Displaying publications 21 - 40 of 108 in total

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  1. Fulltext Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks
    Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, et al.
    Diagnostics (Basel), 2021 Oct 09;11(10).
    PMID: 34679557 DOI: 10.3390/diagnostics11101859
    The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.
  2. Fulltext Bioelectrical Impedance Analysis Derived-Phase Angle as a Pragmatic Tool to Detect Protein Energy Wasting among Multi-Ethnic Hemodialysis Patients
    Lim CK, Lim JH, Ibrahim I, Chan YM, Zakaria NF, Yahya R, et al.
    Diagnostics (Basel), 2021 Sep 23;11(10).
    PMID: 34679443 DOI: 10.3390/diagnostics11101745
    Protein-energy wasting (PEW) is a devastating metabolic derangement that leads to increased morbidity and mortality in hemodialysis (HD) patients. This study aimed to determine the diagnostic test accuracy of bioelectrical impedance analysis derived-phase angle (PhA) in detecting PEW among HD patients. This was a multi-centre, cross-sectional study conducted amongst 152 multi-ethnic HD patients in Klang Valley, Malaysia. PEW was assessed using the International Society of Renal Nutrition and Metabolism criteria as the reference method. PhA was measured using a multi-frequency bioelectrical impedance spectroscopy at 50 kHz. Multiple and logistic regressions were used to determine factors associated with PhA and PEW diagnosis, respectively. A receiver operating characteristics curve analysis was used to establish the gender-specific PhA cut-offs to detect PEW. PEW existed in 21.1% of the HD patients. PhA was found as an independent predictor of PEW (adjOR = 0.308, p = 0.001), with acceptable to excellent discriminative performance (adjAUCmale = 0.809; adjAUCfemale = 0.719). Male patients had higher PhA cut-off compared to female patients (4.26° vs. 3.30°). We concluded that PhA is a valid and pragmatic biomarker to detect PEW in multi-ethnic Malaysian HD patients and a gender-specific cut-off is necessary, attributed to the gender differences in body composition.
  3. Fulltext Application of Proteomics to the Study of the Therapeutics and Pathogenicity of Giardia duodenalis
    Aziz AFE, Roshidi N, Othman N, Mohd Hanafiah K, Arifin N
    Diagnostics (Basel), 2022 Nov 09;12(11).
    PMID: 36359588 DOI: 10.3390/diagnostics12112744
    Giardia duodenalis remains a neglected tropical disease. A key feature of the sustained transmission of Giardia is the ability to form environmentally resistant cysts. For the last 38 years, proteomics has been utilised to study various aspects of the parasite across different life cycle stages. Thirty-one articles have been published in PubMed from 2012 to 2022 related to the proteomics of G. duodenalis. Currently, mass spectrometry with LC-MS/MS and MALDI-TOF/TOF has been commonly utilised in proteomic analyses of Giardia, which enables researchers to determine potential candidates for diagnostic biomarkers as well as vaccine and drug targets, in addition to allowing them to investigate the virulence of giardiasis, the pathogenicity mechanisms of G. duodenalis, and the post-translational modifications of Giardia proteins throughout encystation. Over the last decade, valuable information from proteomics analyses of G. duodenalis has been discovered in terms of the pathogenesis and virulence of Giardia, which may provide guidance for the development of better means with which to prevent and reduce the impacts of giardiasis. Nonetheless, there is room for improving proteomics analyses of G. duodenalis, since genomic sequences for additional assemblages of Giardia have uncovered previously unknown proteins associated with the Giardia proteome. Therefore, this paper aims to review the applications of proteomics for the characterisation of G. duodenalis pathogenicity and the discovery of novel vaccine as well as drug targets, in addition to proposing some general directions for future Giardia proteomic research.
  4. Fulltext A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits
    Ambayya A, Sahibon S, Yang TW, Zhang QY, Hassan R, Sathar J
    Diagnostics (Basel), 2021 Nov 22;11(11).
    PMID: 34829510 DOI: 10.3390/diagnostics11112163
    Thalassemia is one of the major inherited haematological disorders in the Southeast Asia region. This study explored the potential utility of red blood cell (RBC) parameters and reticulocyte cell population data (CPD) parameters in the differential diagnosis of α and β-thalassaemia traits as a rapid and cost-effective tool for screening of thalassemia traits. In this study, a total of 1597 subjects (1394 apparently healthy subjects, 155 subjects with α-thalassaemia trait, and 48 subjects with β-thalassaemia trait) were accrued. The parameters studied were the RBC parameters and reticulocyte CPD parameters derived from Unicel DxH800. A novel algorithm named αβ-algorithm was developed: (MN-LMALS-RET × RDW) - MCH) to discriminate α from β-thalassaemia trait with a cut-off value of 1742.5 [AUC = 0.966, sensitivity = 92%, specificity = 90%, 95% CI = 0.94-0.99]. Two prospective studies were carried: an in-house cohort to assess the specificity of this algorithm in 310 samples comprising various RBC disorders and in an interlaboratory cohort of 65 α-thalassemia trait, and 30 β-thalassaemia trait subjects to assess the reproducibility of the findings. We propose the αβ-algorithm to serve as a rapid, inexpensive surrogate evaluation tool of α and β-thalassaemia in the population screening of thalassemia traits in geographic regions with a high burden of these inherited blood disorders.
  5. Fulltext Integration of the Cortical Haemodynamic Response Measured by Functional Near-Infrared Spectroscopy and Amino Acid Analysis to Aid in the Diagnosis of Major Depressive Disorder
    Ong SK, Husain SF, Wee HN, Ching J, Kovalik JP, Cheng MS, et al.
    Diagnostics (Basel), 2021 Oct 25;11(11).
    PMID: 34829325 DOI: 10.3390/diagnostics11111978
    BACKGROUND: Major depressive disorder (MDD) is a debilitating condition with a high disease burden and medical comorbidities. There are currently few to no validated biomarkers to guide the diagnosis and treatment of MDD. In the present study, we evaluated the differences between MDD patients and healthy controls (HCs) in terms of cortical haemodynamic responses during a verbal fluency test (VFT) using functional near-infrared spectroscopy (fNIRS) and serum amino acid profiles, and ascertained if these parameters were correlated with clinical characteristics.

    METHODS: Twenty-five (25) patients with MDD and 25 age-, gender-, and ethnicity-matched HCs were recruited for the study. Real-time monitoring of the haemodynamic response during completion of a VFT was quantified using a 52-channel NIRS system. Serum samples were analysed and quantified by liquid chromatography-mass spectrometry for amino acid profiling. Receiver-operating characteristic (ROC) curves were used to classify potential candidate biomarkers.

    RESULTS: The MDD patients had lower prefrontal and temporal activation during completion of the VFT than HCs. The MDD patients had lower mean concentrations of oxy-Hb in the left orbitofrontal cortex (OFC), and lower serum histidine levels. When the oxy-haemoglobin response was combined with the histidine concentration, the sensitivity and specificity of results improved significantly from 66.7% to 73.3% and from 65.0% to 90.0% respectively, as compared to results based only on the NIRS response.

    CONCLUSIONS: These findings demonstrate the use of combination biomarkers to aid in the diagnosis of MDD. This technique could be a useful approach to detect MDD with greater precision, but additional studies are required to validate the methodology.

  6. Fulltext The Potential Use of Volatile Biomarkers for Malaria Diagnosis
    Chai HC, Chua KH
    Diagnostics (Basel), 2021 Nov 30;11(12).
    PMID: 34943481 DOI: 10.3390/diagnostics11122244
    Pathogens may change the odor and odor-related biting behavior of the vector and host to enhance pathogen transmission. In recent years, volatile biomarker investigations have emerged to identify odors that are differentially and specifically released by pathogens and plants, or the pathogen-infected or even cancer patients. Several studies have reported odors or volatile biomarkers specifically detected from the breath and skin of malaria-infected individuals. This review will discuss the potential use of these odors or volatile biomarkers for the diagnosis of malaria. This approach not only allows for the non-invasive mean of sample collection but also opens up the opportunity to develop a biosensor for malaria diagnosis in low-resource settings.
  7. Fulltext The Effects of Suprabullar Pneumatization on the Orientation of Its Surrounding Anatomical Structures Relevant to the Frontal Drainage Pathway
    Umar NF, Aziz ME, Mat Lazim N, Abdullah B
    Diagnostics (Basel), 2021 Dec 27;12(1).
    PMID: 35054219 DOI: 10.3390/diagnostics12010052
    OBJECTIVE: The aim of this study was to evaluate the effects of suprabullar pneumatization on the orientation of the frontal sinus outflow structures and its association with the volume of anterior ethmoid sinus.

    METHODS: A retrospective chart review of computed tomography of paranasal sinuses (CTPNS) images was conducted. A total of 370 sides of the CTPNS of 185 patients were analyzed.

    RESULTS: The course of anterior ethmoidal artery (AEA) along the skull base (p = 0.04) and position of AEA at the second lamella (p = 0.04) was significantly associated with the type of suprabullar pneumatization. The AEA is expected to be lower at the skull base and at a longer distance from the second lamella with the increase in grading of the suprabullar pneumatization. The distance of AEA to the second lamella (p < 0.001) and third lamella (p = 0.04) was significantly different depending on the type of suprabullar pneumatization, which indicates AEA is expected to be at a longer distance from the second lamella and third lamella in higher grade suprabullar pneumatization. The type of suprabullar pneumatization has a significant but weak association with the anterior ethmoid sinus volume (p = 0.04).

    CONCLUSIONS: There is a significant effect of the type of suprabullar pneumatization on the orientation of the surrounding anatomical structures at the frontal recess. The type of suprabullar pneumatization is influenced by the anterior ethmoid sinus volume, which suggests it has a possible role in the frontal drainage pathway.

  8. Fulltext An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease
    Khafaga DS, Ibrahim A, El-Kenawy EM, Abdelhamid AA, Karim FK, Mirjalili S, et al.
    Diagnostics (Basel), 2022 Nov 21;12(11).
    PMID: 36428952 DOI: 10.3390/diagnostics12112892
    Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework's efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.
  9. Fulltext Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver
    Halim AAA, Andrew AM, Mustafa WA, Mohd Yasin MN, Jusoh M, Veeraperumal V, et al.
    Diagnostics (Basel), 2022 Nov 19;12(11).
    PMID: 36428930 DOI: 10.3390/diagnostics12112870
    Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
  10. Fulltext Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
    Alsalatie M, Alquran H, Mustafa WA, Mohd Yacob Y, Ali Alayed A
    Diagnostics (Basel), 2022 Nov 10;12(11).
    PMID: 36428816 DOI: 10.3390/diagnostics12112756
    The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors' increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.
  11. Fulltext Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records
    Hanis TM, Ruhaiyem NIR, Arifin WN, Haron J, Wan Abdul Rahman WF, Abdullah R, et al.
    Diagnostics (Basel), 2022 Nov 16;12(11).
    PMID: 36428886 DOI: 10.3390/diagnostics12112826
    This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.
  12. Fulltext Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
    Arifin WN, Yusof UK
    Diagnostics (Basel), 2022 Nov 17;12(11).
    PMID: 36428900 DOI: 10.3390/diagnostics12112839
    In medical care, it is important to evaluate any new diagnostic test in the form of diagnostic accuracy studies. These new tests are compared to gold standard tests, where the performance of binary diagnostic tests is usually measured by sensitivity (Sn) and specificity (Sp). However, these accuracy measures are often biased owing to selective verification of the patients, known as partial verification bias (PVB). Inverse probability bootstrap (IPB) sampling is a general method to correct sampling bias in model-based analysis and produces debiased data for analysis. However, its utility in PVB correction has not been investigated before. The objective of this study was to investigate IPB in the context of PVB correction under the missing-at-random assumption for binary diagnostic tests. IPB was adapted for PVB correction, and tested and compared with existing methods using simulated and clinical data sets. The results indicated that IPB is accurate for Sn and Sp estimation as it showed low bias. However, IPB was less precise than existing methods as indicated by the higher standard error (SE). Despite this issue, it is recommended to use IPB when subsequent analysis with full data analytic methods is expected. Further studies must be conducted to reduce the SE.
  13. Fulltext An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal
    Ullah H, Heyat MBB, Akhtar F, Muaad AY, Ukwuoma CC, Bilal M, et al.
    Diagnostics (Basel), 2022 Dec 28;13(1).
    PMID: 36611379 DOI: 10.3390/diagnostics13010087
    The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.
  14. Fulltext Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review
    Tan XJ, Cheor WL, Lim LL, Ab Rahman KS, Bakrin IH
    Diagnostics (Basel), 2022 Dec 09;12(12).
    PMID: 36553119 DOI: 10.3390/diagnostics12123111
    Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
  15. Fulltext Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning
    Qasmieh IA, Alquran H, Zyout A, Al-Issa Y, Mustafa WA, Alsalatie M
    Diagnostics (Basel), 2022 Dec 17;12(12).
    PMID: 36553211 DOI: 10.3390/diagnostics12123204
    A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.
  16. Fulltext Allred Scoring of ER-IHC Stained Whole-Slide Images for Hormone Receptor Status in Breast Carcinoma
    Ahmad Fauzi MF, Wan Ahmad WSHM, Jamaluddin MF, Lee JTH, Khor SY, Looi LM, et al.
    Diagnostics (Basel), 2022 Dec 08;12(12).
    PMID: 36553102 DOI: 10.3390/diagnostics12123093
    Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and intensity of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This work integrates cell detection and classification workflow for breast carcinoma estrogen receptor (ER)-IHC-stained images and presents an automated evaluation system. The system first detects all cells within the specific regions and classifies them into negatively, weakly, moderately, and strongly stained, followed by Allred scoring for ER status evaluation. The generated Allred score relies heavily on accurate cell detection and classification and is compared against pathologists' manual estimation. Experiments on 40 whole-slide images show 82.5% agreement on hormonal treatment recommendation, which we believe could be further improved with an advanced learning model and enhancement to address the cases with 0% ER status. This promising system can automate the exhaustive exercise to provide fast and reliable assistance to pathologists and medical personnel. The system has the potential to improve the overall standards of prognostic reporting for cancer patients, benefiting pathologists, patients, and also the public at large.
  17. Fulltext A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models
    Sarra RR, Dinar AM, Mohammed MA, Ghani MKA, Albahar MA
    Diagnostics (Basel), 2022 Nov 22;12(12).
    PMID: 36552906 DOI: 10.3390/diagnostics12122899
    Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.
  18. Fulltext Urine and Saliva: Relevant Specimens for Malaria Diagnosis?
    Chai HC, Chua KH
    Diagnostics (Basel), 2022 Nov 29;12(12).
    PMID: 36552996 DOI: 10.3390/diagnostics12122989
    Blood remains the specimen of preference for malaria diagnosis, whether it is for microscopic, nucleic acid-based or biomarker detection of Plasmodium present in a patient. However, concerning the disadvantages of blood drawing, specimens that can be non-invasively collected under non-hygienic settings would come in handy for malaria diagnosis in endemic areas with limited resources. Although the current approaches using saliva or urine might not be as sensitive and specific as using blood, the potential of these two specimens should not be underestimated and efforts in developing diagnostic methods for Plasmodium detection specifically in these two specimens should continue without giving up. This review not only compiles and summarizes the sensitivity and specificity achieved by various detection approaches when using these samples for malaria diagnosis, it also intends to enhance the possibility of using saliva and urine for diagnostic purposes by describing how Plasmodium nucleic acid and antigens may likely be present in these samples. This review may hopefully encourage and motivate researchers in developing saliva- and urine-based diagnostic methods for Plasmodium detection to facilitate the control and eradication of malaria. In summary, the presence of Plasmodium DNA and antigens in urine and saliva makes these two specimens relevant and useful for malaria diagnosis.
  19. Fulltext A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
    Haque F, Reaz MBI, Chowdhury MEH, Shapiai MIB, Malik RA, Alhatou M, et al.
    Diagnostics (Basel), 2023 Jan 11;13(2).
    PMID: 36673074 DOI: 10.3390/diagnostics13020264
    Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram's area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model's performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN.
  20. Fulltext Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network
    Elhassan TA, Mohd Rahim MS, Siti Zaiton MH, Swee TT, Alhaj TA, Ali A, et al.
    Diagnostics (Basel), 2023 Jan 05;13(2).
    PMID: 36673006 DOI: 10.3390/diagnostics13020196
    Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the "GT-DCAE WBC augmentation model". In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the "two-stage DCAE-CNN atypical WBC classification model" (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model's discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%.
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