A family-centered orthodontic screening approach using a machine learning-based mobile application.

Background/purpose: Skeletal orthodontic deformities can have functional and aesthetic consequences, making early detection critical. This study aimed to address the issue of parents bringing their children for routine orthodontic checkups after the ideal treatment age has passed. To address this, we developed a mobile application that uses machine-learning to make a preliminary diagnosis of skeletal malocclusion using just one photograph. Materials and methods: A retrospective study was conducted on 524 pre-pubertal children, aged between 5 and 12 years, to evaluate the accuracy of the machine learning based mobile application. The application detects multiple points in photographs taken from the mobile camera and generates a signal indicating the diagnosis of skeletal malocclusion. Results: The final accuracy of the Class III vs not Class III model deployed to the mobile application was above 81%, indicating its ability to accurately identify skeletal malocclusion. On a separate validation dataset of 145 patients diagnosed by 5 different clinicians, the accuracy of Class II vs Class I model was 69%; And pg 4, ln 61: as Class II vs Class I with 69% accuracy. Conclusion: The application provides parents with important information about the orthodontic problem, age of treatment, and various treatment options. This enables parents to seek further advice from an orthodontist at an earlier stage and make informed decisions. However, the diagnosis should still be confirmed by an orthodontist. This approach has the potential to improve access to orthodontic care, especially in underserved communities.

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.

Two-dimensional and three-dimensional echocardiographic assessment of aorticroot diameter in patients with coronary ectasia

Background: Coronary artery ectasia (CAE) is a well-recognized but relatively uncommon finding encountered during diagnostic coronary angiography. It is commonly defined as in appropriate dilation of the coronary arteries exceeding the largest diameter of an adjacent normal vessel more than 1.5-fold. CAE is not an isolated and benign disease but a reflection of a generalized vascular media defect. Objective: The aim of this study was to compare 2D-TTEand 3D-TTE measurements of the aortic root diameter in patients with coronary artery ectasia to assess the presence of aortic root dilatation. Patients and methods: This prospective observational study included 50 consecutive patients came to the Department of Cardiology, Al-Azhar University Hospital, New Damietta for coronary angiography. The study was carried out from November 2017 until December 2018. Injection aortography was used as a gold standard and to assess the presence of ascending aorta dilatation in those patients. Results: The present study shows that there was a good correlation between 3D-TTE and aortography at the levels of aortic annulus, sinuses of Valsalva, sinotubular junction (r =0.98,0.95,0.98) but a rough correlation between 2D-TTE and aortography at these levels (r =0.49,0.48,0.46). The present study shows that there was increase prevalence of aortic root dilatation 13 patients (26%) and ascending aorta dilatation 9patients(18%) in patients with CAE. Conclusions: Accuracy of aortic root measurement by 3DTTE was superior to that by 2DTTE, because the values by 2DTTE were underestimated compared to those measured by 3DTTE and aortography. Increase prevalence of aortic root dilatation and ascending aorta dilatation in patients with coronary artery ectasia. Dilated Ascending aorta was associated with a higher prevalence of aortic root dilatation