X-rays imaging analysis for early diagnosis of the thoracic disorders using Capsule Neural Network with Transfer learning

Over the past few years, millions of people around the world have developed thoracic ailments. MRI, CT scan, reverse transcription, and other methods are among those used to detect thoracic disorders. These procedures demand medical knowledge and are exceedingly pricy and delicate. An alternate and more widely used method to diagnose diseases of the chest is X-ray imaging. The goal of this study was to increase detection precision in order to develop a computationally assisted diagnostic tool. Different diseases can be identified by combining radiological imaging with various artificial intelligence application approaches. In this study, transfer learning (TL) and capsule neural network techniques are used to propose a method for the automatic detection of various thoracic illnesses utilizing digitized chest X-ray pictures of suspected patients. Four public databases were combined to build a dataset for this purpose. Three pre trained convolutional neural networks (CNNs) were utilized in TL with augmentation as a preprocessing technique to train and evaluate the model. Pneumonia, COVID19, normal, and TB (Tb) were the four class classifiers used to train the network to categorize. © 2023 IEEE.

Randomized Placebo-Controlled Phase 3 Trial of Vibrating Capsule for Chronic Constipation.

BACKGROUND & AIMS: Despite therapeutic advances, effective treatments for chronic constipation remain an unmet need. The vibrating capsule is a nonpharmacologic, orally ingested, programmable capsule that vibrates intraluminally to induce bowel movements. We aimed to determine the efficacy and safety of the vibrating capsule in patients with chronic constipation. METHODS: We conducted a phase 3, double-blind, placebo-controlled trial of patients with chronic constipation, who were randomized to receive either a vibrating or placebo capsule, once daily, 5 days a week for 8 weeks. The primary efficacy end points were an increase of 1 or more complete spontaneous bowel movements per week (CSBM1 responder) or 2 or more CSBMs per week (CSBM2) from baseline during at least 6 of the 8 weeks. Safety analyses were performed. RESULTS: Among 904 patients screened, 312 were enrolled. A greater percentage of patients receiving the vibrating capsule achieved both primary efficacy end points compared with placebo (39.3% vs 22.1%, P = .001 for CSBM1; 22.7% vs 11.4% P = .008 for CSBM2). Significantly greater improvements were seen with the vibrating capsule for the secondary end points of straining, stool consistency, and quality-of-life measures compared with placebo. Adverse events were mild, gastrointestinal in nature, and similar between groups, except that a mild vibrating sensation was reported by 11% of patients in the vibrating capsule group, but none withdrew from the trial. CONCLUSIONS: In patients with chronic constipation, the vibrating capsule was superior to placebo in improving bowel symptoms and quality of life. The vibrating capsule was safe and well tolerated. (Clinical trials.gov, Number: NCT03879239).

Deciphering suppressive effects of Lianhua Qingwen Capsule on COVID-19 and synergistic effects of its major botanical drug pairs.

The COVID-19 pandemic has resulted in excess deaths worldwide. Conventional antiviral medicines have been used to relieve the symptoms, with limited therapeutic effect. In contrast, Lianhua Qingwen Capsule is reported to exert remarkable anti-COVID-19 effect. The current review aims to: 1) uncover the main pharmacological actions of Lianhua Qingwen Capsule for managing COVID-19; 2) verify the bioactive ingredients and pharmacological actions of Lianhua Qingwen Capsule by network analysis; 3) investigate the compatibility effect of major botanical drug pairs in Lianhua Qingwen Capsule; and 4) clarify the clinical evidence and safety of the combined therapy of Lianhua Qingwen Capsule and conventional drugs. Numerous bioactive ingredients in Lianhu Qingwen, such as quercetin, naringenin, ß-sitosterol, luteolin, and stigmasterol, were identified to target host cytokines, and to regulate the immune defence in response to COVID-19. Genes including androgen receptor (AR), myeloperoxidase (MPO), epidermal growth factor receptor (EGFR), insulin (INS), and aryl hydrocarbon receptor (AHR) were found to be significantly involved in the pharmacological actions of Lianhua Qingwen Capsule against COVID-19. Four botanical drug pairs in Lianhua Qingwen Capsule were shown to have synergistic effect for the treatment of COVID-19. Clinical studies demonstrated the medicinal effect of the combined use of Lianhua Qingwen Capsule and conventional drugs against COVID-19. In conclusion, the four main pharmacological mechanisms of Lianhua Qingwen Capsule for managing COVID-19 are revealed. Therapeutic effect has been noted against COVID-19 in Lianhua Qingwen Capsule.

Multiple Cerebral Infarcts Triggered by Sars-Cov-2 Virus in an Asymptomatic Cadasil Patient: Case Report

During the coronavirus pandemic, increasing evidence has confirmed that the SARS-CoV-2 virus is susceptible to increased risk of stroke. On the other hand, the relationship between the SARS-CoV-2 virus and CADASIL was among the topics discussed in the literature with a small number of cases. In this case report, we present multiple cerebral infarcts in an asymptomatic CADASIL patient and we aim to shed light on the complex nature of cerebrovascular manifestations of the SARS-CoV-2 virus. A 50-year-old man with an unremarkable past medical history was admitted to our department with fever and neurologic manifestations on the 6th day of self-isolation due to positive reverse-transcriptase-polymerasechain-reaction assay in a nasopharyngeal sample for SARS-CoV-2. Neurological deficits were related to the acute vascular lesions located in the border-zone areas of both hemispheres, corpus callosum, and cerebellar peduncles on brain MRI. Lesions in chronic nature in the bilateral subcortical white matter predominantly involving the external capsule and temporal poles were also challenging. As a result of a comprehensive study that could explain the neurological status and imaging findings, the CADASIL diagnosis is reached by genetic testing for NOTCH-3. The experience, in this case, suggests considering patients with suspicious MRI findings for CADASIL diagnosis during the coronavirus pandemic. Further studies are needed to explain the underlying pathophysiological mechanisms related to cerebrovascular manifestations of SARS-CoV-2.Copyright © 2022 by Turkish Cerebrovascular Diseases Society.

Developing a cooperative learning project: bridging the design, merchandising and retail processes

The purpose of this study is to develop and evaluate a cross-disciplinary collaborative project where students can engage in a cross-disciplinary collaborative learning environment in which students jointly develop a semester-long project designed to bridge the design, merchandising, and retailing processes. The cooperative learning model was employed for the development and implementation of the project. After completing the project, we evaluated the student learning experiences against the Student Learning Outcomes through qualitative (i.e. reflection papers) and quantitative analysis (i.e. pre and post surveys). Findings suggest that, through the Cotton Capsule Wardrobe project, students from both programs gained insights and knowledge of professional practices within the fashion industry. Despite the impact that the COVID-19 pandemic had in interrupting the later segments of the project, this research still provides valuable insight to the literature by demonstrating the application of the cooperative learning modules in cross-disciplinary environments.

Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method

The new coronavirus, which emerged in early 2020, caused a major global health crisis in 7 continents. An essential step towards fighting this virus is computed tomography (CT) scans. CT scans are an effective radiological method to detecting the diagnosis in early stage, but have greatly increased the workload of radiologists. For this reason, there are systems needed that will reduce the duration of CT examinations and assist radiologists. In this study, a two-stage system has been proposed for COVID-19 detection. First, a hybrid method is proposed that can segment the infected region from CT images. The reason for this is that there is not always a reference image in the datasets used in the classification. For this purpose;UNet, UNet++, SegNet and PsPNet were used both separately and as hybrids with GAN, to automatically segment infected areas from chest CT slices. According to the segmentation results, cGAN-UNet hybrid system was selected as the most successful method. Experimental results show that the proposed method achieves a segmentation success with a dice score of 92.32% and IoU score of 86.41%. In the second stage, three classifiers which include a Convolutional Neural Network (CNN), a PatchCNN and a Capsule Neural Network (CapsNet) were used to classify the generated masks as either COVID-19 or not, using the segmented images obtained from cGAN-UNet. Success of these classifiers was 99.20%, 92.55% and 73.84%, respectively. According to these results, the highest success was achieved in the system where cGAN-Unet and CNN are used together. © 2023 Lavoisier. All rights reserved.

Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study).

AIM: Lower gastrointestinal (GI) diagnostics have been facing relentless capacity constraints for many years, even before the COVID-19 era. Restrictions from the COVID pandemic have resulted in a significant backlog in lower GI diagnostics. Given recent developments in deep neural networks (DNNs) and the application of artificial intelligence (AI) in endoscopy, automating capsule video analysis is now within reach. Comparable to the efficiency and accuracy of AI applications in small bowel capsule endoscopy, AI in colon capsule analysis will also improve the efficiency of video reading and address the relentless demand on lower GI services. The aim of the CESCAIL study is to determine the feasibility, accuracy and productivity of AI-enabled analysis tools (AiSPEED) for polyp detection compared with the 'gold standard': a conventional care pathway with clinician analysis. METHOD: This multi-centre, diagnostic accuracy study aims to recruit 674 participants retrospectively and prospectively from centres conducting colon capsule endoscopy (CCE) as part of their standard care pathway. After the study participants have undergone CCE, the colon capsule videos will be uploaded onto two different pathways: AI-enabled video analysis and the gold standard conventional clinician analysis pathway. The reports generated from both pathways will be compared for accuracy (sensitivity and specificity). The reading time can only be compared in the prospective cohort. In addition to validating the AI tool, this study will also provide observational data concerning its use to assess the pathway execution in real-world performance. RESULTS: The study is currently recruiting participants at multiple centres within the United Kingdom and is at the stage of collecting data. CONCLUSION: This standard diagnostic accuracy study carries no additional risk to patients as it does not affect the standard care pathway, and hence patient care remains unaffected.

COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network.

This study proposes a deep-learning-based solution (named CapsNetCovid) for COVID-19 diagnosis using a capsule neural network (CapsNet). CapsNets are robust for image rotations and affine transformations, which is advantageous when processing medical imaging datasets. This study presents a performance analysis of CapsNets on standard images and their augmented variants for binary and multi-class classification. CapsNetCovid was trained and evaluated on two COVID-19 datasets of CT images and X-ray images. It was also evaluated on eight augmented datasets. The results show that the proposed model achieved classification accuracy, precision, sensitivity, and F1-score of 99.929%, 99.887%, 100%, and 99.319%, respectively, for the CT images. It also achieved a classification accuracy, precision, sensitivity, and F1-score of 94.721%, 93.864%, 92.947%, and 93.386%, respectively, for the X-ray images. This study presents a comparative analysis between CapsNetCovid, CNN, DenseNet121, and ResNet50 in terms of their ability to correctly identify randomly transformed and rotated CT and X-ray images without the use of data augmentation techniques. The analysis shows that CapsNetCovid outperforms CNN, DenseNet121, and ResNet50 when trained and evaluated on CT and X-ray images without data augmentation. We hope that this research will aid in improving decision making and diagnostic accuracy of medical professionals when diagnosing COVID-19.

Study on basic and clinical application of Shufeng Jiedu Capsule in treating respiratory tract infection.

Shufeng Jiedu Capsule (SFJDC), composed of eight herbs, is a big brand traditional Chinese medicine (TCM) for the treatment of different respiratory tract infectious diseases with good clinical efficacy and few side effects. It is clinically applied to acute upper respiratory tract infection(URI), influenza, acute exacerbation of chronic obstructive pulmonary disease (AECOPD), community-acquired pneumonia(CAP) and other diseases, due to its antibacterial, antiviral, anti-inflammatory, immunoregulatory and antipyretic activities. In particular, it has shown good clinical effects for COVID-19, and was included in the fourth to tenth editions of the 'Diagnosis and Treatment Protocol for COVID-19 (Trial)' by the National Health Commission. In recent years, studies on the secondary development which focus on the basic and clinical application of SFJDC have been widely reported. In this paper, chemical components, pharmacodynamic material basis, mechanisms, compatibility rule and clinical application were systematically summarized, in order to provide theoretical and experimental basis for further research and clinical application of SFJDC.

The Effect of Tang Forte (Royal Jelly) Capsule on Hypoglycemia and Clinical Course in COVID-19 Patients Under Corticosteroid Therapy.

Corticosteroids improve the complications of Covid-19 but may cause some side effects such as hyperglycemia. Royal jelly is one of the bee products that exert anti-inflammatory, insulin-like, and hypoglycemic activities. The present study was conducted to investigate the effect of royal jelly capsules on blood sugar and the clinical course of Covid-19 in the patients receiving corticosteroid therapy. In this clinical trial, 72 Covid-19 patients with positive reverse transcription polymerase chain reaction (RT-PCR) test and pulmonary involvement hospitalized in Shahrekord Hajar Hospital were enrolled and randomized into two groups: treatment (receiving corticosteroids and Royal Jelly 1000 mg capsules daily for 7 days) and placebo (given corticosteroids and placebo). Laboratory tests, blood sugar, and clinical courses were determined and compared. Data was analyzed using SPSS version 16. On day 7 after the onset of the intervention, the dosage and frequency of insulin, FBS level, and required corticosteroid showed a decrease in both groups but the inter-group difference was not significant (P > .05). As well, the Spo2 level indicated a non-significant increase and hospital stay length indicated a non-significant decrease in the intervention group (P > .05). Among the symptoms, only headache, cough, and dyspnea indicated an improvement in the intervention group (P < .05). Overall, the results indicated the short-term consumption of royal jelly could not significantly improve blood sugar and the clinical course of Covid-19; however, it could significantly improve headache, cough, and dyspnea in the patients.

COVID-19 associated acute demyelination masquerading as stroke: a case report

BackgroundDuring the recent outbreak of COVID-19, various atypical extrapulmonary manifestations are being seen, including neurological ones. Reported cases mainly include encephalopathy, myelitis, and cranial nerve involvement. This case describes uncommon neuroradiological finding in the context of COVID-19.Case presentationWe report an atypical case of COVID-19 presenting with stroke-like episode, with MRI brain showing isolated bilateral posterior internal capsule involvement. This has rarely been reported in literature.ConclusionAs the numbers of COVID-19 cases are increasing, such atypical presentations should be kept in mind.

Initial Stage Identification of COVID-19 Using Capsule Networks

Coronavirus is a pandemic that has kept us in great grief for the past few months. These days have created a devastating effect all through the world. As coronavirus has lot of similarities with other lung diseases, it becomes a challenging task for medical practitioners to identify the virus. A fast and robust system to identify the disease has been the need of the hour. In this chapter, we have used convolutional CapsNet for detecting COVID-19 disease using chest X-ray images. This design aims at obtaining fast and accurate diagnostic results. The proposed technique with less trainable parameters, COVID-CAPS, produced an accuracy of 87.5%, a sensitivity of 90%, a specificity of 95.8%, and an area under the curve (AUC) of 0.97. The main advantage of using CapsNet is that it can capture affine transformation in data that is a common scenario while dealing with real-world X-ray images. The CapsNet model is trained with normal data and tested with affine transformed data. The accuracy level obtained in the proposed method is comparatively much better along with having less learnable parameters and computational speed as compared to standard architectures such as ResNet, MobileNet, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

New Coronavirus 2 (SARS-CoV-2) Detection Method from Human Nucleic Acid Sequences Using Capsule Networks

The new coronavirus SARS-CoV-2 is an infectious virus with a long incubation period, which was first detected in Wuhan, China, spread all over the world, seriously threatening human life. Therefore, accurate and rapid detection of SARS-CoV-2 is very important for controlling the epidemic and preventing its further spread. Currently, nucleic acid detection makes an important contribution to the prevention and control of SARS-CoV-2. In this study, a new and highly sensitive nucleic acid detection method for SARS-CoV-2 has been proposed. The nucleic acid sequences were digitized by Entropy-based mapping technique. Then, the digitized these sequences were divided into 100-unit sections using the sliding window method and given as input to Capsule Networks.10988 segments (5494 SARS-CoV-2, 5494 normal) are classified with capsule nets. With the proposed method, an accuracy performance of 100% was achieved by using capsule networks to identify SARS-CoV-2 from nucleic acid sequences. The results show that the proposed method successfully identifies SARS-CoV-2 from nucleic acid sequences © 2023, Brazilian Archives of Biology and Technology. All rights reserved

DCACorrCapsNet: A deep channel‐attention correlative capsule network for COVID‐19 detection based on multi‐source medical images

The raging trend of COVID‐19 in the world has become more and more serious since 2019, causing large‐scale human deaths and affecting production and life. Generally speaking, the methods of detecting COVID‐19 mainly include the evaluation of human disease characterization, clinical examination and medical imaging. Among them, CT and X‐ray screening is conducive to doctors and patients' families to observe and diagnose the severity and development of the COVID‐19 more intuitively. Manual diagnosis of medical images leads to low the efficiency, and long‐term tired gaze will decline the diagnosis accuracy. Therefore, a fully automated method is needed to assist processing and analysing medical images. Deep learning methods can rapidly help differentiate COVID‐19 from other pneumonia‐related diseases or healthy subjects. However, due to the limited labelled images and the monotony of models and data, the learning results are biased, resulting in inaccurate auxiliary diagnosis. To address these issues, a hybrid model: deep channel‐attention correlative capsule network, for channel‐attention based spatial feature extraction, correlative feature extraction, and fused feature classification is proposed. Experiments are validated on X‐ray and CT image datasets, and the results outperform a large number of existing state‐of‐the‐art studies. [ABSTRACT FROM AUTHOR] Copyright of IET Image Processing (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata.

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.

Deep-CoV: An integrated deep learning model to detect COVID-19 using chest X-ray and CT images

The COVID-19 virus has fatal effect on lung function and due to its rapidity the early detection is necessary at the moment. The radiographic images have already been used by the researchers for the early diagnosis of COVID-19. Though several existing research exhibited very good performance with either x-ray or computer tomography (CT) images, to the best of our knowledge no such work has reported the assembled performance of both x-ray and CT images. Thus increase in accuracy with higher scalability is the main concern of the recent research. In this article, an integrated deep learning model has been developed for detection of COVID-19 at an early stage using both chest x-ray and CT images. The lack of publicly available data about COVID-19 disease motivates the authors to combine three benchmark datasets into a single dataset of large size. The proposed model has applied various transfer learning techniques for feature extraction and to find out the best suite. Finally the capsule network is used to categorize the sub-dataset into COVID positive and normal patients. The experimental results show that, the best performance exhibits by the ResNet50 with capsule network as an extractor-classifier pair with the combined dataset, which is composed of 575 numbers of x-ray images and 930 numbers of CT images. The proposed model achieves accuracy of 98.2% and 97.8% with x-ray and CT images, respectively, and an average of 98%. © 2023 Wiley Periodicals LLC.

DPDH-CapNet: A Novel Lightweight Capsule Network with Non-routing for COVID-19 Diagnosis Using X-ray Images.

COVID-19 has claimed millions of lives since its outbreak in December 2019, and the damage continues, so it is urgent to develop new technologies to aid its diagnosis. However, the state-of-the-art deep learning methods often rely on large-scale labeled data, limiting their clinical application in COVID-19 identification. Recently, capsule networks have achieved highly competitive performance for COVID-19 detection, but they require expensive routing computation or traditional matrix multiplication to deal with the capsule dimensional entanglement. A more lightweight capsule network is developed to effectively address these problems, namely DPDH-CapNet, which aims to enhance the technology of automated diagnosis for COVID-19 chest X-ray images. It adopts depthwise convolution (D), point convolution (P), and dilated convolution (D) to construct a new feature extractor, thus successfully capturing the local and global dependencies of COVID-19 pathological features. Simultaneously, it constructs the classification layer by homogeneous (H) vector capsules with an adaptive, non-iterative, and non-routing mechanism. We conduct experiments on two publicly available combined datasets, including normal, pneumonia, and COVID-19 images. With a limited number of samples, the parameters of the proposed model are reduced by 9x compared to the state-of-the-art capsule network. Moreover, our model has faster convergence speed and better generalization, and its accuracy, precision, recall, and F-measure are improved to 97.99%, 98.05%, 98.02%, and 98.03%, respectively. In addition, experimental results demonstrate that, contrary to the transfer learning method, the proposed model does not require pre-training and a large number of training samples.

Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic's impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology's most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general "fear of the unknown in AI" by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

Effectiveness of Xiaoyao capsule on sleep disorders and mood disturbance in patients in recovery from coronavirus disease 2019: a randomized controlled trial.

OBJECTIVE: To study the efficacy of Xiaoyao capsule in improving the clinical symptoms of sleep and mood disorders during recovery from coronavirus disease 2019 (COVID-19). METHODS: The study cohort comprised 200 patients with sleep and mood disorders during recovery from COVID-19. Patients were randomized into the control group and the experimental group in a 1:1 ratio by blocked randomization. The patients received either Xiaoyao capsule (experimental group) or a placebo Xiaoyao capsule (control group) for 2 weeks. The improvements in the Traditional Chinese Medicine (TCM) syndrome scales, total effective rates, and disappearance rates of irritability, anxiety, and poor sleep were compared between the two groups. RESULTS: The TCM syndrome pattern scales, total effective rates, and disappearance rates of irritability, anxiety, and poor sleep did not significantly differ between the experimental group versus the control group in the full analysis set and the per protocol set after 1 and 2 weeks of treatment ( > 0.05). CONCLUSIONS: Xiaoyao capsule do not significantly improve the clinical symptoms of sleep and mood disorders in patients in recovery from COVID-19.