A personalized periodontitis risk based on nonimage electronic dental records by machine learning.

Objective: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).

Methods: By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing.

The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.

Background: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data.

A Systematic Umbrella Review of the Effects of Teledentistry on Costs and Oral-Health Outcomes.

Teledentistry offers possibilities for improving efficiency and quality of care and supporting cost-effective healthcare systems. This umbrella review aims to synthesize existing systematic reviews on teledentistry and provide a summary of evidence of its clinical- and cost-effectiveness. A comprehensive search strategy involving various teledentistry-related terms, across seven databases, was conducted.

The radiographer's perspective on X-ray examinations in potentially pregnant patients; results of a focus group study among Dutch radiographers.

Introduction: The Netherlands does not have a national guideline for performing radiographic examinations on pregnant patients. Radiographic examination is a generic term for all examinations performed using ionizing radiation, including but not limited to radiographs, fluoroscopy and computed tomography. A pilot study amongst radiographers (Medical Radiation Technologists (MRTs)) showed that standardized practice of radiographic examinations on pregnant women is not evident between Radiology departments and that there is a need for a national guideline as the varying practice methods may lead to confusion and uncertainty amongst both patients and MRTs.