AI-based radiodiagnosis using chest X-rays: A review.

Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.

A novel abnormality annotation database for COVID-19 affected frontal lung X-rays.

Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.

Seroprevalence of severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies among health-care workers prior and after 4-6 weeks of coronavirus disease vaccine administration at tertiary care center of Southwest Bihar, India

AIM: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. To control the spread, a mass vaccination program is initiated. Antibody titer after vaccination can be a better marker to monitor immunological response. MATERIALS AND METHODS: The study was carried out at the Department of Microbiology, Narayan Medical College and Hospital, Jamuhar Sasaram, southwest Bihar, considering the sample size, type, and collection. First, antibody was tested before vaccination and second antibody value after 28 days of the first dose of COVID vaccine among the health-care workers and housekeeping staff. RESULTS: A total of 251 subjects were administered with vaccination (Covishield) to check the immunoglobulin g (IgG) responses. The concentration of the SARS-CoV-2 IgG antibody in female patients tended to be higher than in male patients. CONCLUSION: There is a difference in antibody positivity among males and females. Most of the participants had IgG positivity, because of their profession, vaccination boosted percentage positivity in both males and females. Females have more IgG levels compared to males. Hence, recommend that separate guidelines can be made between males and females for vaccination dosages. [ FROM AUTHOR] Copyright of Indian Journal of Health Sciences & Biomedical Research is the property of Wolters Kluwer India Pvt Ltd 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 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 . (Copyright applies to all s.)

Real-world use of the Breathing Pattern Assessment Tool in assessment of breathlessness post-COVID-19.

INTRODUCTION: Breathing pattern disorders (BPDs) are a common cause of chronic breathlessness, including after acute respiratory illnesses such as COVID pneumonia. BPD is however underdiagnosed, partly as a result of difficulty in clinically assessing breathing pattern. The Breathing Pattern Assessment Tool (BPAT) has been validated for use in diagnosing BPD in patients with asthma but to date has not been validated in other diseases. METHODS: Patients undergoing face-to-face review in a post-COVID clinic were assessed by a respiratory physician and specialist respiratory physiotherapist. Assessment included a Dyspnoea-12 (D12) questionnaire to assess breathlessness, physiotherapist assessment of breathing pattern including manual assessment of respiratory motion, and BPAT assessment. The sensitivity and specificity of BPAT for diagnosis of BPD in post-COVID patients was assessed. RESULTS: BPAT had a sensitivity of 89.5% and specificity of 78.3% for diagnosing BPD in post-COVID breathlessness. Patients with a BPAT score above the diagnostic cut-off had higher levels of breathlessness than those with lower BPAT scores (D12 score mean average 19.4 vs 13.2). CONCLUSION: BPAT has high sensitivity and moderate specificity for BPD in patients with long COVID. This would support its use as a screening test in clinic, and as a diagnostic tool for large cohort studies.

Gis Based Spatial Analysis and Prediction of Covid-19 Cases

COVID-19 has come out to be a threat that has far-reaching repercussions in all parts of human existence;as a result, it is the most pressing concern facing countries around the world. This paper is centred on using a geographic information system to map COVID-19 instances across India, followed by COVID-19 case projections in various areas of India. A geographic information system (GIS) is a computer system that verifies, records, stores and displays data about places on the Earth’s surface, with India as the primary emphasis. Because the COVID-19 has had a distinct influence on different parts of India, the research we conducted provides a correct connection between past, current, and future instances in India employing prediction by using the SARIMA(Seasonal Autoregressive Integrated Moving Average) model to forecast time series. Python is used to implement the project. Several databases, including global databases like Natural Earth, UNEP Environmental Data Explorer, GRUMP, and national databases like Open Data Archive and ISRO’s Geo-Platform, are utilised to collect data for mapping and displaying instances across the country. These databases are combined to get the required output that is to be plotted and displayed. The prediction of coronavirus cases has also been done using the SARIMA model with an accuracy of 95.37percent.

Development, deployment and evaluation of digitally enabled, remote, supported rehabilitation for people with long COVID-19 (Living With COVID-19 Recovery): protocol for a mixed-methods study.

INTRODUCTION: Long COVID-19 is a distressing, disabling and heterogeneous syndrome often causing severe functional impairment. Predominant symptoms include fatigue, cognitive impairment ('brain fog'), breathlessness and anxiety or depression. These symptoms are amenable to rehabilitation delivered by skilled healthcare professionals, but COVID-19 has put severe strain on healthcare systems. This study aims to explore whether digitally enabled, remotely supported rehabilitation for people with long COVID-19 can enable healthcare systems to provide high quality care to large numbers of patients within the available resources. Specific objectives are to (1) develop and refine a digital health intervention (DHI) that supports patient assessment, monitoring and remote rehabilitation; (2) develop implementation models that support sustainable deployment at scale; (3) evaluate the impact of the DHI on recovery trajectories and (4) identify and mitigate health inequalities due to the digital divide. METHODS AND ANALYSIS: Mixed-methods, theoretically informed, single-arm prospective study, combining methods drawn from engineering/computer science with those from biomedicine. There are four work packages (WP), one for each objective. WP1 focuses on identifying user requirements and iteratively developing the intervention to meet them; WP2 combines qualitative data from users with learning from implementation science and normalisation process theory, to promote adoption, scale-up, spread and sustainability of the intervention; WP3 uses quantitative demographic, clinical and resource use data collected by the DHI to determine illness trajectories and how these are affected by use of the DHI; while WP4 focuses on identifying and mitigating health inequalities and overarches the other three WPs. ETHICS AND DISSEMINATION: Ethical approval obtained from East Midlands - Derby Research Ethics Committee (reference 288199). Our dissemination strategy targets three audiences: (1) Policy makers, Health service managers and clinicians responsible for delivering long COVID-19 services; (2) patients and the public; (3) academics. TRIAL REGISTRATION NUMBER: Research Registry number: researchregistry6173.

Evaluation of Knowledge, Attitude, Awareness, Fear, and Anxiety Levels in Pateints Visisting the Routine Dental Outpatient Department during COVID 19 Pandemic - A Cross-sectional Hospital-Based Observational Research.

BACKGROUND: Since the COVID 19 pandemic has hit worldwide wide being one of the biggest psychological menace that had an impact on all socioeconomic strata as well age group of society. Looking at the present scenario of confusion and anxiety a cross-sectional research was planned to see the level of fear and anxiety among a patient who is already having concern about his oral health and when he comes to a dental outpatient department (OPD) what are his main areas of concern and anxiety. METHODOLOGY: Cross-sectional study was planned in faculty of dental sciences, all the patients coming to dental OPD over a period of 3 months from November 2020 to March 2021 were asked to self-fill or fill with assistance a google form compromising of 10 multiple response questionnaire validated according to previous studies and surveys. Later on, the data was compiled and evaluated. RESULTS: Out of 511 Patients enrolled in the study after taking their online consent. 28.2% of population did not know about COVID 19. 67.4% were aware about the pandemic whereas 4.4% were confused and did not know about the complete scenario. 36.9% of population were not in stress and 62.4% were in stress and had anxiety regarding their visit for dental check up and treatment. 30.4% had no fear of catching the infection from dental procedure, 62.4% had fear and 7.2% of population was ignorant regarding the same. About 70.7% had quarantined themselves before coming for dental treatment. 62.4% were willing for dental treatment post-vaccination and 26% were not willing for treatment 11.6% were not sure. After applying statistical analysis, it was found that P < 0.05 and people coming to dental OPD were in lot of stress and anxiety regarding the dental procedures during the COVID pandemic. CONCLUSION: Since the global pandemic has caused major worry among the populations but still there are many who are not so anxious. Knowledge and awareness regarding the disease and vaccination have led to a wave of calmness in some, but still many people have been impacted and are in major dilemma whether they should get a dental treatment or should delay it ??

Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images.

With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network (COMiT-Net) which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions and semantic segmentation of COVID19 symptoms in CXRs taken from the ChestXray-14, CheXpert, and a consolidated COVID-19 dataset. These annotations will be released to the research community. Experiments performed with more than 2500 frontal CXR images show that at 90% specificity, the proposed COMiT-Net yields 96.80% sensitivity.

Post-COVID symptoms reported at asynchronous virtual review and stratified follow-up after COVID-19 pneumonia.

BACKGROUND: The COVID-19 pandemic has strained healthcare systems and how best to address post-COVID health needs is uncertain. Here we describe the post-COVID symptoms of 675 patients followed up using a virtual review pathway, stratified by severity of acute COVID infection. METHODS: COVID-19 survivors completed an online/telephone questionnaire of symptoms after 12+ weeks and a chest X-ray. Dependent on findings at virtual review, patients were provided information leaflets, attended for investigations and/or were reviewed face-to-face. Outcomes were compared between patients following high-risk and low-risk admissions for COVID pneumonia, and community referrals. RESULTS: Patients reviewed after hospitalisation for COVID pneumonia had a median of two ongoing physical health symptoms post-COVID. The most common was fatigue (50.3% of high-risk patients). Symptom burden did not vary significantly by severity of hospitalised COVID pneumonia but was highest in community referrals. Symptoms suggestive of depression, anxiety and post-traumatic stress disorder were common (depression occurred in 24.9% of high-risk patients). Asynchronous virtual review facilitated triage of patients at highest need of face-to-face review. CONCLUSION: Many patients continue to have a significant burden of post-COVID symptoms irrespective of severity of initial pneumonia. How best to assess and manage long COVID will be of major importance over the next few years.