Publications by authors named "R P Hsung"
Objectives: Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers.
View Article and Find Full Text PDFObjective: To evaluate and compare the accuracy of detection methods for the diagnosis of secondary caries around direct restorations in posterior teeth.
Data: Accuracy parameters including sensitivity, specificity, diagnostic odds ratio (DOR), area under curve (AUC), and partial AUC (pAUC) are generated from studies assessing the accuracy of detection methods for secondary caries.
Sources: Publications from PubMed, Web of Science, Scopus, Medline, EMBASE and Cochrane Library databases.
Objectives: To evaluate the validity and reliability of smartphone-generated three-dimensional (3D) facial images for routine evaluation of the oronasal region of patients with cleft by comparing their accuracy to that of direct anthropometry (DA) and 3dMD.
Materials And Methods: Eighteen soft-tissue facial landmarks were manually labelled on each of the 17 (9 males and 8 females; mean age 23.3 ± 5.
Purpose: With the increasing use of artificial intelligence (AI) in dentistry, it is feasible to self-monitor oral health using Oral Health AI Advisors (OHAI Advisors). This technological advancement offers the potential for early detection of oral diseases and facilitates early prevention. This systematic review aimed to evaluate the effectiveness of OHAI Advisors as a tool in preventive dentistry for the general population.
View Article and Find Full Text PDFArticle Synopsis
- Photovoltaic (PV) panels are a key area of research in green energy, particularly for estimating their parameters for health monitoring and fault diagnosis.
- A new method using an artificial neural network (ANN) and particle swarm optimization (PSO) has been developed to estimate these parameters more accurately by analyzing dynamic current and voltage responses.
- Simulations demonstrate that this new approach can improve estimation accuracy by up to 3.5% and increases the speed of convergence compared to traditional methods.