فهرست مطالب
Journal of Biomedical Physics & Engineering
Volume:12 Issue: 6, Nov-Dec 2022
- تاریخ انتشار: 1401/09/15
- تعداد عناوین: 12
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Pages 549-550
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Pages 551-558
The health organisation has suffered from the lack of diagnosis support systems and physicians in India. Further, the physicians are struggling to treat many patients, and the hospitals also have the lack of a radiologist especially in rural areas; thus, almost all cases are handled by a single physician, leading to many misdiagnoses. Computer aided diagnostic systems are being developed to address this problem. The current study aimed to review the different methods to detect pneumonia using neural networks and compare their approach and results. For the best comparisons, only papers with the same data set ChestXray14 are studied.
Keywords: Pneumonia, Convolution Neural Networks, Mass Chest X-Ray, Chest X-ray14, Diagnosis, Computer-Assisted, Deep Learning -
Pages 559-568BackgroundThe conventional procedure of skin-related disease detection is a visual inspection by a dermatologist or a primary care clinician, using a dermatoscope. The suspected patients with early signs of skin cancer are referred for biopsy and histopathological examination to ensure the correct diagnosis and the best treatment. Recent advancements in deep convolutional neural networks (CNNs) have achieved excellent performance in automated skin cancer classification with accuracy similar to that of dermatologists. However, such improvements are yet to bring about a clinically trusted and popular system for skin cancer detection.ObjectiveThis study aimed to propose viable deep learning (DL) based method for the detection of skin cancer in lesion images, to help physicians in diagnosis.Material and MethodsIn this analytical study, a novel DL based model was proposed, in which other than the lesion image, the patient’s data, including the anatomical site of the lesion, age, and gender were used as the model input to predict the type of the lesion. An Inception-ResNet-v2 CNN pretrained for object recognition was employed in the proposed model.ResultsBased on the results, the proposed method achieved promising performance for various skin conditions, and also using the patient’s metadata in addition to the lesion image for classification improved the classification accuracy by at least 5% in all cases investigated. On a dataset of 57536 dermoscopic images, the proposed approach achieved an accuracy of 89.3%±1.1% in the discrimination of 4 major skin conditions and 94.5%±0.9% in the classification of benign vs. malignant lesions.ConclusionThe promising results highlight the efficacy of the proposed approach and indicate that the inclusion of the patient’s metadata with the lesion image can enhance the skin cancer detection performance.Keywords: CNN, Deep Learning, Dermoscopy, Lesion, Metadata, Skin Cancer
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Pages 569-582BackgroundAlzheimer’s disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer‘s patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses.ObjectiveThis study aimed to develop an efficient automated method to differentiate Alzheimer’s patients from normal elderly and present the essential features with accurate Alzheimer’s diagnosis.Material and MethodsIn this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm.ResultsThe LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.ConclusionThe proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time.Keywords: Hippocampus, Amygdala, Cortical thickness, Gyrification index, Sulcal depth, Alzheimer Disease, Relief algorithm
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Pages 583-590BackgroundPostoperative infection in Coronary Artery Bypass Graft (CABG) is one of the most common complications for diabetic patients, due to an increase in the hospitalization and cost. To address these issues, it is necessary to apply some solutions.ObjectiveThe study aimed to the development of a Clinical Decision Support System (CDSS) for predicting the CABG postoperative infection in diabetic patients.Material and MethodsThis developmental study is conducted on a private hospital in Tehran in 2016. From 1061 CABG surgery medical records, we selected 210 cases randomly. After data gathering, we used statistical tests for selecting related features. Then an Artificial Neural Network (ANN), which was a one-layer perceptron network model and a supervised training algorithm with gradient descent, was constructed using MATLAB software. The software was then developed and tested using the receiver operating characteristic (ROC) diagram and the confusion matrix.ResultsBased on the correlation analysis, from 28 variables in the data, 20 variables had a significant relationship with infection after CABG (P<0.05). The results of the confusion matrix showed that the sensitivity of the system was 69%, and the specificity and the accuracy were 97% and 84%, respectively. The Receiver Operating Characteristic (ROC) diagram shows the appropriate performance of the CDSS.ConclusionThe use of CDSS can play an important role in predicting infection after CABG in patients with diabetes. The designed software can be used as a supporting tool for physicians to predict infections caused by CABG in diabetic patients as a susceptible group. However, other factors affecting infection must also be considered for accurate prediction.Keywords: Decision Support Systems, Clinical, Surgical Wound Infection, Coronary artery bypass, Diabetes
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Pages 591-598Background
Model for end-stage liver disease (MELD) is currently used for liver transplantation (LT) allocation, however, it is not a sufficient criterion.
ObjectiveThis current study aims to perform a hybrid neural network analysis of different data, make a decision tree and finally design a decision support system for improving LT prioritization.
Material and MethodsIn this cohort follow-up-based study, baseline characteristics of 1947 adult patients, who were candidates for LT in Shiraz Organ Transplant Center, Iran, were assessed and followed for two years and those who died before LT due to the end-stage liver disease were considered as dead cases, while others considered as alive cases. A well-organized checklist was filled for each patient. Analysis of the data was performed using artificial neural networks (ANN) and support vector machines (SVM). Finally, a decision tree was illustrated and a user friendly decision support system was designed to assist physicians in LT prioritization.
ResultsBetween all MELD types, MELD-Na was a stronger determinant of LT candidates’ survival. Both ANN and SVM showed that besides MELD-Na, age and ALP (alkaline phosphatase) are the most important factors, resulting in death in LT candidates. It was cleared that MELD-Na <23, age <53 and ALP <257 IU/L were the best predictors of survival in LT candidates. An applicable decision support system was designed in this study using the above three factors.
ConclusionTherefore, Meld-Na, age and ALP should be used for LT allocation. The presented decision support system in this study will be helpful in LT prioritization by LT allocators.
Keywords: Prioritization, Allocation, Artificial Neural Network, Decision Trees, MELD-Na, Liver Transplantation, Neural Network Computers -
Pages 599-610BackgroundCharacterization of parotid tumors before surgery using multi-parametric magnetic resonance imaging (MRI) scans can support clinical decision making about the best-suited therapeutic strategy for each patient.ObjectiveThis study aims to differentiate benign from malignant parotid tumors through radiomics analysis of multi-parametric MR images, incorporating T2-w images with ADC-map and parametric maps generated from Dynamic Contrast Enhanced MRI (DCE-MRI).Material and MethodsMRI scans of 31 patients with histopathologically-confirmed parotid gland tumors (23 benign, 8 malignant) were included in this retrospective study. For DCE-MRI, semi-quantitative analysis, Tofts pharmacokinetic (PK) modeling, and five-parameter sigmoid modeling were performed and parametric maps were generated. For each patient, borders of the tumors were delineated on whole tumor slices of T2-w image, ADC-map, and the late-enhancement dynamic series of DCE-MRI, creating regions-of-interest (ROIs). Radiomic analysis was performed for the specified ROIs.ResultsAmong the DCE-MRI-derived parametric maps, wash-in rate (WIR) and PK-derived Ktrans parameters surpassed the accuracy of other parameters based on support vector machine (SVM) classifier. Radiomics analysis of ADC-map outperformed the T2-w and DCE-MRI techniques using the simpler classifier, suggestive of its inherently high sensitivity and specificity. Radiomics analysis of the combination of T2-w image, ADC-map, and DCE-MRI parametric maps resulted in accuracy of 100% with both classifiers with fewer numbers of selected texture features than individual images.ConclusionIn conclusion, radiomics analysis is a reliable quantitative approach for discrimination of parotid tumors and can be employed as a computer-aided approach for pre-operative diagnosis and treatment planning of the patients.Keywords: Parotid Neoplasms, Radiomics, Texture Analysis, Magnetic Resonance Imaging, Machine Learning, diagnosis
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Pages 611-626Background
Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA).
ObjectiveThis paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality.
Material and MethodsIn this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models.
ResultsA total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively.
ConclusionThe hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.
Keywords: Machine Learning, Artificial Intelligence, Coronavirus (COVID-19), Data mining, Mortality -
Pages 627-636BackgroundObstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient’s body is fitted with many sensors.ObjectiveThis study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms.Material and MethodsIn this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM).ResultsThe OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied.ConclusionANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.Keywords: Electrocardiography, Sleep apnea, Obstructive, Machine Learning
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Pages 637-644Background
Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival.
ObjectiveTo develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.
Material and MethodsIn this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection.
ResultsThe examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%).
ConclusionMachine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically.
Keywords: Artificial Intelligence, Breast cancer, Digital Screens, Screen Time, Visible Light, Blue Light, Prognosis Prediction, Smartphone, Circadian Clocks -
Pages 645-654BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults and its early detection is effective in the successful treatment of children. Electroencephalography (EEG) has been widely used for classifying ADHD and normal children. In recent years, deep learning leads to more accurate classification.ObjectiveThis study aims to adapt convolutional neural networks (CNNs) for classifying ADHD and normal children based on the connectivity measure of their EEG signals.Material and MethodsIn this experimental study, the dataset consisted of 61 ADHD and 60 normal children from which 13021 epochs were extracted as input for model training and evaluation. Synchronization likelihood (SL) and wavelet coherence (WC) were considered connectivity measures. The neighborhood between EEG channels was arranged in a two-dimensional matrix for better representation. Four-dimensional (4D) and six-dimensional (6D) connectivity tensors were composed as model inputs. Two architectures were developed, one 4D and 6D CNN for SL and WC-based diagnosis of ADHD, respectively.ResultsA 5-fold cross-validation was utilized to assess developed models. The average accuracy of 98.56% for 4D CNN and 98.85% for 6D CNN in epoch-based classification were obtained. In the case of subject-based classification, the accuracy was 99.17% for both models.ConclusionBased on the evaluation metrics of the proposed models, ADHD children can be diagnosed and ADHD and normal children can be successfully distinguished.Keywords: Attention-Deficit, Hyperactivity Disorder (ADHD), Functional Connectivity, Electroencephalography, Neural networks, Deep Learning, Artificial Intelligence
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Pages 655-668Background
Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC.
ObjectiveThis study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult.
Material and MethodsThis retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software.
ResultsThe results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC.
ConclusionMulti-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.
Keywords: Pancreatic Ductal Adenocarcinoma (PDAC), Multi-Scale Texture Analysis, Treatment Response Assessment, Tumour Detection, Visualization