Tooth Problems Knowledge Based System

Abstract: background: Dental and oral health is an essential part of your overall health and well-being. Poor oral hygiene can lead to dental cavities and gum disease, and has also been linked to heart disease, cancer, and diabetes. Maintaining healthy teeth and gums is a lifelong commitment. The earlier you learn proper oral hygiene habits — such as brushing, flossing, and limiting your sugar intake — the easier it’ll be to avoid costly dental procedures and long-term health issues. (Healthline, n.d.) Objectives The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment by presenting suggestions on Tooth Problems to the user by asking about symptoms

Classification of A few Fruits Using Deep Learning

Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether a certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach

Frequency of Abnormalities Detected by Point-of-Care Lung Ultrasound in Symptomatic COVID-19 Patients: Systematic Review and Meta-Analysis

The COVID-19 pandemic has resulted in significant morbidity, mortality, and strained healthcare systems worldwide. Thus, a search for modalities that can expedite and improve the diagnosis and management of this entity is underway. Recent data suggested the utility of lung ultrasound (LUS) in the diagnosis of COVID-19 by detecting an interstitial pattern (B-pattern). Hence, we aimed to pool the proportion of various reported lung abnormalities detected by LUS in symptomatic COVID-19 patients. We conducted a systematic review (PubMed, MEDLINE, and EMBASE until April 25, 2020) and a proportion meta-analysis. We included seven studies examining the role of LUS in 122 COVID-19 patients. The pooled proportion (PP) of B-pattern detected by lung ultrasound (US) was 0.97 (95% CI: 0.94-1.00 0%, 4.6). The PP of finding pleural line abnormalities was 0.70 (95% CI: 0.13-1.00 96%, 103.9), of pleural thickening was 0.54 (95% 0.11-0.95 93%, 61.1), of subpleural or pulmonary consolidation was 0.39 (95% CI: 0.21-0.58 72%, 17.8), and of pleural effusion was 0.14 (95% CI: 0.00-0.37 93%, 27.3). Our meta-analysis revealed that almost all SARS-CoV-2-infected patients have abnormal lung US. The most common abnormality is interstitial involvement depicted as B-pattern. The finding from our review highlights the potential role of this modality in the triage, diagnosis, and follow-up of COVID-19 patients. A sizable diagnostic accuracy study comparing LUS, computed tomography scan, and COVID-19-specific tests is warranted to further test this finding and to delineate the diagnostic and prognostic yield of each of these modalities

A multi-country analysis of COVID-19 hospitalizations by vaccination status

Background: Individuals vaccinated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), when infected, can still develop disease that requires hospitalization. It remains unclear whether these patients differ from hospitalized unvaccinated patients with regard to presentation, coexisting comorbidities, and outcomes. Methods: Here, we use data from an international consortium to study this question and assess whether differences between these groups are context specific. Data from 83,163 hospitalized COVID-19 patients (34,843 vaccinated, 48,320 unvaccinated) from 38 countries were analyzed. Findings: While typical symptoms were more often reported in unvaccinated patients, comorbidities, including some associated with worse prognosis in previous studies, were more common in vaccinated patients. Considerable between-country variation in both in-hospital fatality risk and vaccinated-versus-unvaccinated difference in this outcome was observed. Conclusions: These findings will inform allocation of healthcare resources in future surges as well as design of longer-term international studies to characterize changes in clinical profile of hospitalized COVID-19 patients related to vaccination history. Funding: This work was made possible by the UK Foreign, Commonwealth and Development Office and Wellcome (215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z, and 220757/Z/20/Z); the Bill & Melinda Gates Foundation (OPP1209135); and the philanthropic support of the donors to the University of Oxford's COVID-19 Research Response Fund (0009109). Additional funders are listed in the "acknowledgments" section