii) bio-polymer film and commercial calcium carbonate⁻ (C⁻CaCO₃) based red seaweed bio-film with the conventional biodegradable mulch film. To the best of our knowledge, there has been limited research on the application of commercial CaCO₃ (C⁻CaCO₃) and microbially induced CaCO₃ (MB⁻CaCO₃) as fillers for the preparation of films from seaweed bio-polymer and comparison with biodegradable commercial plasticulture packaging. The results revealed that the mechanical, contact angle, and biodegradability properties of the polymer composite films incorporated with C⁻CaCO₃ and MB⁻CaCO₃ fillers were comparable or even superior than the conventional biodegradable mulch film. The seaweed polymer film incorporated with MB⁻CaCO₃ showed the highest contact angle of 100.94°, whereas conventional biodegradable mulch film showed a contact angle of 90.25°. The enhanced contact angle of MB⁻CaCO₃ resulted in high barrier properties, which is highly desired in the current scenario for plasticulture packaging application. The water vapor permeability of MB⁻CaCO₃ based seaweed films was low (2.05 ± 1.06 g·m/m²·s·Pa) when compared to conventional mulch film (2.68 ± 0.35 g·m/m²·s·Pa), which makes the fabricated film an ideal candidate for plasticulture application. The highest tensile strength (TS) was achieved by seaweed-based film filled with commercial CaCO₃ (84.92% higher than conventional mulch film). SEM images of the fractured surfaces of the fabricated films revealed the strong interaction between seaweed and fillers. Furthermore, composite films incorporated with MB⁻CaCO₃ promote brighter film, better water barrier, hydrophobicity, and biodegradability compared to C⁻CaCO₃ based seaweed polymer film and conventional mulch film. From this demonstrated work, it can be concluded that the fabricated MB⁻CaCO₃ based seaweed biopolymer film will be a promising candidate for plasticulture and agricultural application.
  10. Jamaludin SYN, Azimi I, Davis FM, Peters AA, Gonda TJ, Thompson EW, et al.
    Oncol Lett, 2018 Apr;15(4):4289-4295.
    PMID: 29541196 DOI: 10.3892/ol.2018.7827
    CXC ligand (L)12 is a chemokine implicated in the migration, invasion and metastasis of cancer cells via interaction with its receptors CXC chemokine receptor (CXCR)4 and CXCR7. In the present study, CXCL12-mediated Ca2+signalling was compared with two basal-like breast cancer cell lines, MDA-MB-231 and MDA-MB-468, which demonstrate distinct metastatic potential. CXCL12 treatment induced Ca2+responses in the more metastatic MDA-MB-231 cells but not in the less metastatic MDA-MB-468 cells. Assessment of mRNA levels of CXCL12 receptors and their potential modulators in both cell lines revealed that CXCR4 and CXCR7 levels were increased in MDA-MB-231 cells compared with MDA-MB-468 cells. Cluster of differentiation (CD)24, the negative regulator of CXCL12 responses, demonstrated increased expression in MDA-MB-468 cells compared with MDA-MB-231 cells, and the two cell lines expressed comparable levels of hypoxia-inducible factor (HIF)2α, a CXCR4 regulator. Induction of epithelial-mesenchymal transition (EMT) by epidermal growth factor exhibited opposite effects on CXCR4 mRNA levels compared with hypoxia-induced EMT. Neither EMT inducer exhibited an effect on CXCR7 expression, however hypoxia increased HIF2α expression levels in MDA-MB-468 cells. Analysis of the gene expression profiles of breast tumours revealed that the highest expression levels of CXCR4 and CXCR7 were in the Claudin-Low molecular subtype, which is markedly associated with EMT features.
  11. Yin DXC, Chiow SM, Karandikar A, Goh JPN, Manish BM, Gan JWJ, et al.
    Med J Malaysia, 2024 Mar;79(2):196-202.
    PMID: 38553926
    OBJECTIVE: The standard treatment for regional failure in nasopharyngeal carcinoma (NPC) is the radical neck dissection (RND). Our study sought to determine if magnetic resonance imaging (MRI) may accurately predict nodal involvement to allow selected levels of neck dissection to be preserved.

    STUDY DESIGN AND SETTING: We analysed retrospectively all NPC patients in our centre undergoing neck dissections as salvage therapy for nodal recurrence. Nodal involvement based on the preoperative MRI was assessed and compared with postoperative histopathology.

    METHODS: This is a retrospective study conducted on patients in our centre with recurrent NPC from February 2002 to February 2017. Patients were identified from the database of the otolaryngology oncology division at our institution. Of these, 28 patients met all our inclusion and exclusion criteria. We calculated sensitivity and specificity as well as average number of nodes per patient.

    RESULTS: In our study, we calculated the false negative and false positive rates of preoperative MRI neck by levels. Overall sensitivity of MRI picking up disease by level was 76% and specificity was 86%.

    CONCLUSION: Based on our study, we will be missing a total of 10 (7.1%) diseased neck levels in eight (28.5%) patients. MRI alone, therefore, does not provide enough information to allow safe selective preservation of neck levels in surgical salvage of neck recurrences in NPC.

  12. Acharya UR, Mookiah MR, Koh JE, Tan JH, Bhandary SV, Rao AK, et al.
    Comput Biol Med, 2016 08 01;75:54-62.
    PMID: 27253617 DOI: 10.1016/j.compbiomed.2016.04.015
    Posterior Segment Eye Diseases (PSED) namely Diabetic Retinopathy (DR), glaucoma and Age-related Macular Degeneration (AMD) are the prime causes of vision loss globally. Vision loss can be prevented, if these diseases are detected at an early stage. Structural abnormalities such as changes in cup-to-disc ratio, Hard Exudates (HE), drusen, Microaneurysms (MA), Cotton Wool Spots (CWS), Haemorrhages (HA), Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in PSED can be identified by manual examination of fundus images by clinicians. However, manual screening is labour-intensive, tiresome and time consuming. Hence, there is a need to automate the eye screening. In this work Bi-dimensional Empirical Mode Decomposition (BEMD) technique is used to decompose fundus images into 2D Intrinsic Mode Functions (IMFs) to capture variations in the pixels due to morphological changes. Further, various entropy namely Renyi, Fuzzy, Shannon, Vajda, Kapur and Yager and energy features are extracted from IMFs. These extracted features are ranked using Chernoff Bound and Bhattacharyya Distance (CBBD), Kullback-Leibler Divergence (KLD), Fuzzy-minimum Redundancy Maximum Relevance (FmRMR), Wilcoxon, Receiver Operating Characteristics Curve (ROC) and t-test methods. Further, these ranked features are fed to Support Vector Machine (SVM) classifier to classify normal and abnormal (DR, AMD and glaucoma) classes. The performance of the proposed eye screening system is evaluated using 800 (Normal=400 and Abnormal=400) digital fundus images and 10-fold cross validation method. Our proposed system automatically identifies normal and abnormal classes with an average accuracy of 88.63%, sensitivity of 86.25% and specificity of 91% using 17 optimal features ranked using CBBD and SVM-Radial Basis Function (RBF) classifier. Moreover, a novel Retinal Risk Index (RRI) is developed using two significant features to distinguish two classes using single number. Such a system helps to reduce eye screening time in polyclinics or community-based mass screening. They will refer the patients to main hospitals only if the diagnosis belong to the abnormal class. Hence, the main hospitals will not be unnecessarily crowded and doctors can devote their time for other urgent cases.
  13. Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al.
    Comput Biol Med, 2014 Oct;53:55-64.
    PMID: 25127409 DOI: 10.1016/j.compbiomed.2014.07.015
    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.
  14. Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.
    Comput Biol Med, 2016 06 01;73:131-40.
    PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009
    Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
  15. Mookiah MR, Acharya UR, Fujita H, Koh JE, Tan JH, Noronha K, et al.
    Comput Biol Med, 2015 Aug;63:208-18.
    PMID: 26093788 DOI: 10.1016/j.compbiomed.2015.05.019
    Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision. AMD can be dry or wet type, however dry AMD is most common. It is classified into early, intermediate and late AMD. The early detection and treatment may help one to stop the progression of the disease. Automated AMD diagnosis may reduce the screening time of the clinicians. In this work, we have introduced LCP to characterize normal and AMD classes using fundus images. Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features are extracted from fundus images. These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz. Decision Tree (DT), Nearest Neighbour (k-NN), Naive Bayes (NB), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to classify normal and AMD classes. The performance of the system is evaluated using both private (Kasturba Medical Hospital, Manipal, India) and public domain datasets viz. Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) using ten-fold cross validation. The proposed approach yielded best performance with a highest average accuracy of 97.78%, sensitivity of 98.00% and specificity of 97.50% for STARE dataset using 22 significant features. Hence, this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD.
  16. Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, et al.
    Comput Biol Med, 2017 05 01;84:89-97.
    PMID: 28351716 DOI: 10.1016/j.compbiomed.2017.03.008
    Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.
  17. Acharya UR, Raghavendra U, Koh JEW, Meiburger KM, Ciaccio EJ, Hagiwara Y, et al.
    Comput Methods Programs Biomed, 2018 Nov;166:91-98.
    PMID: 30415722 DOI: 10.1016/j.cmpb.2018.10.006
    BACKGROUND AND OBJECTIVE: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images.

    METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.

    RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.

    CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.

  18. Acharya UR, Ng WL, Rahmat K, Sudarshan VK, Koh JEW, Tan JH, et al.
    Comput Biol Med, 2017 12 01;91:13-20.
    PMID: 29031099 DOI: 10.1016/j.compbiomed.2017.10.001
    Shear wave elastography (SWE) examination using ultrasound elastography (USE) is a popular imaging procedure for obtaining elasticity information of breast lesions. Elasticity parameters obtained through SWE can be used as biomarkers that can distinguish malignant breast lesions from benign ones. Furthermore, the elasticity parameters extracted from SWE can speed up the diagnosis and possibly reduce human errors. In this paper, Shearlet transform and local binary pattern histograms (LBPH) are proposed as an original algorithm to differentiate malignant and benign breast lesions. First, Shearlet transform is applied on the SWE images to acquire low frequency, horizontal and vertical cone coefficients. Next, LBPH features are extracted from the Shearlet transform coefficients and subjected to dimensionality reduction using locality sensitivity discriminating analysis (LSDA). The reduced LSDA components are ranked and then fed to several classifiers for the automated classification of breast lesions. A probabilistic neural network classifier trained only with seven top ranked features performed best, and achieved 98.08% accuracy, 98.63% sensitivity, and 97.59% specificity in distinguishing malignant from benign breast lesions. The high sensitivity and specificity of our system indicates that it can be employed as a primary screening tool for faster diagnosis of breast malignancies, thereby possibly reducing the mortality rate due to breast cancer.
  19. Ashkir Z, Samat AHA, Ariga R, Finnigan L, Jermy S, Akhtar MA, et al.
    PMID: 39417278 DOI: 10.1093/ehjci/jeae260
    BACKGROUND: Myocardial disarray, an early feature of hypertrophic cardiomyopathy (HCM) and a substrate for ventricular arrhythmia, is poorly characterised in prehypertrophic sarcomeric variant carriers (SARC+LVH-).

    OBJECTIVES: Using diffusion tensor cardiac magnetic resonance (DT-CMR) we assessed myocardial disarray and fibrosis in both SARC+LVH- and HCM patients and evaluated the relationship between microstructural alterations and electrocardiographic (ECG) parameters associated with arrhythmic risk.

    METHODS: Sixty-two individuals (24 SARC+LVH-, 24 HCM and 14 matched controls) were evaluated with multiparametric CMR including stimulated echo acquisition mode (STEAM) DT-CMR, and blinded quantitative 12-lead ECG analysis.

    RESULTS: Mean diastolic fractional anisotropy (FA) was reduced in HCM compared to SARC+LVH- and controls (0.49±0.05 vs 0.52±0.04 vs 0.53±0.04, p=0.009), even after adjustment for differences in extracellular volume (ECV) (p=0.038). Both HCM and SARC+LVH- had segments with significantly reduced FA relative to controls (54% vs 25% vs 0%, p=0.002). Multiple repolarization parameters were prolonged in HCM and SARC+LVH-, with corrected JT interval (JTc) being most significant (354±42ms vs 356±26ms vs 314±26ms, p=0.002). Among SARC+LVH-, JTc duration correlated negatively with mean FA (r=-0.6, p=0.002). In HCM, the JTc interval showed a stronger association with ECV (r=0.6 p=0.019) than FA (r=-0.1 p=0.72). JTc discriminated SARC+LVH- from controls (Area-under-the-receiver-operator-curve 0.88, CI 0.76-1.00, p<0.001), and in HCM correlated with the ESC HCM sudden cardiac death risk score (r=0.5, p=0.014).

    CONCLUSION: Low diastolic FA, suggestive of myocardial disarray, is present in both SARC+LVH- and HCM. Low FA and raised ECV were associated with repolarization prolongation. Myocardial disarray assessment using DT-CMR and repolarization parameters such as the JTc interval demonstrate significant potential as markers of disease activity in HCM.

  20. Schee JP, Ang CL, Crystal Teoh SC, Tan HJ, Chew SH, Steven A, et al.
    Med J Malaysia, 2023 Sep;78(5):594-601.
    PMID: 37775485
    INTRODUCTION: Intravenous thrombolysis (IVT) with recombinant tissue plasminogen activator is beneficial in acute ischaemic stroke (AIS). We aim to compare the realworld clinical outcomes and service efficiency of IVT in Malaysian primary stroke centres (PSCs) versus acute stroke ready hospitals (ASRHs).

    MATERIALS AND METHODS: We conducted a multi-centre cohort study involving 5 PSCs and 7 ASRHs in Malaysia. Through review of medical records of AIS patients who received IVT from 01 January 2014 to 30 June 2021, real-world data was extracted for analysis. Univariate and multivariate regression models were employed to evaluate the role of PSCs versus ASRHs in post-IVT outcomes and complications. Statistical significance was set at p<0.05.

    RESULTS: A total of 313 multi-ethnic Asians, namely 231 from PSCs and 82 from ASRHs, were included. Both groups were comparable in baseline demographic, clinical, and stroke characteristics. The efficiency of IVT delivery (door-toneedle time), functional outcomes (mRS at 3 months post- IVT), and rates of adverse events (intracranial haemorrhages and mortality) following IVT were comparable between the 2 groups. Notably, 46.8% and 48.8% of patients in PSCs and ASRHs group respectively (p=0.752) achieved favourable functional outcome (mRS≤1 at 3 months post-IVT). Regression analyses demonstrated that post-IVT functional outcomes and adverse events were independent of the role of PSCs or ASRHs.

    CONCLUSION: Our study provides real-world evidence which suggests that IVT can be equally safe, effective, and efficiently delivered in ASRHs. This may encourage the establishment of more ASRHs to extend the benefits of IVT to a greater proportion of stroke populations and enhance the regional stroke care.

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