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BackgroundAccurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images. ObjectiveThe aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care. MethodsA systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19–related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. ResultsOf the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non–neural network–based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting. ConclusionsMachine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.

Background: Radiographs, adjunct to clinical examination are always valuable complementary methods for dental caries detection. Recently, progressing in digital imaging system provides possibility of software designing for automatically dental caries detection. Objectives: The aim of this study was to develop and assess the function of diagnostic computer software designed for evaluation of approximal caries in posterior teeth. This software should be able to indicate the depth and location of caries on digital radiographic images. Materials and Methods: Digital radiographs were obtained of 93 teeth including 183 proximal surfaces. These images were used as a database for designing the software and training the software designer. In the design phase, considering the summed density of pixels in rows and columns of the images, the teeth were separated from each other and the unnecessary regions; for example, the root area in the alveolar bone was eliminated. Therefore, based on summed intensities, each image was segmented such that each segment contained only one tooth. Subsequently, based on the fuzzy logic, a well-known data-clustering algorithm named fuzzy c-means (FCM) was applied to the images to cluster or segment each tooth. This algorithm is referred to as a soft clustering method, which assigns data elements to one or more clusters with a specific membership function. Using the extracted clusters, the tooth border was determined and assessed for cavity. The results of histological analysis were used as the gold standard for comparison with the results obtained from the software. Depth of caries was measured, and finally Intraclass Correlation Coefficient (ICC) and Bland-Altman plot were used to show the agreement between the methods. Results: The software diagnosed 60% of enamel caries. The ICC (for detection of enamel caries) between the computer software and histological analysis results was determined as 0.609 (95% confidence interval [CI] = 0.159-0.849) (P = 0.006). Also, the computer program diagnosed 97% of dentin caries and the ICC between the software and histological analysis results for dentin caries was determined as 0.937 (95% CI=0.906-0.958) (P < 0.001). Bland-Altman plot showed an acceptable agreement for measuring the depth of caries in enamel and dentin. Conclusions: The designed software was able to detect a significant number of dentin caries and acceptable measuring of the depth of carious lesions in enamel and dentin. However, the software had limited ability in detecting enamel lesions. [ABSTRACT FROM AUTHOR]

Background Metabolic syndrome (MetS), a public health problem worldwide, represents a high-risk condition for cardiovascular disease and diabetes. The reserve of subcutaneous fat in the upper torso is an important factor in the development of MetS and its risk factors. As neck circumference (NC) is a simple and reliable index that indicates upper-body subcutaneous fat accumulation, this study was conducted to investigate the association between NC with MetS and its components in a large population-based sample of Iranian adults. Methods The present cross-sectional study was conducted among 2094 individuals aged 20-70 years from Yazd Health Study (YaHS). MetS was defined based on the National Cholesterol Education Program Adult Treatment Panel III (ATP III) criteria. NC more than 40.25 cm for men and more than 35.75 cm for women was considered a high-risk NC. Logistic regression analysis was applied to obtain the associations of NC with MetS and its component. Results After adjusting for age, BMI, energy intake, physical activity, and smoking in those whose NC was higher than normal, the risk of the MetS (odds ratio {OR}: 2.32; 95%CI: 1.55-3.46, P<0.001 in men and OR: 2.56; 95%CI: 1.76-3.74, P<0.001 in women), abdominal obesity (OR: 4.39; 95%CI: 2.67-7.23, P<0.001 in men and OR: 1.92; 95%CI: 1.27-2.90, P=0.002 in women), high blood pressure (OR: 1.54; 95%CI: 1.07-2.21, P=0.02 in men and OR: 1.51; 95%CI: 1.06-2.14, P=0.02 in women), low high-density lipoprotein cholesterol (HDL-C) (OR: 1.47; 95%CI: 1.01-2.15, P=0.04 in men and OR: 1.69; 95%CI: 1.23-2.32, P=0.001 in women), and hypertriglyceridemia (OR: 1.41; 95%CI: 1.03-1.99, P=0.04 in men and OR: 1.68; 95%CI: 1.17-2.41, P=0.005 in women) were higher. There was no significant difference in the risk of hyperglycemia between the two NC groups in both sexes. The Pearson's correlation coefficients of NC with waist circumference, hip circumference, body mass index, and waist-to-height ratio were 0.52, 0.43, 0.41, and 0.31, respectively (P<0.001). Moreover, NC had a considerable correlation with serum triglyceride, high-density lipoprotein, systolic blood pressure, and diastolic blood pressure (0.27, -0.30, 0.29, 0.25, P<0.001), respectively. Conclusion Increased NC was significantly associated with higher odds of MetS and its components. Since NC measurement is simple, inexpensive, reliable, and less invasive, it can be used as a complementary tool in the screening and diagnosis of MetS and its risk factors in clinical and community programs especially in developing countries., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Mohseni-Takalloo et al.)