Displaying publications 61 - 80 of 108 in total

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  1. Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria
    Yamin D, Uskoković V, Wakil AM, Goni MD, Shamsuddin SH, Mustafa FH, et al.
    Diagnostics (Basel), 2023 Oct 18;13(20).
    PMID: 37892067 DOI: 10.3390/diagnostics13203246
    Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.
  2. Characterization of New Alpha Zero (α0) Thalassaemia Deletion (--GB) among Malays in Malaysian Population
    Yasin NM, Abdul Hamid FS, Hassan S, Mat Yusoff Y, Mohd Sahid EN, Esa E
    Diagnostics (Basel), 2023 Oct 23;13(20).
    PMID: 37892108 DOI: 10.3390/diagnostics13203286
    Malaysia is a multicultural and multiethnic country comprising numerous ethnic groups. From the total population of 32.7 million, Malays form the bulk of the Bumiputera in Malaysia comprise about 69.9%, followed by Chinese 22.8%, Indian 6.6%, and others 0.7%. The heterogeneous population and increasing numbers of non-citizens in this country affects the heterogeneity of genetic diseases, diversity, and heterogeneity of thalassaemia mutations. Alpha (α)-thalassaemia is an inherited haemoglobin disorder characterized by hypochromic microcytic anaemia caused by a quantitative reduction in the α-globin chain. A majority of the α-thalassaemia are caused by deletions in the α-globin gene cluster. Among Malays, the most common deletional alpha thalassaemia is -α3.7 deletion followed by --SEA deletion. We described the molecular characterization of a new --GB deletion in our population, involving both alpha genes in cis. Interestingly, we found that this mutation is unique among Malay ethnicities. It is important to diagnose this deletion because of the 25% risk of Hb Bart's with hydrops fetalis in the offspring when in combination with another α0- thalassaemia allele. MLPA is a suitable method to detect unknown and uncommon deletions and to characterize those cases which remain unresolved after a standard diagnostic approach.
  3. Fulltext The Performance of a Calcaneal Quantitative Ultrasound Device, CM-200, in Stratifying Osteoporosis Risk among Malaysian Population Aged 40 Years and Above
    Subramaniam S, Chan CY, Soelaiman IN, Mohamed N, Muhammad N, Ahmad F, et al.
    Diagnostics (Basel), 2020 Mar 25;10(4).
    PMID: 32218298 DOI: 10.3390/diagnostics10040178
    BACKGROUND: Calcaneal quantitative ultrasound (QUS) is widely used in osteoporosis screening, but the cut-off values for risk stratification remain unclear. This study validates the performance of a calcaneal QUS device (CM-200) using dual-energy X-ray absorptiometry (DXA) as the reference and establishes a new set of cut-off values for CM-200 in identifying subjects with osteoporosis.

    METHODS: The bone health status of Malaysians aged ≥40 years was assessed using CM-200 and DXA. Sensitivity, specificity, area under the curve (AUC) and the optimal cut-off values for risk stratification of CM-200 were determined using receiver operating characteristic (ROC) curves and Youden's index (J). Results: From the data of 786 subjects, CM-200 (QUS T-score 0.05). Modified cut-off values for the QUS T-score improved the performance of CM-200 in identifying subjects with osteopenia (sensitivity 67.7% (95% CI: 62.8-72.3%); specificity 72.8% (95% CI: 68.1-77.2%); J = 0.405; AUC 0.702 (95% CI: 0.666-0.739); p < 0.001) and osteoporosis (sensitivity 79.4% (95% CI: 70.0-86.9%); specificity 61.8% (95% CI: 58.1-65.5%); J = 0.412; AUC 0.706 (95% CI: 0.654-0.758); p < 0.001). Conclusion: The modified cut-off values significantly improved the performance of CM-200 in identifying individuals with osteoporosis. Since these values are device-specific, optimization is necessary for accurate detection of individuals at risk for osteoporosis using QUS.

  4. Fulltext Development of Multiplexed Infectious Disease Lateral Flow Assays: Challenges and Opportunities
    Mohd Hanafiah K, Arifin N, Bustami Y, Noordin R, Garcia M, Anderson D
    Diagnostics (Basel), 2017 Sep 07;7(3).
    PMID: 28880218 DOI: 10.3390/diagnostics7030051
    Lateral flow assays (LFAs) are the mainstay of rapid point-of-care diagnostics, with the potential to enable early case management and transform the epidemiology of infectious disease. However, most LFAs only detect single biomarkers. Recognizing the complex nature of human disease, overlapping symptoms and states of co-infections, there is increasing demand for multiplexed systems that can detect multiple biomarkers simultaneously. Due to innate limitations in the design of traditional membrane-based LFAs, multiplexing is arguably limited to a small number of biomarkers. Here, we summarize the need for multiplexed LFA, key technical and operational challenges for multiplexing, inherent in the design and production of multiplexed LFAs, as well as emerging enabling technologies that may be able to address these challenges. We further identify important areas for research in efforts towards developing multiplexed LFAs for more impactful diagnosis of infectious diseases.
  5. Fulltext Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism
    Zulkifley MA, Mohamed NA, Abdani SR, Kamari NAM, Moubark AM, Ibrahim AA
    Diagnostics (Basel), 2021 Apr 24;11(5).
    PMID: 33923215 DOI: 10.3390/diagnostics11050765
    Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich-Pyle (GP) or Tanner-Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.
  6. Fulltext Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach
    Awan MJ, Rahim MSM, Salim N, Mohammed MA, Garcia-Zapirain B, Abdulkareem KH
    Diagnostics (Basel), 2021 Jan 11;11(1).
    PMID: 33440798 DOI: 10.3390/diagnostics11010105
    The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
  7. Fulltext Worldwide Prevalence of Epstein-Barr Virus in Patients with Burkitt Lymphoma: A Systematic Review and Meta-Analysis
    Al-Khreisat MJ, Ismail NH, Tabnjh A, Hussain FA, Mohamed Yusoff AA, Johan MF, et al.
    Diagnostics (Basel), 2023 Jun 15;13(12).
    PMID: 37370963 DOI: 10.3390/diagnostics13122068
    Burkitt lymphoma (BL) is a form of B-cell malignancy that progresses aggressively and is most often seen in children. While Epstein-Barr virus (EBV) is a double-stranded DNA virus that has been linked to a variety of cancers, it can transform B lymphocytes into immortalized cells, as shown in BL. Therefore, the estimated prevalence of EBV in a population may assist in the prediction of whether this population has a high risk of increased BL cases. This systematic review and meta-analysis aimed to estimate the prevalence of Epstein-Barr virus in patients with Burkitt lymphoma. Using the appropriate keywords, four electronic databases were searched. The quality of the included studies was assessed using the Joanna Briggs Institute's critical appraisal tool. The results were reported as percentages with a 95% confidence interval using a random-effects model (CI). PROSPERO was used to register the protocol (CRD42022372293), and 135 studies were included. The prevalence of Epstein-Barr virus in patients with Burkitt lymphoma was 57.5% (95% CI: 51.5 to 63.4, n = 4837). The sensitivity analyses demonstrated consistent results, and 65.2% of studies were of high quality. Egger's test revealed that there was a significant publication bias. EBV was found in a significantly high proportion of BL patients (more than 50% of BL patients). This study recommends EBV testing as an alternative for predictions and the assessment of the clinical disease status of BL.
  8. Fulltext Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
    Zedan MJM, Zulkifley MA, Ibrahim AA, Moubark AM, Kamari NAM, Abdani SR
    Diagnostics (Basel), 2023 Jun 26;13(13).
    PMID: 37443574 DOI: 10.3390/diagnostics13132180
    Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.
  9. Fulltext Privacy-Aware Collaborative Learning for Skin Cancer Prediction
    Ain QU, Khan MA, Yaqoob MM, Khattak UF, Sajid Z, Khan MI, et al.
    Diagnostics (Basel), 2023 Jul 04;13(13).
    PMID: 37443658 DOI: 10.3390/diagnostics13132264
    Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.
  10. Fulltext YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images
    Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M
    Diagnostics (Basel), 2023 Jul 05;13(13).
    PMID: 37443674 DOI: 10.3390/diagnostics13132280
    Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.
  11. Fulltext Biomarkers for Temporomandibular Disorders: Current Status and Future Directions
    Zwiri A, Al-Hatamleh MAI, W Ahmad WMA, Ahmed Asif J, Khoo SP, Husein A, et al.
    Diagnostics (Basel), 2020 May 15;10(5).
    PMID: 32429070 DOI: 10.3390/diagnostics10050303
    Numerous studies have been conducted in the previous years with an objective to determine the ideal biomarker or set of biomarkers in temporomandibular disorders (TMDs). It was recorded that tumour necrosis factor (TNF), interleukin 8 (IL-8), IL-6, and IL-1 were the most common biomarkers of TMDs. As of recently, although the research on TMDs biomarkers still aims to find more diagnostic agents, no recent study employs the biomarker as a targeting point of pharmacotherapy to suppress the inflammatory responses. This article represents an explicit review on the biomarkers of TMDs that have been discovered so far and provides possible future directions towards further research on these biomarkers. The potential implementation of the interactions of TNF with its receptor 2 (TNFR2) in the inflammatory process has been interpreted, and thus, this review presents a new hypothesis towards suppression of the inflammatory response using TNFR2-agonist. Subsequently, this hypothesis could be explored as a potential pain elimination approach in patients with TMDs.
  12. Fulltext Biomarkers for Premature Coronary Artery Disease (PCAD): A Case Control Study
    Shukor MFA, Musthafa QA, Mohd Yusof YA, Wan Ngah WZ, Ismail NAS
    Diagnostics (Basel), 2023 Jan 04;13(2).
    PMID: 36672997 DOI: 10.3390/diagnostics13020188
    Coronary artery disease (CAD) is often associated with the older generation. However, in recent years, there is an increasing trend in the prevalence of CAD among the younger population; this is known as premature CAD. Although biomarkers for CAD have been established, there are limited studies focusing on premature CAD especially among the Malay male population. Thus, the aim of this research was to compare the biomarkers between premature CAD (PCAD) and older CAD (OCAD) among Malay males. Subjects, recruited from the Universiti Kebangsaan Malaysia Medical Centre and National Heart Institution, were divided into four groups: healthy control < 45 years old; premature CAD (PCAD) < 45 years old; healthy control > 60 years old; and older CAD (OCAD) > 60 years old, with n = 30 for each group. Ten potential markers for CAD including soluble sVCAM-1, sICAM-1, interleukin-2, interleukin-6, interleukin-10, Apo-E and Apo-A1, homocysteine, CRP, and vitamin D levels were examined. Our results revealed premature CAD patients had significantly higher values (p < 0.05) of sVCAM-1, CRP, interleukin-6, and vitamin D when compared to the age-matched controls. Similarly, older CAD patients showed higher levels of sVCAM-1, CRP, and interleukin-2 when compared to their age-matched controls. After adjusting for multiple parameters, only CRP remained significant for PCAD and interleukin-2 remained significant for CAD. This indicates that premature CAD and older CAD patients showed different profiles of protein biomarkers. CRP has the potential to become a biomarker for premature CAD while interleukin-2 is a better biomarker for older CAD together with other typical panels of protein biomarkers.
  13. Fulltext Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
    Islam KR, Kumar J, Tan TL, Reaz MBI, Rahman T, Khandakar A, et al.
    Diagnostics (Basel), 2022 Sep 03;12(9).
    PMID: 36140545 DOI: 10.3390/diagnostics12092144
    With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.
  14. Fulltext Genetic Factors for Coronary Heart Disease and Their Mechanisms: A Meta-Analysis and Comprehensive Review of Common Variants from Genome-Wide Association Studies
    Zarkasi KA, Abdullah N, Abdul Murad NA, Ahmad N, Jamal R
    Diagnostics (Basel), 2022 Oct 21;12(10).
    PMID: 36292250 DOI: 10.3390/diagnostics12102561
    Genome-wide association studies (GWAS) have discovered 163 loci related to coronary heart disease (CHD). Most GWAS have emphasized pathways related to single-nucleotide polymorphisms (SNPs) that reached genome-wide significance in their reports, while identification of CHD pathways based on the combination of all published GWAS involving various ethnicities has yet to be performed. We conducted a systematic search for articles with comprehensive GWAS data in the GWAS Catalog and PubMed, followed by a meta-analysis of the top recurring SNPs from ≥2 different articles using random or fixed-effect models according to Cochran Q and I2 statistics, and pathway enrichment analysis. Meta-analyses showed significance for 265 of 309 recurring SNPs. Enrichment analysis returned 107 significant pathways, including lipoprotein and lipid metabolisms (rs7412, rs6511720, rs11591147, rs1412444, rs11172113, rs11057830, rs4299376), atherogenesis (rs7500448, rs6504218, rs3918226, rs7623687), shared cardiovascular pathways (rs72689147, rs1800449, rs7568458), diabetes-related pathways (rs200787930, rs12146487, rs6129767), hepatitis C virus infection/hepatocellular carcinoma (rs73045269/rs8108632, rs56062135, rs188378669, rs4845625, rs11838776), and miR-29b-3p pathways (rs116843064, rs11617955, rs146092501, rs11838776, rs73045269/rs8108632). In this meta-analysis, the identification of various genetic factors and their associated pathways associated with CHD denotes the complexity of the disease. This provides an opportunity for the future development of novel CHD genetic risk scores relevant to personalized and precision medicine.
  15. Fulltext Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier
    Khan A, Khan A, Khan MM, Farid K, Alam MM, Su'ud MBM
    Diagnostics (Basel), 2022 Oct 26;12(11).
    PMID: 36359438 DOI: 10.3390/diagnostics12112595
    Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called "Stacking Classifier" in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
  16. Fulltext Lateral Flow Immunoassays for SARS-CoV-2
    Ang GY, Chan KG, Yean CY, Yu CY
    Diagnostics (Basel), 2022 Nov 18;12(11).
    PMID: 36428918 DOI: 10.3390/diagnostics12112854
    The continued circulation of SARS-CoV-2 virus in different parts of the world opens up the possibility for more virulent variants to evolve even as the coronavirus disease 2019 transitions from pandemic to endemic. Highly transmissible and virulent variants may seed new disruptive epidemic waves that can easily put the healthcare system under tremendous pressure. Despite various nucleic acid-based diagnostic tests that are now commercially available, the wide applications of these tests are largely hampered by specialized equipment requirements that may not be readily available, accessible and affordable in less developed countries or in low resource settings. Hence, the availability of lateral flow immunoassays (LFIs), which can serve as a diagnostic tool by detecting SARS-CoV-2 antigen or as a serological tool by measuring host immune response, is highly appealing. LFI is rapid, low cost, equipment-free, scalable for mass production and ideal for point-of-care settings. In this review, we first summarize the principle and assay format of these LFIs with emphasis on those that were granted emergency use authorization by the US Food and Drug Administration followed by discussion on the specimen type, marker selection and assay performance. We conclude with an overview of challenges and future perspective of LFI applications.
  17. Fulltext Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis
    Hanis TM, Islam MA, Musa KI
    Diagnostics (Basel), 2022 Jul 05;12(7).
    PMID: 35885548 DOI: 10.3390/diagnostics12071643
    In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85-0.90), 0.83 (95% CI: 0.78-0.87) and 0.84 (95% CI: 0.81-0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks' linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.
  18. Fulltext Pap Smear Images Classification Using Machine Learning: A Literature Matrix
    Alias NA, Mustafa WA, Jamlos MA, Alquran H, Hanafi HF, Ismail S, et al.
    Diagnostics (Basel), 2022 Nov 22;12(12).
    PMID: 36552907 DOI: 10.3390/diagnostics12122900
    Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.
  19. Fulltext Detection of COVID-19 Virus on Surfaces Using Photonics: Challenges and Perspectives
    Taha BA, Al Mashhadany Y, Bachok NN, Ashrif A Bakar A, Hafiz Mokhtar MH, Dzulkefly Bin Zan MS, et al.
    Diagnostics (Basel), 2021 Jun 19;11(6).
    PMID: 34205401 DOI: 10.3390/diagnostics11061119
    The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers' effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment.
  20. Fulltext Reagent Effects on the Activated Partial Thromboplastin Time Clot Waveform Analysis: A Multi-Centre Study
    Wong WH, Tan CW, Abdul Khalid NB, Dalimoenthe NZ, Yip C, Tantanate C, et al.
    Diagnostics (Basel), 2023 Jul 22;13(14).
    PMID: 37510191 DOI: 10.3390/diagnostics13142447
    (1) Background: The activated partial thromboplastin time (APTT)- based clot waveform analysis (CWA) quantitatively extends information obtained from the APTT waveform through its derivatives. However, pre-analytical variables including reagent effects on the CWA parameters are poorly understood and must be standardized as a potential diagnostic assay. (2) Methods: CWA was first analysed with patient samples to understand reagent lot variation in three common APTT reagents: Pathromtin SL, Actin FS, and Actin FSL. A total of 1055 healthy volunteers were also recruited from seven institutions across the Asia-Pacific region and CWA data were collected with the Sysmex CS analysers. (3) Results: CWA parameters varied less than 10% between lots and the linear mixed model analysis showed few site-specific effects within the same reagent group. However, the CWA parameters were significantly different amongst all reagent groups and thus reagent-specific 95% reference intervals could be calculated using the nonparametric method. Post-hoc analysis showed some degree of influence by age and gender with weak correlation to the CWA (r < 0.3). (4) Conclusions: Reagent type significantly affects APTT-based CWA with minimal inter-laboratory variations with the same coagulometer series that allow for data pooling across laboratories with more evidence required for age- and gender-partitioning.
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