PREPARATION AND NUMERICAL SIMULATIONS OF DRUG-LOADED HYDROXYAPATITE BIOMATERIALS

To meet the challenge of regenerating bone lost to disease or trauma, biodegradable scaffolds are being investigated as a way to regenerate bone without the need for an auto- or allograft. Herein, we prepared poly (lactic acid) (PLA)/chitosan(CS)/nano-hydroxyapatite biomaterials through solution polymerization and solvent volatilization. Cefadroxil was used as a model drug for loading on biomaterials using supercritical carbon dioxide. In addition, we investigated the in vitro drug release effect, and the in vitro release results showed that the drug could release more than 73% of the drug load within 48[Formula: see text]h. This excellent drug release property could allow continuous drug use at the wound site, further broadening its application in the medical field. A three-dimensional finite element (FE) model of bone screws was established, and the mechanical properties of the screws were numerically calculated. The stress and deformation of the bone screws under different external conditions were simulated. The bending simulation showed that the screw can withstand the maximum deformation of 0.418[Formula: see text]mm and equivalent stress of 566.94[Formula: see text]MPa at a force of 700 N. The maximum equivalent stress of the screw reached 321.84[Formula: see text]MPa, and the corresponding torque was 779.68 N[Formula: see text]mm when the torsion angle was gradually increased to 30[Formula: see text]. The fabricated material has excellent mechanical properties and can be used for bone repair. This study provides a new direction for preparing drug-loaded polymer biomaterials and developing new materials for bone repair.

DATA SECURITY — THE NEXT IMPERATIVE IN HEALTHCARE SYSTEMS

Wireless Sensor Networks (WSNs) have significantly impacted healthcare applications by giving the possibility of monitoring the patient’s physiological parameters using different sensors. The use of WSN and the wireless Body Area Networks (WBANs) offers possible solutions for monitoring the health parameters in remote areas. On the other hand, the use of wireless communication medium and information security is the primary concern in WBANs. Because WBANs use the different small sensors placed on the human body to collect the physiological data. They need resource and computational restrictions, thus, building the use of complex and advanced encryption algorithms infeasible. It is essential in the WBAN to monitor and transmit the data to provide reliable and secure communication. Wrong and incomplete information can create difficulties in patient health which can be sometimes more dangerous. This gives the motivation to make such security protocols or algorithms to achieve high security in WBANs. So, the research has been currently focused on reliable communication between the doctor and patient, routing algorithms, and the data’s security by using various new technologies. This paper discusses the different security threats and solutions for designing healthcare applications and routing and layer attacks. Furthermore, the paper has been focused on the Data Distribution Service Models for data security. The paper also includes artificial intelligence and machine learning algorithms in healthcare implemented by various companies.

A NOVEL IMAGE PROCESSING ALGORITHM TO AUTOMATICALLY EXTRACT AND CLASSIFY LIVER BEARING TUMORS USING PATTERN RECOGNITION AND FUZZY TRANSFORM

This study presents a novel liver disease classification method by applying pattern recognition technique to automatically segmented liver from the images of computed tomographic (CT) scans. The methodology comprises of disease classification by the extraction of textural features from focal liver region bearing tumors. Two types of liver textures are investigated in this study for classification accuracy judgement. First, original liver texture is considered for feature extraction. Second, liver is used for feature extraction. The CT image dataset comprises 308 liver samples with 193 samples of malignant tumor and 115 samples of benign tumor. The entire liver tissue bearing tumor is segmented from the CT image automatically in the pre-processing stage using fuzzy transformation function and morphological processing. Four sets of textural feature matrices are applied to the liver for feature extraction. Gray level co-occurrence matrix (GLCM), standard deviation gray level co-occurrence matrix (SDGLCM), seven-moment matrix (7MM) and seven-moment gray level co-occurrence matrix (7MGLCM) are the combinational feature matrices applied to classify the liver as malignant or benign using support vector machines (SVMs). The best classification accuracy is achieved for original liver texture by 7MGLCM, which is 97% with AUC[Formula: see text]0.99 for training dataset and 97.8% with AUC[Formula: see text]1 for test dataset.

CONFIDENCE LEVEL OF A MOTION FEEDBACK SYSTEM FOR ANALYZING EXERCISE TRAINING EFFICACY

The increase in aged population is a global trend. Inculcating healthy behaviors such as regular exercises in the elderly has a significant impact on the financial and medical burden globally. Moreover, air pollution and the outbreak of the coronavirus disease 19 (COVID-19) pose a serious threat to public health. In order to improve the health conditions of the population, this study developed a motion feedback system named MoveV that can be used for several indoor training exercises. This system provides instant motion feedback by synchronizing exercise training videos on the website using a motion analysis algorithm that is applicable on smartphones, and a cloud database platform is used to record health behaviors. Feature extraction is performed based on force intensity, motion velocity, and exercise direction. The resultant accuracy of the motion feedback system was tested by a motion science expert and presented as the confidence level. For perfect movement, a confidence level of up to 90.5% was achieved, indicating that the MoveV system was able to record users’ exercise frequency and distinguish whether the user was performing well in the exercise movements. The proposed system is convenient and does not incur additional expenditure by purchasing any new device. Furthermore, it provides visual and voice feedback, companionship, and exercise motivation to the users, all of which are important factors when using online exercise platforms.

A NOVEL MACHINE LEARNING-BASED APPROACH FOR IDENTIFICATION OF UNREALISTIC TAX RETURNS BY EEG SIGNAL PROCESSING

For many years, the uncertainty of lie-detection systems has been one of the concerns of tax organizations. Clearly, the results of these systems must be generalized by a high value of accuracy to be acceptable by related systems to identify tax fraud. In this paper, a new method based on P300-based component has been proposed for detection of tax fraud. To this end, the test protocol is designed based on Odd-ball paradigm concealed information recognition. This test was done on 40 people and their brain signals were acquired. After prepossessing, the classic features are extracted from each single trial. After that, time–frequency (TF) transformation is applied on the sweeps and TF features are produced thereupon. Then, the best combinational feature vector is selected in order to improve classifier accuracy. Finally, guilty and innocent persons are classified by K-nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers. We found that combination of time–frequency and classic features has better ability to achieve higher amount of accuracy to identify the unrealistic tax returns. The obtained results show that the proposed method can detect deception by the accuracy of 91% which is better than other previously reported methods. This study, for the first time, succeeded in presenting a novel method for identifying unrealistic tax returns through EEG signal processing, which has significantly improved the yield of this study compared to the previous literature.

INSIGHTS ON ATRIAL ELECTROCARDIOGRAM IN SINUS RHYTHM AND ATRIOVENTRICULAR BLOCK FOR IMPROVED CLINICAL DIAGNOSIS

Objective: This study aims to characterize P and Ta wave of Modified Limb Lead (MLL) Electrocardiogram (ECG) in Normal Sinus Rhythm (NSR) and Atrioventricular Block (AVB). Methods: ECGs were recorded using MLL configuration from 100 NSR volunteers (mean age 31 years, 35 women) and 20 male AVB patients (mean age 72 years). Amplitudes and durations of P, Ta wave, and PTa Interval (PTaI) were measured, plotted, and analyzed for both the groups. Results: P-wave amplitudes were larger in AVB, and also P, Ta waves correlated significantly in both groups with higher correlation in AVB (NSR: [Formula: see text]; AVB: [Formula: see text]). Ta-wave duration ([Formula: see text] ms) was longer than P-wave duration ([Formula: see text] ms) in AVB patients and was opposite to P-wave polarity in all the leads. PP Interval (PPI) correlated significantly with P wave (NSR: [Formula: see text]; AVB: [Formula: see text]), Ta wave ([Formula: see text]; [Formula: see text]), PTaI ([Formula: see text]; [Formula: see text]), and corrected PTaI ([Formula: see text]; [Formula: see text]). Conclusion: P-wave right axis shift leads to the higher P-wave amplitude in AVB which may be due to the advancing age and atrial chamber enlargement. In NSR, the duration of observable Ta wave was longer than P wave, whereas in AVB, the Ta wave duration was 3–3.5 times longer than P wave.

DIAGNOSIS OF COVID-19 BASED ON ARTIFICIAL INTELLIGENCE MODELS AND PHYSIOLOGICAL SENSORS: REVIEW

Covid-19 invaded the world very quickly and caused the loss of many lives; maximum emergency was activated all over the world due to its rapid spread. Consequently, it became a huge burden on emergency and intensive care units due to the large number of infected individuals and the inability of the medical staff to deal with patients according to the degree of severity. Covid-19 can be diagnosed based on the artificial intelligence (AI) model. Based on AI, the CT images of the patient’s chest can be analyzed to identify the patient case whether it is normal or he/she has Covid-19. The possibility of employing physiological sensors such as heart rate, temperature, respiratory rate, and SpO2 sensors in diagnosing Covid-19 was investigated. In this paper, several articles which used intelligent techniques and vital signs for diagnosing Covid-19 have been reviewed, classified, and compared. The combination of AI and physiological sensors reading, called AI-PSR, can help the clinician in making the decisions and predicting the occurrence of respiratory failure in Covid-19 patients. The physiological parameters of the Covid-19 patients can be transmitted wirelessly based on a specific wireless technology such as Wi-Fi and Bluetooth to the clinician to avoid direct contact between the patient and the clinician or nursing staff. The outcome of the AI-PSR model leads to the probability of recording and linking data with what will happen later, to avoid respiratory failure, and to help the patient with one of the mechanical ventilation devices.

DETERMINATION OF FIBER ORIENTATION IN HIGH ANGULAR RESOLUTION DIFFUSION IMAGING USING SPHERICAL HARMONICS AND PARTICLE SWARM OPTIMIZATION

The fiber directions in High Angular Resolution Diffusion Imaging (HARDI) with low fractional anisotropy or low Signal to Noise Ratio (SNR) cannot be estimated accurately. In this paper, the fiber directions are estimated using Particle Swarm Optimization and Spherical Deconvolution (PSO-SD). Fiber orientation is modeled as a Dirac delta function in [Formula: see text]. The Spherical Harmonic Coefficients (SHC) of the Dirac delta function in the [Formula: see text] direction are obtained using the rotational harmonic matrix and the SHC of the Dirac delta function in the [Formula: see text]-axis. The PSO-SD method is used to determine ([Formula: see text]). We generated noise-free synthetic data for isotropic regions (FA varied from 0.1 to 0.8) and synthetic data with two crossing fibers for anisotropic regions with SNRs of 20, 15, 10 and 5 (FA [Formula: see text] 0.78). In the noise-free signal (FA [Formula: see text] 0.3), the Success Ratio (SR) and Mean Difference Angle (MDA) of the PSO-SD method were 1∘ and 9.48∘, respectively. In the noisy signal (FA [Formula: see text] 0.78, SNR [Formula: see text] 10, crossing angle [Formula: see text] 40), the SR and MDA of PSO-SD (with [Formula: see text]) were 0.46∘ and 12.3∘, respectively. The PSO-SD method can estimate fiber directions in HARDI with low fractional anisotropy and low SNR. Moreover, it has a higher SR and lower MDA in comparison with those of the super-CSD method.

AUTOMATED POLYP DETECTION IN COLONOSCOPY VIDEOS USING IMAGE ENHANCEMENT AND SALIENCY DETECTION ALGORITHM

Colonoscopy has proven to be an active diagnostic tool that examines the lower half of the digestive system’s anomalies. This paper confers a Computer-Aided Detection (CAD) method for polyps from colonoscopy images that helps to diagnose the early stage of Colorectal Cancer (CRC). The proposed method consists primarily of image enhancement, followed by the creation of a saliency map, feature extraction using the Histogram of Oriented-Gradients (HOG) feature extractor, and classification using the Support Vector Machine (SVM). We present an efficient image enhancement algorithm for highlighting clinically significant features in colonoscopy images. The proposed enhancement approach can improve the overall contrast and brightness by minimizing the effects of inconsistent illumination conditions. Detailed experiments have been conducted using the publicly available colonoscopy databases CVC ClinicDB, CVC ColonDB and the ETIS Larib. The performance measures are found to be in terms of precision (91.69%), recall (81.53%), F1-score (86.31%) and F2-score (89.45%) for the CVC ColonDB database and precision (90.29%), recall (61.73%), F1-score (73.32%) and F2-score (82.64%) for the ETIS Larib database. Comparison with the futuristic method shows that the proposed approach surpasses the existing one in terms of precision, F1-score, and F2-score. The proposed enhancement with saliency-based selection significantly reduced the number of search windows, resulting in an efficient polyp detection algorithm.