Machine learning approaches in Covid-19 severity risk prediction in Morocco.

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

Which method to measure dentomaxillary discrepancy?

Tooth-size assessment is a useful indicator of dental crowding which constitutes one of the main parameters in tooth-size arch-length discrepancy (TSALD). Systematic TSALD evaluation is imperative in all patients consulting for dentofacial orthopedic treatment. The present study aims to compare four measurement techniques for calibrating tooth size. It was performed on casts taken from 30 Moroccan patients consulting at the Dentofacial Orthopedics Department of the Rabat School of Dentistry. Four measurement methods were used to calculate the largest mesiodistal diameter of mandibular teeth mesial to the first molars. Statistical analysis showed that the methods using a digital caliper, the analog vernier caliper and the computerized technique are reliable, accurate, and interchangeable. In contrast, the analog caliper with no vernier is less accurate.

[The impact of extractions on profile esthetics: a statistical study]. / Impact des extractions sur le profil esthétique : étude statistique.

This study assesses and compares the impact of orthodontic treatment with premolar extractions on the profile of the soft tissues in hyper- and normodivergent patients from our Moroccan population which is characterized by a high rate of hyperdivergency. This retrospective study was performed on a sample of 60 adults (30 hyperdivergents and 30 normodivergents). Pre-treatment and end-of-treatment headfilms were used to take sagittal and vertical measurements of soft tissue using the Frapier method which takes the Björk-Nielsen implant line as superimposition landmark. The results of this study show marked improvements on the soft tissues, even though the mean differences are occasionally non-or only slightly significant. In contrast, the vertical dimension has little incidence on the results obtained. The response to treatment of hyperdivergent patients, even when displaying a greater initial esthetic deficit, is similar to that of normodivergents. In certain areas, the improvement was occasionally greater, notably at the soft-tissue pogonion, the mento-labial angle and the naso-labial angle.