Biomedical Engineering: Applications, Basis and Communications

Background and Objective: Regarding the Coronavirus disease-2019 (COVID-19) pandemic in past years and using medical images to detect it, the image processing of the lungs and enhancement of its quality are some of the challenges in the medical image processing field. As it sounds from previous studies, the lung image processing has been raised in the other lung diseases such as lung cancer, too. Thus, the accurate classifying between normal lung image and abnormal is a challenge to aid physicians.

Methods: In this paper, we have proposed an image fusion technique to increase the accuracy of classifier. In this technique, some signal preprocessing tools like discrete wavelet transform (DWT), wavelet scattering transform (WST), and image fusion by using DWT are employed to enhance ordinary convolutional neural network (CNN) classifier accuracy.

Results: Unlike other studies, in this paper, different aspects of an image are fused with itself to emphasize its information which may be neglected in a total assessment of the image. We have achieved 89.8% accuracy for very simple structure of CNN classifier without using proposed fusion, and when we used proposed methods, the classifier accuracy increased to 91.8%.

Conclusions: This study reveals using efficient preprocessing and presenting input images which lead to decrease the complications of deep learning classifier, and increase its accuracy overall.

Renal cysts are categorized as simple cysts and complex cysts. Simple cysts are harmless and complicated cysts are cancerous and leading to a dangerous situation. The study aims to implement a deep learning-based segmentation that uses the Renal images to segment the cyst, detecting the size of the cyst and assessing the state of cyst from the infected renal image. The automated method for segmenting renal cysts from MRI abdominal images is based on a U-net algorithm. The deep learning-based segmentation like U-net algorithm segmented the renal cyst. The characteristics of the segmented cyst were analyzed using the Statistical features extracted using GLCM algorithm. The machine learning classification is performed using the extracted GLCM features. Three machine learning classifiers such as Naïve Bayes, Hoeffding Tree and SVM are used in the proposed study. Naive Bayes and Hoeffding Tree achieved the highest accuracy of 98%. The SVM classifier achieved 96% of accuracy. This study proposed a new system to diagnose the renal cyst from MRI abdomen images. Our study focused on cyst segmentation, size detection, feature extraction and classification. The three-classification method suits best for classifying the renal cyst. Naïve Bayes and Hoeffding Tree classifier achieved the highest accuracy. The diameter of cyst size is measured using the blobs analysis method to predict the renal cyst at an earlier stage. Hence, the deep learning-based segmentation performed well in segmenting the renal cyst and the three classifiers achieved the highest accuracy, above 95%.

Breast cancer (BC) is a critical health issue that affects countless women, and it is the second leading reason of death worldwide. The phosphatidylinositol 3 kinases (PI3Ks) constitute a group of lipid kinases that play a role in tumorigenesis, development, migration, infiltration, programmed cell death, glycogen synthesis, DNA correction and viability by the PI3K/Akt cascade. The PI3K pathway has been linked to a variety of malignancies and increases the activation rate of cancer. Here, focus was given to the study of PI3K pathway involved in BC and emphasis was given on a particular nSH₂ domain that resides in the regulatory subunit of PI3K to find a potent inhibitor. A detailed pathway and interaction study was performed from KEGG pathway database and from the cystoscope. A total list of 60 compounds, comprises phytochemicals, and herbal compounds were screened based on structural similarity and eight FDA-approved drugs were considered. The docking analysis was carried over through the AutoDock software and Ligplot analysis was performed to investigate the interaction between the nSH₂ domain and the potent inhibitors. To ensure the complex stability, 20 ns of simulation run was also performed on the best complexes using GROMACS. From this study, it can be concluded that Evodia fruit has the maximum stability in the catalytic region among all the listed inhibitors against the target proteins and can act as a potent inhibitor among the others.

Cancer is a major health concern that affects a significant number of people worldwide and can often result in fatalities. Therefore, there is a growing need to develop effective approaches for early diagnosis and classification of different types of cancer. Early detection of cancer is crucial for prompt and accurate treatment. Thus, researchers have been working to identify non-invasive and precise methods for the early diagnosis, monitoring, and control of cancer. Leukemia and prostate cancer are two of the most common types of cancer globally. Microarray data analysis has become a valuable tool for diagnosing and classifying different types of cancerous tissues. To improve the accuracy of diagnosis, hybrid algorithms and neural networks are being employed. This paper provides a review of different biomarkers for leukemia and prostate cancer and proposes a novel method for distinguishing between the two cancers. The proposed method includes appropriate gene selection, a new hybrid model, and differential analysis of microarray data to create a diagnostic tool. The results indicate that the proposed algorithm is highly accurate and efficient in selecting a small set of valuable genes to improve classification accuracy. In conclusion, the accurate diagnosis and classification of cancer are essential for timely and effective treatment. The proposed method can contribute to the development of a reliable diagnostic tool for leukemia and prostate cancer, and the application of microarray data and hybrid algorithms can be useful for diagnosing other types of cancer as well.

Bone defects affect many people and impose expenses of costly treatment with possible complications. This study aims to investigate a novel Poly ($𝜀$-caprolactone)/Poly (L-lactic) acid/Gelatin nanofiber [PCL/PLA/GNF] scaffold containing 5% resveratrol (Resv) which was manufactured via thermally induced phase separation technique (TIPS), and its applicability for bone defect treatment. Gelatin nanofiber (GNF) was synthesized via the electrospinning method and mixed with PCL/PLA solution and then 5% resveratrol was added to fabricate a 3D scaffold via the TIPS technique. The prepared scaffolds were evaluated regarding their porosity, morphology, contact angle, degradation properties, biomechanical, blood compatibility, and cell viability via MTT assay. The scaffolds were further investigated by implantation in a rat femur defect model. PCL/PLA/GNF with 5% Resv showed a cancellated structure with irregular-shaped pores. The mean pore size was estimated to be 160 $\mu$m and the porosity was 80.56 ± 2.68%. The contact angle of the fabricated scaffold was 95.4 ± 3.4, which determines the hydrophobic nature of the scaffold. Increased cell viability in scaffolds was observed by adding resveratrol. Twelve weeks after the implantation of the scaffold into the bone defect, the defects filled with PCL/PLA/GNF-resveratrol contained scaffold were remarkably better than PCL/PLA/GNF and negative control group (89.23 ± 6.34% in 12 weeks), and the difference was significant (p ¡ 0.05). In conclusion, the PCL/PLA/GNF scaffold containing 5% of resveratrol demonstrated adequate mechanical and physical properties. There is possible applicability of PCL/PLA/GNF scaffold containing 5% of resveratrol for surgical treatment of bone defects.

Lung cancer is a major global health concern and a leading cause of cancer-related deaths. Accurate diagnosis and treatment of lung cancer are crucial for improving patient outcomes. Subtyping lung cancer provides essential information about its molecular characteristics, clinical behavior, and prognosis, thereby guiding treatment planning. Radiomics, a novel discipline, offers a promising approach to characterize the tumor microenvironment by extracting quantitative imaging features from medical images. Radiomics aims to comprehensively and non- invasively characterize tumors and their microenvironment, enabling the identification of tumor subtypes, prediction of therapy response, and enhancement of patient outcomes. This study evaluates the effectiveness of a Particle Swarm Optimization-Random Forest (PSO-RF) classifier for subtype categorization of lung cancer based on radiomics using computed tomography (CT) images. The study utilizes three datasets, extracting 1093 radiomic features and reducing them to 20 significant features through extra tree feature selection. Optimized parameters of the PSO-RF classifier are determined using 10-fold cross-validation and compared to traditional machine learning classifiers and reported works. Results demonstrate that the PSO-RF classifier outperforms other methods, achieving an accuracy of 92%, precision of 92.5%, recall of 92%, and $F$ 1-score of 0.92 in the Lung1 dataset. Training on Dataset 3 and validating the Lung3 dataset confirm the generalizability of the model, yielding an accuracy of 87% and an AUC of 0.91 across diverse scenarios. These findings highlight the efficacy of radiomics in identifying lung cancer subtypes and demonstrate the potential of the PSO-RF classifier. The incorporation of radiomics into clinical practice has the potential to greatly improve patient outcomes by customizing treatment approaches according to unique tumor characteristics. The demonstrated effectiveness of the PSO-RF classifier makes it a valuable resource for diagnosing and categorizing different subtypes of lung cancer.

Cardiac Arrhythmia is an abnormal heart rhythm that develops when the electrical impulses control the heart’s contraction which does not function properly. The heart can beat too fast (tachycardia), too slow (bradycardia), or in an irregular pattern. Observing ECG signal peaks and channels freehand is difficult due to their ingenious modification. Automated detection of cardiovascular abnormalities is preferred for the early diagnosis of cardiac disorders. This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various morphological characteristics are analyzed from the signal penetrated by the classifier to diagnose the irregular beats. A set of twenty-two clinically feasible features comprising temporal, morphological, and statistical were extracted from the processed ECG signals and applied to the classifier to categorize cardiovascular irregularities like Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Beats (APB), and Premature Ventricular Contraction (PVC). The Beth Israel Hospital at Massachusetts Institute of Technology (MIT-BIH) dataset has been used for this work, where feature datasets are split into training and evaluation subsets. The training set is used to train machine learning models on the extracted features, while the evaluation set is used to assess the performance of the trained models. The evaluation metrics such as Accuracy (Acc), Sensitivity (Se), Specificity (Sp), and Positive Predictivity (Pp), are frequently used to evaluate the model’s performance in Arrhythmia detection along with classification. The simulation has been conducted using SVM and CS-DT classifier with performance for all individual class labels at a Confidence Factor (CF) of 0.5. The performance of the time and frequency domain features is merged resulting in higher classification of Sensitivity, Specificity, Positive Predictivity, and Accuracy of 89.5%, 98.11%, 87.76%, and 96.8% in SVM, 97.71%, 99.58%, 97.66%, 99.32% in CS-DT classifier in identifying the irregular heartbeats.

Background: Sit-to-stand (STS) is an integral daily life activity. Handrail height significantly affects STS. However, the multifactorial influences of horizontal handrail height and width on STS have not been investigated.

Purpose: The purpose of this study was to evaluate the influence of different heights and widths of horizontal handrails on the motion time, joint angles, and joint moments during STS to determine the optimal handrail height and width during STS.

Methods: The study was conducted on 16 healthy young adults. Six experimental conditions were tested: high handrail large width; high handrail small width; medium handrail large width; middle handrail small width; low handrail large width; low handrail small width. The movement time, joint angle, and joint moment were analyzed and compared.

Results: Different handrail heights had a significant influence on the percent of motion time in the first phase. Only handrail height significantly influenced the maximum trunk tilt angle. There was an interaction between handrail height and width for the peak hip joint moment.

Conclusions: These findings indicated that people who have difficulty leaning forward will expend less effort and backward falls can be prevented when using the high handrail. The large width can be particularly helpful for patients with poor hip strength. Therefore, patients with impaired lower extremity strength can employ a high handrail with a large width to reduce the burden of performing STS transfers.