Displaying publications 1 - 20 of 113 in total

  1. Jasmine Pemeena Priyadarsini M, Kotecha K, Rajini GK, Hariharan K, Utkarsh Raj K, Bhargav Ram K, et al.
    J Healthc Eng, 2023;2023:3563696.
    PMID: 36776955 DOI: 10.1155/2023/3563696
    The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient's treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  2. Radhiana H, Azian AA, Razali MR, Kamariah CM
    Med J Malaysia, 2010 Dec;65(4):319-25.
    PMID: 21901958
    Computed tomography (CT) is widely used in assessing clinically stable patients with blunt abdominal trauma. In these patients, liver is one of the commonest organs being injured and CT can accurately identify and assess the extent of the injury. The CT features of blunt liver trauma include laceration, subcapsular or parenchymal haematomas, active haemorrhage and vascular injuries. Widespread use of CT has notably influenced the management of blunt liver injury from routine surgical to nonsurgical management. We present pictorial illustrations of various liver injuries depicted on CT in patients with blunt trauma.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  3. Rais NNM, Bradley DA, Hashim A, Osman ND, Noor NM
    Appl Radiat Isot, 2019 Nov;153:108810.
    PMID: 31351374 DOI: 10.1016/j.apradiso.2019.108810
    For a range of doses familiarly incurred in computed tomography (CT), study is made of the performance of Germanium (Ge)-doped fibre dosimeters formed into cylindrical and flat shapes. Indigenously fabricated 2.3 mol% and 6 mol% Ge-dopant concentration preforms have been used to produce flat- and cylindrical-fibres (FF and CF) of various size and diameters; an additional 4 mol% Ge-doped commercial fibre with a core diameter of 50 μm has also been used. The key characteristics examined include the linearity index f(d), dose sensitivity and minimum detectable dose (MDD), the performance of the fibres being compared against that of lithium-fluoride based TLD-100 thermoluminescence (TL) dosimeters. For doses in the range 2-40 milligray (mGy), delivered at constant potential of 120 kilovoltage (kV), both the fabricated and commercial fibres demonstrate supralinear behaviours at doses  4 mGy. In terms of dose sensitivity, all of the fibres show superior TL sensitivity when compared against TLD-100, the 2.3 mol% and 6 mol% Ge-doped FF demonstrating the greatest TL sensitivity at 84 and 87 times that of TLD-100. The TL yields for the novel Ge-doped silica glass render them appealing for use within the present medical imaging dose range, offering linearity at high sensitivity down to less than 2 mGy.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  4. Tan D, Mohamad NA, Wong YH, Yeong CH, Cheah PL, Sulaiman N, et al.
    Int J Hyperthermia, 2019;36(1):554-561.
    PMID: 31132888 DOI: 10.1080/02656736.2019.1610800
    Purpose: This study aimed to evaluate the effects of various computed tomography (CT) acquisition parameters and metal artifacts on CT number measurement for CT thermometry during CT-guided thermal ablation. Methods: The effects of tube voltage (100-140 kVp), tube current (20-250 mAs), pitch (0.6-1.5) and gantry rotation time (0.5, 1.0 s) as well as metal artifacts from a radiofrequency ablation (RFA) needle on CT number were evaluated using liver tissue equivalent polyacrylamide (PAA) phantom. The correlation between CT number and temperature from 37 to 80 °C was studied on PAA phantom using optimum CT acquisition parameters. Results: No statistical significant difference (p > 0.05) was found on CT numbers under the variation of different acquisition parameters for the same temperature setting. On the other hand, the RFA needle has induced metal artifacts on the CT images of up to 8 mm. The CT numbers decreased linearly when the phantom temperature increased from 37 to 80 °C. A linear regression analysis on the CT numbers and temperature suggested that the CT thermal sensitivity was -0.521 ± 0.061 HU/°C (R2 = 0.998). Conclusion: CT thermometry is feasible for temperature assessment during RFA with the current CT technology, which produced a high CT number reproducibility and stable measurement at different CT acquisition parameters. Despite being affected by metal artifacts, the CT-based thermometry could be further developed as a tissue temperature monitoring tool during CT-guided thermal ablation.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  5. Teh V, Sim KS, Wong EK
    Scanning, 2016 Nov;38(6):842-856.
    PMID: 27302216 DOI: 10.1002/sca.21334
    According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  6. Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B
    Comput Biol Med, 2022 Jun;145:105464.
    PMID: 35390746 DOI: 10.1016/j.compbiomed.2022.105464
    BACKGROUND: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning.

    METHOD: Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.

    RESULTS: The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.

    CONCLUSION: While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  7. Tuang GJ, Zahedi FD, Husain S, Hamizan AKW, Kew TY, Thanabalan J
    Int J Med Sci, 2023;20(2):211-218.
    PMID: 36794158 DOI: 10.7150/ijms.68095
    Introduction: The fundament of forensic science lies in identifying a body. The morphological complexity of the paranasal sinus (PNS), which varies greatly amongst individual, possess a discriminatory value that potentially contributes to the radiological identification. The sphenoid bone represents the keystone of the skull and forms part of the cranial vault. It is intimately associated with vital neurovascular structures. The sphenoid sinus, located within the body of the sphenoid bone, has variable morphology. The sphenoid septum's inconsistent position and the degree, as well as the direction disparities of sinus pneumatization, have indeed accorded it a unique structure in providing invaluable information in forensic personnel identification. Additionally, the sphenoid sinus is situated deep within the sphenoid bone. Therefore, it is well protected from traumatic degradation from external causes and can be potentially utilized in forensic studies. The authors aim to study the possibility of variation among the race, and gender in the Southeast Asian (SEA) population, using volumetric measurements of the sphenoid sinus. Materials and methods: This is a retrospective cross-sectional analysis of computerized tomographic (CT) imaging of the PNS of 304 patients (167 males, 137 females) in a single centre. The volume of the sphenoid sinus was reconstructed and measured using commercial real-time segmentation software. Result: The total volume of sphenoid sinus of male gender had shown to be larger, 12.22 (4.93 - 21.09) cm3 compared to the counterpart of 10.19 (3.75 - 18.72) cm3 (p = .0090). The Chinese possessed a larger total sphenoid sinus volume, 12.96 (4.62 - 22.21) cm3) than the Malays, 10.68 (4.13 - 19.25) cm3 (p = .0057). No correlation was identified between the age and volume of the sinus (cc= -.026, p = .6559). Conclusion: The sphenoid sinus volume in males was found to be larger than those of females. It was also shown that race influences sinus volume. Volumetric analysis of the sphenoid sinus can potentially be utilized in gender and race determination. The current study provided normative data on the sphenoid sinus volume in the SEA region, which can be helpful for future studies.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  8. Raman K, Govindaraju R, James K, Abu Bakar MZ, Patil N, Shah MN
    J Laryngol Otol, 2023 Feb;137(2):169-173.
    PMID: 34924062 DOI: 10.1017/S0022215121004175
    OBJECTIVE: Knowledge of anatomical variations of the frontal recess and frontal sinus and recognition of endoscopic landmarks are vital for safe and effective endoscopic sinus surgery. This study revisited an anatomical landmark in the frontal recess that could serve as a guide to the frontal sinus.

    METHOD: Prevalence of the anterior ethmoid genu, its morphology and its relationship with the frontal sinus drainage pathway was assessed. Computed tomography scans with multiplanar reconstruction were used to study non-diseased sinonasal complexes.

    RESULTS: The anterior ethmoidal genu was present in all 102 anatomical sides studied, independent of age, gender and race. Its position was within the frontal sinus drainage pathway, and the drainage pathway was medial to it in 98 of 102 cases. The anterior ethmoidal genu sometimes extended laterally and formed a recess bounded by the lamina papyracea laterally, by the uncinate process anteriorly and by the bulla ethmoidalis posteriorly. Distance of the anterior ethmoidal genu to frontal ostia can be determined by the height of the posterior wall of the agger nasi cell rather than its volume or other dimensions.

    CONCLUSION: This study confirmed that the anterior ethmoidal genu is a constant anatomical structure positioned within frontal sinus drainage pathway. The description of anterior ethmoidal genu found in this study explained the anatomical connection between the agger nasi cell, uncinate process and bulla ethmoidalis and its structural organisation.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  9. Alzu'bi D, Abdullah M, Hmeidi I, AlAzab R, Gharaibeh M, El-Heis M, et al.
    J Healthc Eng, 2022;2022:3861161.
    PMID: 37323471 DOI: 10.1155/2022/3861161
    Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  10. Shamim S, Awan MJ, Mohd Zain A, Naseem U, Mohammed MA, Garcia-Zapirain B
    J Healthc Eng, 2022;2022:6566982.
    PMID: 35422980 DOI: 10.1155/2022/6566982
    The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-19 up to 10 January 2022. CT scans can be used as an alternative to time-consuming RT-PCR testing for COVID-19. This research work proposes a segmentation approach to identifying ground glass opacity or ROI in CT images developed by coronavirus, with a modified structure of the Unet model having been used to classify the region of interest at the pixel level. The problem with segmentation is that the GGO often appears indistinguishable from a healthy lung in the initial stages of COVID-19, and so, to cope with this, the increased set of weights in contracting and expanding the Unet path and an improved convolutional module is added in order to establish the connection between the encoder and decoder pipeline. This has a major capacity to segment the GGO in the case of COVID-19, with the proposed model being referred to as "convUnet." The experiment was performed on the Medseg1 dataset, and the addition of a set of weights at each layer of the model and modification in the connected module in Unet led to an improvement in overall segmentation results. The quantitative results obtained using accuracy, recall, precision, dice-coefficient, F1score, and IOU were 93.29%, 93.01%, 93.67%, 92.46%, 93.34%, 86.96%, respectively, which is better than that obtained using Unet and other state-of-the-art models. Therefore, this segmentation approach proved to be more accurate, fast, and reliable in helping doctors to diagnose COVID-19 quickly and efficiently.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  11. Ninomiya K, Arimura H, Tanaka K, Chan WY, Kabata Y, Mizuno S, et al.
    Comput Methods Programs Biomed, 2023 Jun;236:107544.
    PMID: 37148668 DOI: 10.1016/j.cmpb.2023.107544
    OBJECTIVES: To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes.

    METHODS: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.

    RESULTS: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.

    CONCLUSION: 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  12. Chong B, Jayabaskaran J, Ruban J, Goh R, Chin YH, Kong G, et al.
    Circ Cardiovasc Imaging, 2023 May;16(5):e015159.
    PMID: 37192298 DOI: 10.1161/CIRCIMAGING.122.015159
    BACKGROUND: Epicardial adipose tissue (EAT) has garnered attention as a prognostic and risk stratification factor for cardiovascular disease. This study, via meta-analyses, evaluates the associations between EAT and cardiovascular outcomes stratified across imaging modalities, ethnic groups, and study protocols.

    METHODS: Medline and Embase databases were searched without date restriction on May 2022 for articles that examined EAT and cardiovascular outcomes. The inclusion criteria were (1) studies measuring EAT of adult patients at baseline and (2) reporting follow-up data on study outcomes of interest. The primary study outcome was major adverse cardiovascular events. Secondary study outcomes included cardiac death, myocardial infarction, coronary revascularization, and atrial fibrillation.

    RESULTS: Twenty-nine articles published between 2012 and 2022, comprising 19 709 patients, were included in our analysis. Increased EAT thickness and volume were associated with higher risks of cardiac death (odds ratio, 2.53 [95% CI, 1.17-5.44]; P=0.020; n=4), myocardial infarction (odds ratio, 2.63 [95% CI, 1.39-4.96]; P=0.003; n=5), coronary revascularization (odds ratio, 2.99 [95% CI, 1.64-5.44]; P<0.001; n=5), and atrial fibrillation (adjusted odds ratio, 4.04 [95% CI, 3.06-5.32]; P<0.001; n=3). For 1 unit increment in the continuous measure of EAT, computed tomography volumetric quantification (adjusted hazard ratio, 1.74 [95% CI, 1.42-2.13]; P<0.001) and echocardiographic thickness quantification (adjusted hazard ratio, 1.20 [95% CI, 1.09-1.32]; P<0.001) conferred an increased risk of major adverse cardiovascular events.

    CONCLUSIONS: The utility of EAT as an imaging biomarker for predicting and prognosticating cardiovascular disease is promising, with increased EAT thickness and volume being identified as independent predictors of major adverse cardiovascular events.

    REGISTRATION: URL: https://www.crd.york.ac.uk/prospero; Unique identifier: CRD42022338075.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  13. Joshi SC, Pant I, Hamzah F, Kumar G, Shukla AN
    Indian J Cancer, 2008 12 30;45(4):137-41.
    PMID: 19112200
    Positron emission tomography (PET) has emerged as an important diagnostic tool in the management of lung cancers. Although PET is sensitive in detection of lung cancer, but FDG (2-deoxy-2- 18 fluro-D-glucose) is not tumor specific and may accumulate in a variety of nonmalignant conditions occasionally giving false positive result. Addition of CT to PET improves specificity foremost, but also sensitivity in tumor imaging. Thus, PET/CT fusion images are a more accurate test than either of its individual components and are probably also better than side-by-side viewing of images from both modalities. PET/CT fusion images are useful in differentiating between malignant and benign disease, fibrosis and recurrence, staging and in changing patient management to more appropriate therapy. With analysis and discussion it appears that PET/ CT fusion images have the potential to dramatically improve our ability to manage the patients with lung cancer and is contributing to our understanding of cancer cell biology and in development of new therapies.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  14. Al-Ameen Z, Sulong G
    Interdiscip Sci, 2015 Sep;7(3):319-25.
    PMID: 26199211 DOI: 10.1007/s12539-015-0022-1
    In computed tomography (CT), blurring occurs due to different hardware or software errors and hides certain medical details that are present in an image. Image blur is difficult to avoid in many circumstances and can frequently ruin an image. For this, many methods have been developed to reduce the blurring artifact from CT images. The problems with these methods are the high implementation time, noise amplification and boundary artifacts. Hence, this article presents an amended version of the iterative Landweber algorithm to attain artifact-free boundaries and less noise amplification in a faster application time. In this study, both synthetic and real blurred CT images are used to validate the proposed method properly. Similarly, the quality of the processed synthetic images is measured using the feature similarity index, structural similarity and visual information fidelity in pixel domain metrics. Finally, the results obtained from intensive experiments and performance evaluations show the efficiency of the proposed algorithm, which has potential as a new approach in medical image processing.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  15. Sulong S, Alias A, Johanabas F, Yap Abdullah J, Idris B
    J Craniofac Surg, 2019 8 14;31(1):46-50.
    PMID: 31403510 DOI: 10.1097/SCS.0000000000005810
    BACKGROUND: Craniosynostosis is a congenital defect that causes ≥1 suture to fuse prematurely. Cranial expansion surgery which consists of cranial vault reshaping with or without fronto-orbital advancement (FOA) is done to correct the skull to a more normal shape of the head as well as to increase the intracranial volume (ICV). Therefore, it is important to evaluate the changes of ICV after the surgery and the effect of surgery both clinically and radiologically.

    OBJECTIVE: The aim of this study is to evaluate the ICV in primary craniosynostosis patients after the cranial vault reshaping with or without FOA and to compare between syndromic and nonsyndromic synostosis group, to determine factors that associated with significant changes in the ICV postoperative, and to evaluate the resolution of copper beaten sign and improvement in neurodevelopmental delay after the surgery.

    METHODS: This is a prospective observational study of all primary craniosynostosis patients who underwent operation cranial vault reshaping with or without FOA in Hospital Kuala Lumpur from January 2017 until Jun 2018. The ICV preoperative and postoperative was measured using the 3D computed tomography (CT) imaging and analyzed. The demographic data, clinical and radiological findings were identified and analyzed.

    RESULTS: A total of 14 cases (6 males and 8 females) with 28 3D CT scans were identified. The mean age of patients was 23 months. Seven patients were having syndromic synostosis (4 Crouzon syndromes and 3 Apert syndromes) and 7 nonsyndromic synostosis. The mean preoperative ICV was 880 mL (range, 641-1234 mL), whereas the mean postoperative ICV was 1081 mL (range, 811-1385 mL). The difference was 201 mL which was statistically significant (P  1.0). However, there was 100% (n = 13) improvement of this copper beaten sign. However, the neurodevelopmental delay showed no improvement which was statistically not significant (P > 1.0).

    CONCLUSION: Surgery in craniosynostosis patient increases the ICV besides it improves the shape of the head. From this study, the syndromic synostosis had better increment of ICV compared to nonsyndromic synostosis.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  16. Ng BH, Nuratiqah NA, Andrea YLB, Faisal AH, Soo CI, Najma K, et al.
    Med J Malaysia, 2020 07;75(4):368-371.
    PMID: 32723996
    BACKGROUND AND OBJECTIVE: Coronavirus Disease 2019 (COVID- 19) was first reported in Malaysia in March 2020. We describe here the clinical characteristics and computed tomography (CT) patterns in asymptomatic young patients who had laboratory-confirmed COVID-19.

    METHODS: This is a retrospective observational study where 25 male in-patients with laboratory-confirmed COVID-19 in Hospital Canselor Tuanku Muhriz. Demographics, clinical data and CT images of these patients were reviewed by 2 senior radiologists.

    RESULTS: In total there were 25 patients (all males; mean age [±SD], 21.64±2.40 years; range, 18-27 years). Patients with abnormal chest CT showed a relatively low normal absolute lymphocytes count (median: 2.2 x 109/L) and absolute monocyte count (median: 0.5 x 109/L). Lactate dehydrogenase was elevated in 5 (20%) of the patients. The procalcitonin level was normal while elevated levels of alanine aminotransferase, total bilirubin, platelet and C-reactive protein were common. Baseline chest CT showed abnormalities in 6 patients. The distribution of the lesions were; upper lobe 3 (12%) lower lobe 3 (12%) with peripheral distribution 4 (16%). Of the 25 patients included, 4 (16%) had ground glass opacification (GGO), 1 (4%) had a small peripheral subpleural nodule, and 1 (4%) had a dense solitary granuloma. Four patients had typical CT features of COVID-19.

    CONCLUSION: We found that the CT imaging showed peripheral GGO in our patients. They remained clinically stable with no deterioration of their respiratory symptoms suggesting stability in lung involvement. We postulate that rapid changes in CT imaging may not be present in young, asymptomatic, non-smoking COVID-19 patients. Thus the use of CT thoraxfor early diagnosis may be reserved for patients in the older agegroups, and not in younger patients.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  17. Al-Shabi M, Lan BL, Chan WY, Ng KH, Tan M
    Int J Comput Assist Radiol Surg, 2019 Oct;14(10):1815-1819.
    PMID: 31020576 DOI: 10.1007/s11548-019-01981-7
    PURPOSE: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.

    METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.

    RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  18. Dong X, Xu S, Liu Y, Wang A, Saripan MI, Li L, et al.
    Cancer Imaging, 2020 Aug 01;20(1):53.
    PMID: 32738913 DOI: 10.1186/s40644-020-00331-0
    BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge.

    METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.

    RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.

    CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  19. Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R
    Sci Rep, 2021 07 08;11(1):14133.
    PMID: 34238992 DOI: 10.1038/s41598-021-93658-y
    COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  20. Tawfiq Zyoud TY, Abdul Rashid SN, Suppiah S, Abdul Rahim E, Mahmud R
    Med J Malaysia, 2020 07;75(4):411-418.
    PMID: 32724006
    INTRODUCTION: Autopsy is one of the most important approaches to identify clearly the exact cause of death, whether it was due to natural causes, sudden death, or traumatic. Various studies have been done in different countries regarding ways to improve the diagnosis during autopsy. The imaging approach is one of the methods that has been used to complement autopsy findings and to enhance the diagnosis for achieving the most accurate post-mortem diagnosis. The aim of this study is to identify the role of imaging modalities that complement routine autopsy and correlate the findings of diagnostic imaging that can help improve the accuracy of diagnosing the cause of death.

    METHODS: We sourced articles from Scopus, Ovid and PubMed databases for journal publications related to post-mortem diagnostic imaging. We highlight the most relevant full articles in English that explain the type of modality that was utilised and the added value it provided for diagnosing the cause of death.

    RESULTS: Minimally invasive autopsies assisted by imaging modalities added a great benefit to forensic medicine, and supported conventional autopsy. In particular the role of post mortem computed tomography (PMCT), post mortem computed tomography angiography (PMMR) and positron emission tomography computed tomography (PMCTA) that have incremental benefits in diagnosing traumatic death, fractures, tissue injuries, as well as the assessment of body height or weight for corpse identification.

    CONCLUSION: PMCT and PMMR, with particular emphasis on PMCTA, can provide higher accuracy than the other modalities. They can be regarded as indispensable methods that should be applied to the routine autopsy protocol, thus improving the findings and accuracy of diagnosing the cause of death.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
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