Displaying publications 1 - 20 of 54 in total

    Med J Malaya, 1960 Dec;15:65-9.
    PMID: 13773946
    Matched MeSH terms: Thorax/abnormalities*
  2. Hanafi HR, Zakaria ZA
    Case Rep Obstet Gynecol, 2017;2017:9821213.
    PMID: 29348951 DOI: 10.1155/2017/9821213
    Thoracoschisis is a rare congenital malformation characterized by herniation of the abdominal content through a defect in the thorax. There are previously 12 reported cases, most discussing the postnatal findings and management. Here we describe a case of left thoracoschisis with associated upper limb abnormality which was diagnosed antenatally with the aid of 3D ultrasound.
    Matched MeSH terms: Thorax
    Med J Malaya, 1955 Jun;9(4):276-80.
    PMID: 13253127
    Matched MeSH terms: Thorax*; Thoracic Wall*
  4. Albadr MAA, Tiun S, Ayob M, Al-Dhief FT, Omar K, Hamzah FA
    PLoS One, 2020;15(12):e0242899.
    PMID: 33320858 DOI: 10.1371/journal.pone.0242899
    The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.
    Matched MeSH terms: Thorax/physiopathology; Thorax/virology
    Tubercle, 1960 Apr;41:103-8.
    PMID: 13832282
    Matched MeSH terms: Thorax*
  6. Ong PT, Yong JC, Chin KY, Hii YS
    Chemosphere, 2011 Jul;84(5):578-84.
    PMID: 21529890 DOI: 10.1016/j.chemosphere.2011.03.059
    Understanding on the bioaccumulation and depuration of PAHs (polycyclic aromatic hydrocarbons) in Penaeus monodon is important in seafood safety because it is one of the most popular seafood consumed worldwide. In this study, we used anthracene as the precursor compound for PAHs accumulation and depuration in the shrimp. Commercial feed pellets spiked with anthracene were fed to P. monodon. At 20 mg kg(-1) anthracene, P. monodon accumulated 0.1% of the anthracene from the feed. P. monodon deputed the PAH two times faster than its accumulation. The shrimp reduced its feed consumption when anthracene content in the feed exceeded 20 mg kg(-1). At 100 mg kg(-1) anthracene, P. monodon started to have necrosis tissues on the posterior end of their thorax. The bioaccumulation factor (BAF), uptake rate constant (k(1)) and depuration rate constant (k(2)) of anthracene in P. monodon were 1.15×10(-3), 6.80×10(-4) d(-1) and 6.28×10(-1) d(-1), respectively. The depuration rate constant is about thousand times higher than the uptake rate constant and this indicated that this crustacean is efficient in depurating hydrocarbons from their tissue.
    Matched MeSH terms: Thorax/drug effects; Thorax/metabolism; Thorax/pathology
  7. Ramanaidu S, Sta Maria R, Ng Kh, George J, Kumar G
    Biomed Imaging Interv J, 2006 Jul;2(3):e35.
    PMID: 21614244 MyJurnal DOI: 10.2349/biij.2.3.e35
    A study of radiation dose and image quality following changes to the tube potential (kVp) in paediatric chest radiography.
    Matched MeSH terms: Thorax
  8. Moey, Soo-Foon, Nur Farah Hani Muhd Jaafar, Nursyahirah Saidin
    Introduction: Various medium and high tube potentials were utilized to conduct chest x-rays. There
    are advantages and disadvantages with regards to image quality and radiation dose when using
    medium and high kilovoltage (kVp) technique. However, radiographers have misconstrued
    understanding pertaining to the choice of tube potential as well as grid usage when performing chest radiography. Methods: The experimental study was conducted using the PBU-50 phantom by exposing it with medium kVp utilizing grid and non-grid as well as high kVp with grid. All images obtained were evaluated using the modified evaluation criteria for PA chest established by the Commission of European Communities, 1996 whilst the dose area product (DAP) was determined using the Dose Area Product (DAP) meter. The value obtained from the DAP meter was converted to entrance surface dose (ESD) usingCALDOSE_X5.0 software and mathematical formula. Results: The Wilcoxon Signed-Rank Test indicated a significant difference in ESD when using medium and high kVp; Z= -2.666, p
    Matched MeSH terms: Thorax
  9. Ohn MH, Ohn KM
    BMJ Case Rep, 2021 May 31;14(5).
    PMID: 34059541 DOI: 10.1136/bcr-2020-241408
    Poland's syndrome (PS) is a rare developmental anomaly that can manifest mild (pectoralis muscles involvement) to severe deformities (rib hypoplasia and hand deformities). We report a case of 69-year-old man who presented to the emergency department with a traumatic chest injury after a fall. It was initially thought to have a significant chest injury as the trauma survey revealed a palpable defect and tenderness in the right anterior chest wall. There was also a symbrachydactyly deformity in the right hand. CT of the chest showed lack of right pectoralis muscles, which were consistent with PS. This case highlights the importance of gathering detail history in adult trauma patients such as congenital disorder especially in the presence of bony deformity. With possibilities of several traumatic conditions in trauma patients eliminated, one can expand the non-traumatic differential, keeping in mind the possibility of a congenital disorder that can mimic traumatic chest injury.
    Matched MeSH terms: Thorax
  10. 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: Thorax
  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: Thorax
  12. Chang KM, Chun YT, Chen SH, Lu L, Su HT, Liang HM, et al.
    Sensors (Basel), 2016 Jul 20;16(7).
    PMID: 27447641 DOI: 10.3390/s16071126
    Chan Ding training is beneficial to health and emotional wellbeing. More and more people have taken up this practice over the past few years. A major training method of Chan Ding is to focus on the ten Mailuns, i.e., energy points, and to maintain physical stillness. In this article, wireless wearable accelerometers were used to detect physical stillness, and the created physical stillness index (PSI) was also shown. Ninety college students participated in this study. Primarily, accelerometers used on the arms and chest were examined. The results showed that the PSI values on the arms were higher than that of the chest, when participants moved their bodies in three different ways, left-right, anterior-posterior, and hand, movements with natural breathing. Then, they were divided into three groups to practice Chan Ding for approximately thirty minutes. Participants without any Chan Ding experience were in Group I. Participants with one year of Chan Ding experience were in Group II, and participants with over three year of experience were in Group III. The Chinese Happiness Inventory (CHI) was also conducted. Results showed that the PSI of the three groups measured during 20-30 min were 0.123 ± 0.155, 0.012 ± 0.013, and 0.001 ± 0.0003, respectively (p < 0.001 ***). The averaged CHI scores of the three groups were 10.13, 17.17, and 25.53, respectively (p < 0.001 ***). Correlation coefficients between PSI and CHI of the three groups were -0.440, -0.369, and -0.537, respectively (p < 0.01 **). PSI value and the wearable accelerometer that are presently available on the market could be used to evaluate the quality of the physical stillness of the participants during Chan Ding practice.
    Matched MeSH terms: Thorax*
  13. Suzana, A.H., Hasyma, A.H., Suraini, M.S., Saiful Nizam, A.R.
    In this study, we report an extremely rare case of liposarcoma which arises primarily in mediastinum. The patient appeared to have progressive dyspnoea and prolonged cough for a duration of one year. Chest radiograph and Computed Tomography (CT) of the thorax revealed a large right mediastinal mass with fatty component. It was confirmed to be primary liposarcoma on histopathological examination.
    Matched MeSH terms: Thorax
  14. Norlijah, O., Abu, M.N., Mohd Nor, A., Yip, C.W.
    Endobronchial tuberculosis is an uncommon manifestation involving the tracheobronchial tree. The clinical presentation is typically non-specific. We report this unusual complication of pulmonary tuberculosis initially diagnosed as foreign body in a 16-month-old child.
    Matched MeSH terms: Thorax
  15. Raemy Md. Zein, Noorul Azreen Azis, Isa Halim, Adi Saptari, Seri Rahayu Kamat
    Working in a safe working posture is a necessity to enhance occupational health of industrial workers. Poor
    working posture may lead to injuries, discomfort and fatigue to the workers. The objective of this study is to survey the
    postures practised by the Malaysian industrial workers. A questionnaire survey was performed among 282 Malaysian
    industrial workers in 10 different industries. From the answered questionnaire, it was observed that shoulder at chest
    level (30.1%), back in a bent forward (33.3%) and lifting heavy load (44.7%) are the major work postures practised by
    most of industrial workers. This survey identifies that working with shoulder and hand at chest level and back region
    moderately bent forward is the main working posture practice by worker. Workers also reported lifting load below 5 kg
    at the workstation. This survey recommended for industrial workers to be aware of the comfortable working posture
    to avoid injury.
    Matched MeSH terms: Thorax
  16. Michael A, Yahya ZO, Mdrazali I, Hanif H
    Med J Malaysia, 2017 02;72(1):75-76.
    PMID: 28255150 MyJurnal
    Penetrating chest wounds is less common but more deadly then blunt trauma. Majority of penetrating chest trauma can be managed conservatively with observation and simple thoracotomy. This case report highlights a bizarre occupational hazard causing a penetrating chest injury and the option of non-invasive management with the aid of computed tomography with 3D reconstruction.
    Matched MeSH terms: Thorax
  17. Wong HK, Stephen ID
    J Eye Mov Res, 2019 Aug 05;12(2).
    PMID: 33828723 DOI: 10.16910/jemr.12.2.5
    Human behaviour is not only influenced by the physical presence of others, but also implied social presence. This study examines the impact of awareness of being eye-tracked on eye movement behaviour in a laboratory setting. During a classic yes/no face recognition task, participants were made to believe that their eye movements were recorded (or not recorded) by eye trackers. Their looking patterns with and without the awareness of being eye-tracked were compared while perceiving social (faces, faces-and-bodies) and non-social (inanimate objects) video stimuli. Area-of-interest (AOI) analysis revealed that misinformed participants (who were not aware that their eye movements were being recorded) looked more at the body (chest and waist) compared to informed participants (who believed they were being eye-tracked), whereas informed participants fixated longer on the mouth and shorter on the eyes of female models than misinformed participants did. These findings highlight the potential impact of an awareness of being eye tracked on one's eye movement pattern when perceiving a social stimulus. We conclude that even within laboratory settings an eye tracker may function as an implied social presence that leads individuals to modify their eye movement behaviour according to socially-derived inhibitory norms.
    Matched MeSH terms: Thorax
  18. Cho YH, Seo JB, Lee SM, Kim N, Yun J, Hwang JE, et al.
    Eur Radiol, 2021 Oct;31(10):7316-7324.
    PMID: 33847809 DOI: 10.1007/s00330-021-07747-7
    OBJECTIVES: To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).

    METHODS: This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.

    RESULTS: The five features remaining after the LASSO analysis were %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.

    CONCLUSIONS: A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.

    KEY POINTS: • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

    Matched MeSH terms: Thorax
  19. Sachithanandan A, Tan YS, Abdul Muis J, Rapi AR, Mohd Arif MN, Badmanaban B, et al.
    Med J Malaysia, 2014 Apr;69(2):92-4.
    PMID: 25241820 MyJurnal
    Traumatic chest injury with complete tracheo- bronchial disruption is uncommon and occurs in approximately 1% of motor vehicle accidents (MVA) (1,2). Such injuries carry a high mortality and patients rarely survive transfer to hospital. A high index of suspicion facilitates early diagnosis. Early operative intervention is vital for survival. We describe a rare case of traumatic complete disruption of the right mainstem bronchus (RMB) due to blunt chest trauma. The transected airway was reanastomosed emergently avoiding a lung resection.
    Matched MeSH terms: Thorax
  20. Bux S, Mohd Ramli N, Ahmad Sarji S, Kamarulzaman A
    Biomed Imaging Interv J, 2010 Oct-Dec;6(4):e35.
    PMID: 21611071 MyJurnal DOI: 10.2349/biij.6.4.e35
    This is a retrospective descriptive study of the chest imaging findings of 118 patients with confirmed A(H1N1) in a tertiary referral centre. About 42% of the patients had positive initial chest radiographic (CXR) findings. The common findings were bi-basal air-space opacities and perihilar reticular and alveolar infiltrates. In select cases, high-resolution computed tomography (CT) imaging showed ground-glass change with some widespread reticular changes and atelectasis.
    Matched MeSH terms: Thorax
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