Computer-aided Techniques Research Articles

The Channel Migration of Inland Waterway Channels in Ilaje, Ondo State, Nigeria

The channel migration or shifting of navigable river channels have been an issue of major concern, as their effects on the transport environment, channel corridors, the manoeuvrability of vessels transiting these channels and vessels navigation amongst other factors result to inefficiency in inland transport in the riverine region of Ilaje, Nigeria. Most navigational and operational challenges have been attributable to alterations in the waterway channel due to aggradation and degradation processes in the river regime. The study’s goal is to explore channel migration along the Igbokoda-Ayetoro waterway with a view to provide mitigation methods to address potential difficulties along the waterway channel and in the adjoining environment. The objectives of the study include gathering satellite imagery of the study area between 1972 and 2022; assessing the morphological planforms of the study river channel, and assessing the river channel’s effective width using the segment-transect method. The study considered channel width, depth and alignment as indicators to be carefully evaluated to ensure safe, sustainable and efficient inland navigation measures. To ascertain the shapes and forms of the channel during the study period, geospatial and computer-aided techniques were used. Channel widths were extracted using the Segment-Transect method at 100-meter intervals. 50 year and 100-year estimate of the extent of channel/banklines shifting were determined. According to the study, there are changes in the channel’s width, depth and planform (alignment) that make the waterway indeterminate and unsuitable for vessels transiting on two-way manoeuvrability lanes. Hence, the study recommended the sustainable mitigation strategies to tackle the potential challenges pose by channel migration and reduce its impact on the vessel manoeuvrability, navigation and the adjoining environment.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Endodontic Microsurgery with the Aid of Dynamic Navigation System Using Minimally Invasive Incision Approach in Anatomically Complex Scenarios: A Case Series

Endodontic microsurgery (EMS) is an alternative treatment option for previously treated teeth with persistent periapical disease, offering a favorable prognosis. However, it can be particularly challenging in anatomically complex cases, risking unintentional damage to adjacent critical structures. Dynamic navigation (DN), a computer-aided technique, facilitates precise presurgical planning and provides real-time guidance during operation. Integrating the DN technique into EMS procedures can enhance treatment accuracy and effectiveness. This report presents 4 cases involving a minimally invasive incision approach for DN-aided EMS in anatomically complex regions, all of which were completed without complications. After 3–12 months, patients presented asymptomatic, and radiographic examinations revealed complete healing or the periapical radiolucent area decreased in size. The DN technique contributed to reliably mitigating the risk of iatrogenic damage in complicated EMS scenarios. The minimally invasive incision approach not only provided sufficient visibility but also promoted favorable soft tissue healing, making it a valuable addition to surgical endodontics.

On-Site Sensor Sensitivity Adjustment Technique for a Maintenance-Free Heat Flow Monitoring in Building Systems

In this paper, an advanced solution for measuring heat flow through opaque building elements is presented. The solution is based on the implementation of a computer-aided technique for continuous monitoring of heat flow sensor performance and silent checking of their accuracy. In principle, the technique provides an ex-post compensation of potential deviations and inaccuracies detected during the measurement, which can be done without interfering with the ongoing experiment. As a consequence, traditional ‘non-smart’ sensors can be turned into advanced sensors with self-sensing or self-adjustment features at nearly zero additional costs. The high efficiency of the proposed approach was validated against experimental data obtained from an independent set of advanced high-sensitive sensors. Considering the validation results, the proposed technique brings an entirely new potential for maintenance-free applications for thermal performance monitoring in the building sector, typically for long-term experiments or measurements under dynamic environments.

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

Self-assembling peptides induced by eyes absent enzyme to boost the efficacy of doxorubicin therapy in drug-resistant breast cancer cells

Enzyme-induced self-assembly (EISA) is a recently developed nanotechnology technique in which small molecules are induced by cellular enzymes self-assembling into nanostructures inside cancer cells. This technique can boost the efficacy of chemotherapy drugs by avoiding drug efflux, inhibiting the cells’ DNA repair mechanisms, and targeting the mitochondria. In this work, we study the self-assembly of a short peptide and its fluorescence analogue induced by Eyes absent (EYA) tyrosine phosphatases to boost the efficacy of doxorubicin (DOX) therapy in drug-resistant types of breast cancer cells, MDA-MB-231 and MCF-7. The peptides Fmoc–FF–YP and NBD–FF–YP were synthesized with the solid-phase peptide synthesis (SPPS) method and analyzed with HPLC and MALDI-TOF. Dynamic light scattering was used to determine the size distribution of peptides exposed to the EYA enzyme in vitro. The presence of EYA enzymes in breast cancer cells was confirmed using the western blotting assay. The intracellular location of the peptide self-assembly was studied by imaging fluorescence NBD-tagged peptides. The efficacy of the peptide alone and with DOX was determined against MCF-7 and MDA-MB-231 using MTT and LIVE-DEAD assays. Nucleus and cytoplasm F-actin (Phalloidin) staining was used to determine cell morphology changes in response to the combination therapy of peptides/DOX. At an optimal concentration, the peptides are not toxic to the cells; however, they boost the efficacy of DOX against drug-resistant breast cancer cells. We used state-of-the-art computer-aided techniques to predict the molecular structure of peptides and their interactions with EYA. This study demonstrates an approach for incorporating non-cytotoxic components into DOX combination therapy, thereby avoiding increased systemic burden or adverse effects.

Bio-inspired metal extraction: Tailoring peptides for highly selective scandium recovery

Scandium, a pivotal metal with widespread applications, faces challenges in recovery due to its highly dispersed distribution. Biological molecules such as proteins and peptides have shown great potential in the field of metal recovery due to their unparalleled affinity and remarkable selectivity. This research aims to design a scandium-binding peptide by mutating the amino acids and optimizing the structure of the EF-hand peptide derived from lanmodulin, a naturally lanthanides-binding protein. Utilizing computer-aided techniques, three peptides-P1 and its two variants (P2 and P3), were screened and synthesized to characterize their interaction with scandium and other competing metals through circular dichroism (CD), atomic force microscopy (AFM), fourier transform infrared spectroscopy (FTIR) as well as column adsorption experiments. Results showed that the peptide P2 and P3 exhibited higher affinity to scandium than P1, as a result of the more binding sites and the existence of hydrogen bonds in the peptides. Moreover, scandium could induce the significant change in backbone structure of these peptides, and coordinated with carboxyl groups of aspartic acid and glutamic acid. The column adsorption experiments with immobilized peptide P3 on microbeads revealed that a final scandium purity of 96.71 % was achieved and a enrichment coefficient of > 103 was obtained. This study offers a transformative strategy for selective scandium extraction from complex industrial matrices and lays the foundation for future innovations in green extraction technologies.

How do emotions affect giving? Examining the effects of textual and facial emotions in charitable crowdfunding

Drawing on emotional contagion theory and language-mediated association theory, this study develops a research model to examine how textual and facial emotions affect charitable crowdfunding performance. We use computer-aided techniques to extract and measure specific textual and facial emotions in pitches. The proposed model is tested via regression analysis with a sample of 1372 campaigns collected from the largest charitable crowdfunding platform in China—Tencent Gongyi. Moreover, we conducted a fuzzy-set qualitative comparative analysis to examine the complementarity of textual and facial emotions, which supplements the regression analysis results. Our findings show that both textual and facial emotions can impact funding outcomes. However, the effects of specific emotions vary: some (e.g., textual sadness and facial anger) are positive, some (e.g., textual anger and facial fear) are negative, and others (e.g., textual fear, textual disgust, and facial sadness) are insignificant. Moreover, facial emotions complement textual emotions in their effects on funding outcomes. This research outlines a framework to offer a more detailed and comprehensive understanding of emotions in charitable crowdfunding. It also contributes to existing research by revealing the vital but complex role of emotions in the persuasive process of prosocial behaviors and by uncovering the different cognitive mechanisms underlying the impacts of textual and facial emotions.

PneumoCheck : Pneumonia Detection App

Pneumonia is among the most prevalent diseases, and due to lack of experts it is difficult to detect. This paper focuses on surveying and comparing the detection of lung disease using different computer-aided techniques used in application to increase the portability and suggests a revised model for detecting pneumonia. In this survey, we also tried to familiarize ourselves with the different image pre- processing techniques used to convert raw X-ray images into standard formats for analysis and detection, machine learning techniques such as CNN, Deep Learning techniques, which is an important phase in accurate pneumonia detection. Keywords- Pneumonia Detection, CNN, Chest Radiography, Image processing, Deep Learning, Machine Learning, Android Development.

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings.

A Literature Review for Detection and Projection of Cardiovascular Disease Using Machine Learning

The heart is a vital organ that is indispensable in ensuring the general health and welfare of individuals. Cardiovascular diseases (CVD) are the major health concern worldwide and a leading cause of death, leaving behind diabetes and cancer. To deal with the problem, it is essential for early detection and prediction of CVDs, which can significantly reduce morbidity and mortality rates. Computer-aided techniques facilitate physicians in the diagnosis of many heart disorders, such as valve dysfunction, heart failure, etc. Living in an "information age," every day million bytes of data are generated, and we can turn these data into knowledge for clinical investigation using the technique of data mining. Machine learning algorithms have shown promising results in predicting heart disease based on different risk parameter. In this study, for the purpose of predicting CVDs, our aim is to appraise and examine the outputs generated by machine learning algorithms including support vector machines, artificial neural network, logistic regression, random forest and decision trees.This literature survey highlights the correctness of different machine learning algorithms in forecasting heart problem and can be used as a basis for building a Clinical decision-making aid to detect and prevent heart disease at an early stage.

Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation

The development of automated methods for analyzing medical images of colon cancer is one of the main research fields. A colonoscopy is a medical treatment that enables a doctor to look for any abnormalities like polyps, cancer, or inflammatory tissue inside the colon and rectum. It falls under the category of gastrointestinal illnesses, and it claims the lives of almost two million people worldwide. Video endoscopy is an advanced medical imaging approach to diagnose gastrointestinal disorders such as inflammatory bowel, ulcerative colitis, esophagitis, and polyps. Medical video endoscopy generates several images, which must be reviewed by specialists. The difficulty of manual diagnosis has sparked research towards computer-aided techniques that can quickly and reliably diagnose all generated images. The proposed methodology establishes a framework for diagnosing coloscopy diseases. Endoscopists can lower the risk of polyps turning into cancer during colonoscopies by using more accurate computer-assisted polyp detection and segmentation. With the aim of creating a model that can automatically distinguish polyps from images, we presented a modified DeeplabV3+ model in this study to carry out segmentation tasks successfully and efficiently. The framework's encoder uses a pre-trained dilated convolutional residual network for optimal feature map resolution. The robustness of the modified model is tested against state-of-the-art segmentation approaches. In this work, we employed two publicly available datasets, CVC-Clinic DB and Kvasir-SEG, and obtained Dice similarity coefficients of 0.97 and 0.95, respectively. The results show that the improved DeeplabV3+ model improves segmentation efficiency and effectiveness in both software and hardware with only minor changes.

Design of novel isoxazole derivatives as tubulin inhibitors using computer-aided techniques: QSAR modeling, in silico ADMETox, molecular docking, molecular dynamics, biological efficacy, and retrosynthesis

In the current work, computational methods were used to investigate new isoxazole derivatives that could be used as tubulin inhibitors. The study aims to develop a reliable quantitative structure-activity relationship (QSAR) model, following the criteria set by Golbraikh, Tropsha, and Roy. As a result, seven candidate compounds were developed, all having higher activity than the well-established anticancer agent Cisplatin (Cisp). According to the ADMETox in silico test, the candidates Pr4, Pr5, and P6 can be toxic. As a result, we have chosen to focus our study on compounds Pr1, Pr2, and Pr3. Molecular docking analysis revealed that drug candidate Pr2 exhibits the highest stability within the oxidized quinone reductase 2 (PDB ID: 4zvm), target receptor (ΔG(Pr2) = ΔG(Pr3) = −10.4 < ΔG(Pr1) = −10.0 < ΔG(Cisp) = −7.3 kcal/mol). This finding aligns with the activity predictions made by the QSAR model. Furthermore, molecular dynamics simulations of the Pr2–4zvm complex over 100 ns confirm the ligand’s robust stability within the receptor’s active site, supporting the results obtained from molecular docking and the QSAR model predictions. The CaverDock software was utilized to identify the tunnels likely to be followed by ligands moving from the active site to the receptor surface. This analysis also helped in determining the biological efficacy of the target compounds. The results indicated that the Pr2 compound is more effective than the others. Finally, the computer-assisted retrosynthesis process of two high confidence sequences was used to synthesize drug candidates. Communicated by Ramaswamy H. Sarma

A Comparative Study of Antibacterial Activity of Leaf and Root Extracts of Sida acuta Burm. F. Against Staphylococcus sciuri Isolated From Acne Using Computer-Aided Technique

This study investigated the significant anti-bacteriological efficacy between leaf and root of Sida acuta Burm. F. extracts against acne inducing bacteria. Twelve samples were collected from twelve persons with facial acne. Nutrient agar was used for the isolation of the bacteria and 16S rRNA gene sequencing technique was used for molecular identification. Bioinformatics analysis was carried out using Basic Local Alignment Search Tools (BLAST) for similarity, Cluster W software for multiple sequence alignment and MEGA 7 software for the phylogenetic analysis. Antibacterial susceptibility test of the bacterial isolates was carried out using the agar well diffusion method. A total number of six bacterial species were identified as Staphylococcus sciuri. The study revealed that control antibiotic- Ciprofloxacin (CFX) inhibited the growth of all the bacterial isolates. Staphylococcus sciuri was susceptible to leaf ethanol extract of Sida acuta at 75 μg/ml and 100 μg/ml and was susceptible to the aqueous root extract at 25 μg/ml and 100 μg/ml while it was susceptible to the ethanol root extract at 2.5 μg/ml. From this study, it was shown that the aqueous root extracts had more zones of inhibition with the maximum inhibition of 45mm at 100 μg/ml. Hence, Sida acuta Burm. F. is a powerful antimicrobial agent.

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques

Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment. This review study's objective is to analyze the relevant research works on computer-assisted methods for automatically identifying PCOS through a systematic literature review (SLR) methodology as well as investigate the research limitations and explore potential future research scopes in this domain. 28 articles have been selected using the PRISMA approach based on a set of inclusion-exclusion criteria for conducting the review. The data synthesis of the selected articles has been conducted using six data exploration themes. As outcomes, the SLR explored the topical association between the studies; their research profiles; objectives; data size, type, and sources; methodologies applied for the detection of PCOS; and lastly the research outcomes along with their evaluation measures and performances. The study also highlights areas for future research directions examining the study gaps to enhance the current efforts for autonomous PCOS identification; such as integrating advanced techniques with the current methods; developing interactive software systems; exploring deep learning and unsupervised machine learning techniques; enhancing datasets and country context; and investigating more unknown factors behind PCOS. Thus, this SLR provides a state-of-the-art paradigm of autonomous PCOS detection which will support significantly efficient clinical assessment, diagnosis and treatment of PCOS.

Using an innovative method for breast cancer diagnosis based on Extreme Gradient Boost optimized by Simplified Memory Bounded A*

Breast cancer (BC) is a prevalent form of cancer among women and is responsible for a significant number of cancer-related deaths. Early detection of BC is crucial in preventing its progression and reducing mortality rates. Accurately classifying tumors as either benign or malignant helps avoid unnecessary treatments. In recent years, computer-aided techniques have gained popularity for pattern recognition and predictive modeling due to their ability to detect important features. This paper introduces a novel machine learning approach for BC diagnosis. Six machine learning methods, including Stochastic Gradient Descent (SGD), Gaussian Process (GP), Ada Boost, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), and Extreme Gradient Boost (XGBoost), are employed to classify the dataset. The performance of these methods is evaluated using metrics such as accuracy, precision, recall, and F1-score. To improve classification performance, the dataset is oversampled using the random oversampler technique. Additionally, the selected method is combined with five optimizers to further enhance performance. Among the methods evaluated, XGBoost achieves an accuracy of 96.48% and an F1-score of 95.11% on the base dataset. After applying the oversampling process, these metrics improve to 98.17% and 98.19%, respectively. The inclusion of optimizers enhances the performance of XGBoost, with the XGBoost + SMA mixed method demonstrating the best performance, achieving an accuracy of 98.45% and an F1-score of 98.47%. The results indicate the potential of this method (combination of XGBoost and S-MBA* algorithm) in improving the accuracy and efficiency of breast cancer diagnosis, contributing to early detection and improved patient outcomes.

Prediction of therapy for ischemic heart disease from PPG signals using fuzzy GRU network

BackgroundCardiac morbidity like ischemic heart disease (IHD) causes global mortality. Diagnosis of IHD requires coronary angiograms — an invasive, sophisticated, and expensive procedure. However, non-invasive methods could not gain much confidence in swift diagnosis of IHD patients. These potential issues provided the research motivation for diagnosing IHD priorly in this pilot study. ObjectiveA computer-aided technique, as an alternate method to fasten and ease IHD detection and categorization, has been suggested in this cohort study. Here, the classification was conducted by a sandwich model of GRU and fuzzy logic in a deep GRU Fuzzy network. MethodsIn this work, photoplethysmography (PPG) signals were acquired from 355 IHD patients. Gabor–Winger transform was used to derive signal features using statistical identities. Analysis was done with a Fuzzy GRU Network comprising GRU and BiGRU layers with fuzzy layers. An algorithm was designed with a Rete network and three membership functions to deduce fuzzy inference. Then, Choquet Integral was used for defuzzification. ResultsThe proposed network predicted results with 0.85 accuracy, 0.86 recall, 0.84 precision, 0.86 sensitivity, 0.78 Micro-averaged F1-score, and 0.84 specificity. The network architecture was validated using ablation and comparative studies. The PRC and ROC curves gave the highest values of 0.91 AP and 0.90 AUC, respectively for this network. A state-of-the-art study was done to show the superiority of the technique. ConclusionThis computer-aided model using PPG might become a tool in predicting the type of therapy for IHD patients which will provide a non-invasive, inexpensive, and fast mode of diagnosis.

Pixel-level image analysis to derive the broncho-artery (BA) ratio employing HRCT scans: A computer-aided approach

Bronchiectasis in children is a major health issue which can be life-threatening if not diagnosed and effectively treated. In the diagnosis of bronchiectasis, an increased broncho-arterial (BA) ratio is considered a significant marker. The BA ratio is measured by evaluating BA pairs, using high-resolution computed tomography (HRCT) scans. Detecting BA pairs automatically is challenging due to the complex characteristics of BA pairs and the ambiguous appearance of the bronchi. This study proposes an effective computerized approach to detect BA pairs and assess BA ratio using HRCT scans of children and employing computer-aided techniques and novel custom-build algorithms. Attention is given to reconstructing broken bronchial walls and identifying discrete BA pairs using custom-built kernel based and patch-based algorithms for pixel-level image analysis. To detect BA pairs, the lung region is segmented in the HRCT slices and image preprocessing techniques, including noise reduction, binarizing, largest contour detection and a hole-filling algorithm, are applied. A histogram analysis method is introduced to clean the images. A kernel-based algorithm is proposed to reconstruct the pixel distribution if the bronchial wall is so that the bronchi can be detected precisely. Potential arteries are detected using balanced histogram thresholding, morphological opening and an approach based on four conditions related to the object area circularity, rectangular boundary box ratio and enclosing circle area ratio. Potential bronchi are detected through matching of object coordinates with potential arteries, hole-filling and four condition based approaches. The potential BA pairs are detected by matching the coordinates of potential bronchi with those of potential arteries as the artery and bronchus are adjacent to each other in BA pairs. Finally, from the potential BA pairs, actual BA pairs are identified using a custom-built patch algorithm. The study is conducted using 2471 HRCT slices of seven children, obtained from the Royal Darwin Hospital, Australia. The BA ratio is derived based on the ratio of diameters, major axis lengths, minor axis lengths, area, convex hull and equivalent diameter where the BA ratios are respectively 0.51–0.65, 0.49–0.59, 0.59–0.77, 0.25–0.42, 0.29–0.47, 1.5–2 and 0.50–0.65.

Hashimoto Thyroiditis - A Comprehensive Review

Hashimoto thyroiditis (HT) is an inflammatory disorder that often results in hypothyroidism. It has several global implications affecting different regions worldwide. Risk factors for acquiring HT include diet, inheritance, sex, epigenetics, and comorbidities. HT is a complex autoimmune illness characterized by an attack on the thyroid gland by the immune system. At the cellular level, genetic predisposition, environmental triggers, and the loss of immunological tolerance to thyroid antigens all play significant roles in the disease's genesis and progression. There are several mechanisms by which HT may develop, but the typical progression is autoimmune in origin. Many patients do not initially present with symptoms, but later develop symptoms of hypothyroidism. These symptoms include constipation, fatigue, dry skin, weight gain, cold intolerance, decreased energy, memory loss, muscle cramps, hair loss, and irregular menses. Patients with HT usually present with a firm, nontender neck goiter on a physical exam. To distinguish HT from other thyroid pathologies, patient serum should be collected to check for thyroid-specific auto-antibodies against thyroglobulin, thyroid peroxidase, thyroid stimulating hormone. Imaging studies such as ultrasonography with computer-aided techniques using gray-scale features are also diagnostic tools in differentiating between a healthy thyroid and HT. While the most common intervention of HT is synthetic hormone administration with levothyroxine sodium (L-T4), other alternatives or additional treatments include glucocorticoid use, diet alterations, selenium supplementation, vitamin D supplementation, and in more extreme cases, thyroidectomy. While research continues discovering new interventions, each treatment plan is highly variable based on underlying causes and presentations. This comprehensive review hopes to provide an up-to-date compact analysis of literature pertinent to the epidemiology, etiology, risk factors, genetics, pathophysiology, classifications, clinical presentation, diagnosis, and treatments of HT.

Designing multifunctional silica coatings for enhanced broadband antireflection and microfiber contamination sensing

Multifunctional silica coatings find diverse applications in solar energy conversion and pollution monitoring sensors due to their suitability for mass manufacturing. However, the challenge lies in producing robust silica coatings on a large scale with tunable refractive index. This work presents a two-step acid/base catalyzed sol–gel process for synthesizing multifunctional silica coatings with adjustable refractive index. The coatings' porous microstructure allows them to be used as double-layer broadband antireflective coatings and enhances the sensitivity of contamination sensors. The optimized double-layer coatings, designed using computer-aided techniques, demonstrate excellent broadband antireflective performance in the visible regions and high environmental durability. They achieve a maximum transmittance of 99.87% at 591 nm, with an average transmittance of 99.40% in the visible region (400–800 nm) and 98.63% from 400 nm to 1100 nm. Additionally, the coatings exhibit robust abrasion resistance and enhanced surface hydrophobicity with a water contact angle increased from 30° to 126° through HMDS treatment. This work investigated the particle growth mechanism and the relationship between silica nanoparticle microstructure and antireflective coating properties. Furthermore, the coatings' presence of abundant micropores allows successful application onto microfiber contamination sensors, the additional loss increases from 26.40 dB to 37.69 dB over 140 min and decreases to 31.55 dB within one minute, demonstrating rapid adsorption and desorption rates. The work successfully achieves a wide range of adjustable refractive index while maintaining excellent mechanical and optical properties, addressing the traditional trade-off between high porosity and poor mechanical stability. These rationally designed multifunctional silica coatings hold great potential for high-quality broadband antireflection and high sensitivity in microfiber contamination sensing.