Displaying publications 1 - 20 of 1032 in total

  1. Lee J, Kim YE, Kim HY, Sinniah M, Chong CK, Song HO
    Sci Rep, 2015;5:18077.
    PMID: 26655854 DOI: 10.1038/srep18077
    High levels of anti-dengue IgM or IgG can be detected using numerous rapid diagnostic tests (RDTs). However, the sensitivity and specificity of these tests are reduced by changes in envelope glycoprotein antigenicity that inevitably occur in limited expression systems. A novel RDT was designed to enhance diagnostic sensitivity. Dengue viruses cultured in animal cells were used as antigens to retain the native viral coat protein. Monoclonal antibodies (mAbs) were then developed, for the first time, against domain I of envelope glycoprotein (EDI). The anti-dengue EDI mAb was employed as a capturer, and EDII and EDIII, which are mainly involved in the induction of neutralizing antibodies in patients, were fully available to bind to anti-dengue IgM or IgG in patients. A one-way automatic blood separation device prevented reverse migration of plasma and maximize the capture of anti-dengue antibodies at the test lines. A clinical evaluation in the field proved that the novel RDT (sensitivities of 96.5% and 96.7% for anti-dengue IgM and IgG) is more effective in detecting anti-dengue antibodies than two major commercial tests (sensitivities of 54.8% and 82% for SD BIOLINE; 50.4% and 75.3% for PanBio). The innovative format of RDT can be applied to other infectious viral diseases.
    Matched MeSH terms: Sensitivity and Specificity*
  2. Quek KF, Low WY, Razack AH, Chua CB, Loh CS
    Med J Malaysia, 2002 Jun;57(2):169-77.
    PMID: 24326647
    The aim of the study was to validate the Malay version of the General Quentionnaire (GHQ-12) in patients with psychiatric morbidity secondary to urological disorder. Validity and reliability were studied in patients with lower urinary tract symptoms (LUTS) and patients without LUTS. Internal consistency was excellent. A high degree of internal consistency was observed for each of the 12 items and total scores (Cronbach's alpha value = 0.50 and higher and 0.65 respectively. Test-retest correlation coefficient for the 12 items scores was highly significant. Intraclass correlation coefficient was high (ICC=0.47 and above). A significant level between baseline and post-treatment scores were observed across 3 items in the surgical group. The Mal-GHQ-12 is a suitable, reliable, valid and sensitive to clinical change in the Malaysian population.
    Matched MeSH terms: Sensitivity and Specificity*
  3. Yong C, Teo YM, Kapur J
    Med J Malaysia, 2016 Aug;71(4):193-198.
    PMID: 27770118
    To evaluate the performance of contrastenhanced ultrasound (CEUS) in the risk stratification of indeterminate renal lesions picked up incidentally on abdominal imaging, in patients with renal impairment.
    Matched MeSH terms: Sensitivity and Specificity*
  4. Bhugaloo A, Abdullah B, Siow Y, Ng Kh
    Biomed Imaging Interv J, 2006 Apr;2(2):e12.
    PMID: 21614224 MyJurnal DOI: 10.2349/biij.2.2.e12
    The primary objective of this study was to evaluate the specificity and sensitivity of diffusion weighted MR imaging (DWI) in the differentiation and characterisation between benign and malignant vertebral compression fractures compared with conventional T1 WI, T2 WI and fat suppressed contrast enhanced T1 WI in the Malaysian population.
    Matched MeSH terms: Sensitivity and Specificity
  5. Suraiya S, Semail N, Ismail MF, Abdullah JM
    Genome Announc, 2016;4(3).
    PMID: 27198011 DOI: 10.1128/genomeA.00323-16
    Mycobacterium tuberculosis is known to cause pulmonary and extrapulmonary tuberculosis. This organism showed special phylogeographical specificity. Here, we report the complete genome sequence of M. tuberculosis clinical isolate spoligotype SIT745/EAI1-MYS, which was isolated from a Malaysian tuberculosis patient.
    Matched MeSH terms: Sensitivity and Specificity
  6. Ismail A
    Malays J Med Sci, 2000 Jul;7(2):3-8.
    PMID: 22977383 MyJurnal
    For effective management of typhoid, diagnosis of the disease must be done with speed and accuracy. Development of such a test would require antigens that are specific for typhoid diagnosis. Attempts at finding the specific antigen have been carried out throughout the years. The finding of such an antigen can lead to carrier detection as well. Candidate antigens have been used in the development of antigen or antibody detection tests with variation in sensitivity and specificity. Further characterization and understanding of the candidate antigens combined with use of innovative technologies will allow for the ideal test for typhoid and typhoid carriers to be within reach.
    Matched MeSH terms: Sensitivity and Specificity
  7. Ibrahim, M. I., Mohd Norsuddin, N., Che Isa, I. N., Azman, N. F., Mohamad Shahimin, M.
    The radiographer's role in the imaging field is producing the best image to diagnose. Hence, this study is conducted to justify the ability of radiographers in terms of diagnostic performance and visual search patterns during radiographic image interpretation based on their experience. The musculoskeletal radiographic images were chosen as radiographers are expected to perform image interpretation in the red dot system as one of the expanded and extended roles of the radiographer. Sensitivity and specificity in the detection of abnormality are measured. The gaze plot, fixation count and duration are compared between groups of radiographers by using an eye tracker. 19 radiographic images consist of upper and lower extremities are used as stimuli in this study. The result from this study shows no significant difference in terms of sensitivity and specificity with a p-value of 0.818 and 0.146 respectively. For visual search pattern, two images have significant different in term of fixation count (Image 1, p = 0.017; Image 2, p = 0.042) and two images in fixation duration (Image 1, p = 0.001; Image 15, p = 0.021). The gaze plot is not different from an unstructured pattern and less coverage. In conclusion, the experience did not give an influence on the radiographic image interpretation. This may suggest that specific training in areas appropriate to the development of the radiographer could improve the image interpretation.
    Matched MeSH terms: Sensitivity and Specificity
  8. Che Azemin MZ, Hassan R, Mohd Tamrin MI, Md Ali MA
    Int J Biomed Imaging, 2020;2020:8828855.
    PMID: 32849861 DOI: 10.1155/2020/8828855
    The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
    Matched MeSH terms: Sensitivity and Specificity
  9. Islam KT, Raj RG
    Sensors (Basel), 2017 Apr 13;17(4).
    PMID: 28406471 DOI: 10.3390/s17040853
    Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.
    Matched MeSH terms: Sensitivity and Specificity
  10. Wan Fadzlina Wan Muhd Shukeri, Azrina Md. Ralib, Ummu Khultum Jamaludin, Mohd Basri Mat-Nor
    Currently, it is almost impossible to diagnose a patient at the onset of
    sepsis due to the lack of real-time metrics with high sensitivity and specificity. The
    purpose of the present study is to determine the diagnostic value of model-based insulin
    sensitivity (SI) as a new sepsis biomarker in critically ill patients, and compare its
    performance to classical inflammatory parameters. (Copied from article).
    Matched MeSH terms: Sensitivity and Specificity
  11. Horry M, Chakraborty S, Pradhan B, Paul M, Gomes D, Ul-Haq A, et al.
    Sensors (Basel), 2021 Oct 07;21(19).
    PMID: 34640976 DOI: 10.3390/s21196655
    Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
    Matched MeSH terms: Sensitivity and Specificity
  12. Basri KN, Yazid F, Megat Abdul Wahab R, Mohd Zain MN, Md Yusof Z, Zoolfakar AS
    PMID: 34634732 DOI: 10.1016/j.saa.2021.120464
    Caries is one of the non-communicable diseases that has a high prevalence trend. The current methods used to detect caries require sophisticated laboratory equipment, professional inspection, and expensive equipment such as X-ray imaging device. A non-invasive and economical method is required to substitute the conventional methods for the detection of caries. UV absorption spectroscopy coupled with chemometrics analysis has emerged as a good potential candidate for such an application. Data preprocessing methods such as mean centre, autoscale and Savitzky-Golay smoothing were implemented to enhance the signal-to-noise ratio of spectra data. Various classification algorithms namely K-nearest neighbours (KNN), logistic regression (LR) and linear discriminant analysis (LDA) were implemented to classify the severity of dental caries into International Caries Detection and Assessment System (ICDAS) scores. The performance of the prediction model was measured and comparatively analysed based on the accuracy, precision, sensitivity, and specificity. The LDA algorithm combined with the Savitzky-Golay preprocessing method had shown the best result with respect to the validation data accuracy, precision, sensitivity and specificity, where each had values of 0.90, 1.00, 0.86 and 1.00 respectively. The area under the curve of the ROC plot computed for the LDA algorithm was 0.95, which indicated that the prediction algorithm was capable of differentiating normal and caries teeth excellently.
    Matched MeSH terms: Sensitivity and Specificity
  13. Mei-Ling Sharon TAI, Hazman MOHD NOR, Kartini Rahmat, Shanthi Viswanathan, Khairul Azmi Abdul Kadir, Norlisah Ramli, et al.
    Neurology Asia, 2017;22(1):15-23.
    Objective: The primary objective of this study was to describe the neuroimaging changes of tuberculous meningitis (TBM), and to determine the role of neuroimaging in the diagnosis of TBM.
    Methods: Between January 2009 and July 2015, we prospectively recruited TBM patients in two hospitals in Malaysia. Neuroimaging was performed and findings were recorded. The control consists of other types of meningo-encephalitis seen over the same period.
    Results: Fifty four TBM patients were recruited. Leptomeningeal enhancement was seen in 39 (72.2%) patients, commonly at prepontine cistern and interpeduncular fossa. Hydrocephalus was observed in 38 (70.4%) patients, 25 (46.3%) patients had moderate and severe hydrocephalus. Thirty four patients (63.0%) had cerebral infarction. Tuberculoma were seen in 29 (53.7%) patients; 27 (50.0%) patients had classical tuberculoma, 2 (3.7%) patients
    had “other” type of tuberculoma, 18 (33.3%) patients had ≥5 tuberculoma, and 11 (20.4%) patients had < 5 tuberculoma. Fifteen (37.2%) patients had vasculitis, 6 (11.1%) patients had vasospasm. Close to nine tenth (88.9%) of the patients had ≥1 classical neuroimaging features, 77.8% had ≥ 2 classical imaging features of TBM (basal enhancement, hydrocephalus, basal ganglia / thalamic infarct, classical tuberculoma, and vasculitis/vasospasm). Only 4% with other types of meningitis/encephalitis had ≥1 feature, and 1% had two or more classical TBM neuroimaging features. The sensitivity of the imaging features of the imaging features for diagnosis of TBM was 88.9% and the specificity was 95.6%.
    Conclusion: The classic imaging features of basal enhancement, hydrocephalus, basal ganglia/thalamic infarct, classic tuberculoma, and vasculitis are sensitive and specific to diagnosis of TBM.
    Matched MeSH terms: Sensitivity and Specificity
  14. Md Arshad NZ, Ng BK, Md Paiman NA, Abdullah Mahdy Z, Mohd Noor R
    Asian Pac J Cancer Prev, 2018 Jan 27;19(1):213-218.
    PMID: 29373916
    Background: Accuracy of diagnosis with intra-operative frozen sections is extremely important in the evaluation of ovarian tumors so that appropriate surgical procedures can be selected. Study design: All patients who with intra-operative frozen sections for ovarian masses in a tertiary center over nine years from June 2008 until April 2017 were reviewed. Frozen section diagnosis and final histopathological reports were compared. Main outcome measures: Sensitivity, specificity, positive and negative predictive values of intra-operative frozen section as compared to final histopathological results for ovarian tumors. Results: A total of 92 cases were recruited for final evaluation. The frozen section diagnoses were comparable with the final histopathological reports in 83.7% of cases. The sensitivity, specificity, positive predictive value and negative predictive value for benign and malignant ovarian tumors were 95.6%, 85.1%, 86.0% and 95.2% and 69.2%, 100%, 100% and 89.2% respectively. For borderline ovarian tumors, the sensitivity and specificity were 76.2% and 88.7%, respectively; the positive predictive value was 66.7% and the negative predictive value was 92.7%. Conclusion: The accuracy of intra-operative frozen section diagnoses for ovarian tumors is high and this approach remains a reliable option in assessing ovarian masses intra-operatively.
    Matched MeSH terms: Sensitivity and Specificity
  15. Wang P, Jiang L, Soh KL, Ying Y, Liu Y, Huang X, et al.
    Nutr Cancer, 2023;75(1):61-72.
    PMID: 35903897 DOI: 10.1080/01635581.2022.2104877
    Early assessment of malnutrition in cancer patients is very important. The Mini Nutritional Assessment (MNA) is often used to assess malnutrition in adult cancer patients. However, the diagnostic values of MNA are controversial. We aimed to analyze the diagnostic values of MNA in assessing malnutrition in adult cancer patients. A systematic search was performed using Embase, Web of Science, PubMed, the Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Database, and China Science and Technology Journal Database (VIP). Studies comparing MNA with other tools or criteria in cancer patients were included. The quality of the included studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity, specificity, the area under the receiver-operating characteristic curve (AUC), and the diagnostic odds ratio (DOR) were calculated using Stata 17.0 and Meta-DiSc1.4. In addition, sensitivity, subgroup, meta-regression, and publication bias analyses were conducted. In total, 11 studies involving 1367 patients involving MNA were included. The pooled sensitivity, specificity, ROC, and DOR were 0.84 (95% CI: 0.81-0.87), 0.66 (95% CI: 0.63-0.69), 0.84 (95% CI: 0.81-0.87), and 16.11 (95% CI: 7.16-36.27), respectively. In the assessment of malnutrition in adult cancer patients, MNA has high sensitivity and moderate specificity.
    Matched MeSH terms: Sensitivity and Specificity
  16. Haron N, Rajendran S, Kallarakkal TG, Zain RB, Ramanathan A, Abraham MT, et al.
    Oral Dis, 2023 Mar;29(2):380-389.
    PMID: 33914993 DOI: 10.1111/odi.13892
    OBJECTIVE: To evaluate the accuracy of MeMoSA®, a mobile phone application to review images of oral lesions in identifying oral cancers and oral potentially malignant disorders requiring referral.

    SUBJECTS AND METHODS: A prospective study of 355 participants, including 280 with oral lesions/variants was conducted. Adults aged ≥18 treated at tertiary referral centres were included. Images of the oral cavity were taken using MeMoSA®. The identification of the presence of lesion/variant and referral decision made using MeMoSA® were compared to clinical oral examination, using kappa statistics for intra-rater agreement. Sensitivity, specificity, concordance and F1 score were computed. Images were reviewed by an off-site specialist and inter-rater agreement was evaluated. Images from sequential clinical visits were compared to evaluate observable changes in the lesions.

    RESULTS: Kappa values comparing MeMoSA® with clinical oral examination in detecting a lesion and referral decision was 0.604 and 0.892, respectively. Sensitivity and specificity for referral decision were 94.0% and 95.5%. Concordance and F1 score were 94.9% and 93.3%, respectively. Inter-rater agreement for a referral decision was 0.825. Progression or regression of lesions were systematically documented using MeMoSA®.

    CONCLUSION: Referral decisions made through MeMoSA® is highly comparable to clinical examination demonstrating it is a reliable telemedicine tool to facilitate the identification of high-risk lesions for early management.

    Matched MeSH terms: Sensitivity and Specificity
  17. Chin KY, Low NY, Kamaruddin AAA, Dewiputri WI, Soelaiman IN
    Ther Clin Risk Manag, 2017;13:1333-1341.
    PMID: 29070951 DOI: 10.2147/TCRM.S145519
    BACKGROUND: Calcaneal quantitative ultrasound (QUS) is a useful tool in osteoporosis screening. However, QUS device may not be available at all primary health care settings. Osteoporosis self-assessment tool for Asians (OSTA) is a simple algorithm for osteoporosis screening that does not require any sophisticated instruments. This study explored the possibility of replacing QUS with OSTA by determining their agreement in identifying individuals at risk of osteoporosis.

    METHODS: A cross-sectional study was conducted to recruit Malaysian men and women aged ≥50 years. Their bone health status was measured using a calcaneal QUS device and OSTA. The association between OSTA and QUS was determined using Spearman's correlation and their agreement was assessed using Cohen Kappa and receiver-operating curve.

    RESULTS: All QUS indices correlated significantly with OSTA (p<0.05). The agreement between QUS and OSTA was minimal but statistically significant (p<0.05). The performance of OSTA in identifying subjects at risk of osteoporosis according to QUS was poor-to-fair in women (p<0.05), but not statistically significant for men (p>0.05). Changing the cut-off values improved the performance of OSTA in women but not in men.

    CONCLUSION: The agreement between QUS and OSTA is minimal in categorizing individuals at risk of osteoporosis. Therefore, they cannot be used interchangeably in osteoporosis screening.
    Matched MeSH terms: Sensitivity and Specificity*
  18. Chong ZX, Liew WPP, Ong HK, Yong CY, Shit CS, Ho WY, et al.
    Pathol Res Pract, 2021 Sep;225:153565.
    PMID: 34333398 DOI: 10.1016/j.prp.2021.153565
    Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are two common betacoronaviruses, which are still causing transmission among the human population worldwide. The major difference between the two coronaviruses is that MERS-CoV is now causing sporadic transmission worldwide, whereas SARS-CoV-2 is causing a pandemic outbreak globally. Currently, different guidelines and reports have highlighted several diagnostic methods and approaches which could be used to screen and confirm MERS-CoV and SARS-CoV-2 infections. These methods include clinical evaluation, laboratory diagnosis (nucleic acid-based test, protein-based test, or viral culture), and radiological diagnosis. With the presence of these different diagnostic approaches, it could cause a dilemma to the clinicians and diagnostic laboratories in selecting the best diagnostic strategies to confirm MERS-CoV and SARS-CoV-2 infections. Therefore, this review aims to provide an up-to-date comparison of the advantages and limitations of different diagnostic approaches in detecting MERS-CoV and SARS-CoV-2 infections. This review could provide insights for clinicians and scientists in detecting MERS-CoV and SARS-CoV-2 infections to help combat the transmission of these coronaviruses.
    Matched MeSH terms: Sensitivity and Specificity*
  19. Sridharan R, Yunos SM, Aziz S, Hussain RI, Alhabshi SM, Suria Hayati MP, et al.
    Med J Malaysia, 2015 Dec;70(6):326-33.
    PMID: 26988204 MyJurnal
    OBJECTIVE: The aim of this study was to compare the use of semi-automated (Medax Velox 2; Poggio Rusco, Italy) and automated (Bard Magnum Biopsy Instrument; Covington, GA, USA) core biopsy needles, for ultrasound guided breast biopsy.
    MATERIALS AND METHODS: A 14G semi-automatic spring loaded core biopsy needle with a 22-mm-throw (Medax Velox 2; Poggio Rusco, Italy) and 14-gauge automated needle device with a 22-mm-throw biopsy gun (Bard-Magnum Biopsy Instrument, Covington, GA, USA) were used for breast biopsies under ultrasound guidance on alternate months during the study period between July 2009 and May 2011. One hundred and sixty lesions were biopsied and specimens were sent for histological evaluation.
    RESULTS: The automated needle obtained a higher number of histology reports at 84% (67/80) as compared with the semiautomated needle at 60% (48/80) (Fisher exact test, p value=0.023). Inadequate samples with the automated needle were much less at 9% (7/60) than with the semiautomated needle at 23% (18/60) (Fisher exact test, p value=0.028). The semi-automated needle showed slightly less fragmented samples. However, the number of fragmented samples with definitive diagnosis was slightly higher with the automated compared with the semiautomated needle, at 16% (13/80) and 13% (10/80) respectively. Compared with histology of 29 lesions that were excised, the semi-automated needle had higher sensitivity (100%) but lower specificity (75%) and accuracy (90%) compared with the automated needle (88% sensitivity, 100% specificity, 95% accuracy).
    CONCLUSION: Definitive diagnosis from the study samples slightly favours the use of automated core biopsy needle as compared to semi-automated core biopsy needle.
    Study site: Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur
    Matched MeSH terms: Sensitivity and Specificity
  20. Azeez D, Gan KB, Mohd Ali MA, Ismail MS
    Technol Health Care, 2015;23(4):419-28.
    PMID: 25791174 DOI: 10.3233/THC-150907
    BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.
    OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error.
    METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development.
    RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data.
    CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.
    KEYWORDS: Decision support system; emergency department; random forest; randomized resampling
    Matched MeSH terms: Sensitivity and Specificity
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