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  1. Fulltext Enhanced performance of an innovative dengue IgG/IgM rapid diagnostic test using an anti-dengue EDI monoclonal antibody and dengue virus antigen
    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. Fulltext Reliability and validity of the Malay version of the General Health Questionnaire (GHQ - 12) among urological patients
    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. Fulltext Diagnostic performance of contrast-enhanced ultrasound in the evaluation of renal masses in patients with renal impairment
    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. Fulltext Detection of Plasmodium knowlesi in whole blood samples with sandwich enzyme-linked immunosorbent assay (ELISA) using rhoptry-associated protein 1 specific polyclonal antibodies
    Wan Nazri WSM, Ling LY, Wen CF
    J Vector Borne Dis, 2024 Apr 01;61(2):203-210.
    PMID: 38922654 DOI: 10.4103/jvbd.jvbd_55_23
    BACKGROUND OBJECTIVES: Plasmodium knowlesi, a simian malaria species, is now known to infect humans. Due to disadvantages in the current diagnosis methods, many efforts have been placed into developing new methods to diagnose the disease. This study assessed the ability of the PkRAP-1 sandwich enzyme-linked immunosorbent (ELISA) to detect P knowlesi antigens in whole blood specimens.

    METHODS: Western blot assay was conducted to evaluate the ability of raised mouse and rabbit anti-PkRAP-1 polyclonal antibodies to bind to the native proteins in P. knowlesi lysate. The polyclonal antibodies were then used in sandwich ELISA to detect P. knowlesi. In the sandwich ELISA, mouse and rabbit polyclonal antibodies were used as the capture and detection antibodies, respectively. The limit of detection (LOD) of the assay was determined using P. knowlesi A1H1 culture and purified recombinant PkRAP-1.

    RESULTS: Western blot results showed positive reactions towards the proteins in P. knowlesi lysate. The LOD of the assay from three technical replicates was 0.068% parasitaemia. The assay performance in detecting P. knowlesi was 83% sensitivity and 70% specificity with positive and negative predictive values of 74% and 80%, respectively. The anti-PkRAP-1 polyclonal antibodies did not cross-react with P. falciparum and healthy samples, but P. vivax by detecting all 12 samples.

    INTERPRETATION CONCLUSION: PkRAP-1 has the potential as a biomarker for the development of a new diagnostic tool for P. knowlesi detection. Further studies need to be conducted to establish the full potential of the usage of anti-PkRAP-1 antibodies for P. knowlesi detection.

    Matched MeSH terms: Sensitivity and Specificity*
  5. Colorimetric strategies applicable for loop-mediated isothermal amplification
    Saifuddin SA, Rashid R, Nor Azmi NJ, Mohamad S
    J Microbiol Methods, 2024 Aug;223:106981.
    PMID: 38945305 DOI: 10.1016/j.mimet.2024.106981
    In recent years, loop-mediated isothermal amplification (LAMP) has gained popularity for detecting various pathogen-specific genes due to its superior sensitivity and specificity compared to conventional polymerase chain reaction (PCR). The simplicity and flexibility of naked-eye detection of the amplicon make LAMP an ideal rapid and straightforward diagnostic tool, especially in resource-limited laboratories. Colorimetric detection is one of the simplest and most straightforward among all detection methods. This review will explore various colorimetric dyes used in LAMP techniques, examining their reaction mechanisms, advantages, limitations and latest applications.
    Matched MeSH terms: Sensitivity and Specificity*
  6. Fulltext Diffusion weighted MR imaging in acute vertebral compression fractures: differentiation between malignant and benign causes
    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
  7. Fulltext Complete Genome Sequence of Mycobacterium tuberculosis Clinical Isolate Spoligotype SIT745/EAI1-MYS
    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
  8. Fulltext Diagnostic Performance and Eye Movement Parameters during Radiographic Image Interpretation among Radiographers
    Ibrahim, M. I., Mohd Norsuddin, N., Che Isa, I. N., Azman, N. F., Mohamad Shahimin, M.
    Physics and Technology in Medicine, 2020;1(2):12-19.
    MyJurnal
    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
  9. Fulltext COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings
    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
  10. Fulltext Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network
    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
  11. Fulltext Model-based insulin sensitivity for early diagnosis of sepsis in critical care
    Wan Fadzlina Wan Muhd Shukeri, Azrina Md. Ralib, Ummu Khultum Jamaludin, Mohd Basri Mat-Nor
    International Medical Journal Malaysia, 2017;16(11):9-9.
    MyJurnal
    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
  12. Fulltext Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
    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
  13. Chemometrics analysis for the detection of dental caries via UV absorption spectroscopy
    Basri KN, Yazid F, Megat Abdul Wahab R, Mohd Zain MN, Md Yusof Z, Zoolfakar AS
    Spectrochim Acta A Mol Biomol Spectrosc, 2022 Feb 05;266:120464.
    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
  14. Fulltext Neuroimaging findings are sensitive and specific in diagnosis of tuberculous meningitis
    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.
    MyJurnal
    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
  15. Intra-Operative Frozen Sections for Ovarian Tumors – A Tertiary Center Experience
    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
  16. Mini Nutritional Assessment for Adult Cancer Patients: A Systematic Review and Meta-Analysis
    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
  17. High referral accuracy for oral cancers and oral potentially malignant disorders using telemedicine
    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
  18. Artificial neural network and convolutional neural network for prediction of dental caries
    Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS
    Spectrochim Acta A Mol Biomol Spectrosc, 2024 May 05;312:124063.
    PMID: 38394882 DOI: 10.1016/j.saa.2024.124063
    Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
    Matched MeSH terms: Sensitivity and Specificity
  19. The performance of ultrasound and upper gastrointestinal study in diagnosing malrotation in children, with or without volvulus
    Mohamed Burhan MS, Hamid HA, Zaki FM, Ning CJ, Zainal IA, Ros IAC, et al.
    Emerg Radiol, 2024 Apr;31(2):151-165.
    PMID: 38289574 DOI: 10.1007/s10140-024-02201-9
    BACKGROUND: Rapid diagnosis is crucial for pediatric patients with midgut volvulus and malrotation to prevent serious complications. While the upper gastrointestinal study (UGIS) is the traditional method, the use of ultrasound (US) is gaining prominence.

    OBJECTIVES: To assess the diagnostic sensitivity and specificity of US compared to UGIS for malrotation and midgut volvulus.

    METHODS: A cross-sectional study was performed on 68 pediatric patients who underwent US and/or UGIS before surgery for suspected midgut volvulus or malrotation in Kuala Lumpur (PPUKM and HTA), referencing surgical outcomes as the gold standard.

    RESULTS: US demonstrated a higher specificity (100%) than UGIS (83%) for diagnosing malrotation, with a slightly lower sensitivity (97% vs. 100%). For midgut volvulus, US surpassed UGIS in sensitivity (92.9% vs. 66.7%) while maintaining comparable specificity. The SMA/SMV criteria showed better sensitivity (91.1%) than the D3 assessment (78.9%) on US, though both had high specificity.

    CONCLUSION: US is equivalent to UGIS for identifying malrotation and is more sensitive for detecting midgut volvulus, supporting its use as a primary diagnostic tool. The study advocates for combined US and UGIS when either yields inconclusive results, optimizing diagnostic precision for these conditions.

    Matched MeSH terms: Sensitivity and Specificity
  20. An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model
    Yousefpanah K, Ebadi MJ, Sabzekar S, Zakaria NH, Osman NA, Ahmadian A
    Acta Trop, 2024 Sep;257:107277.
    PMID: 38878849 DOI: 10.1016/j.actatropica.2024.107277
    Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagnosis via computed tomography (CT) samples. In this paper, artificial intelligence-based methods are introduced to diagnose COVID-19. At first, a network called CT6-CNN is designed, and then two ensemble deep transfer learning models are developed based on Xception, ResNet-101, DenseNet-169, and CT6-CNN to reach a COVID-19 diagnosis by CT samples. The publicly available SARS-CoV-2 CT dataset is utilized for our implementation, including 2481 CT scans. The dataset is separated into 2108, 248, and 125 images for training, validation, and testing, respectively. Based on experimental results, the CT6-CNN model achieved 94.66% accuracy, 94.67% precision, 94.67% sensitivity, and 94.65% F1-score rate. Moreover, the ensemble learning models reached 99.2% accuracy. Experimental results affirm the effectiveness of designed models, especially the ensemble deep learning models, to reach a diagnosis of COVID-19.
    Matched MeSH terms: Sensitivity and Specificity
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