Artificial intelligence application in endodontics: A narrative review.

Purpose: This review aimed to explore the scientific literature concerning the methodologies and applications of artificial intelligence (AI) in the field of endodontics. The findings may equip dentists with the necessary technical knowledge to understand the opportunities presented by AI.

Materials And Methods: Articles published between 1992 and 2023 were retrieved through an electronic search of Medline via the PubMed, Scopus, and Google Scholar databases.

Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study.

This study aimed to evaluate the performance of a deep learning-based segmentation model for predicting outcomes of non-surgical endodontic treatment. Preoperative and 3-year postoperative periapical radiographic images of each tooth from routine root canal treatments performed by endodontists from 2015 to 2021 were obtained retrospectively from Thammasat University hospital. Preoperative radiographic images of 1200 teeth with 3-year follow-up results (440 healed, 400 healing, and 360 disease) were collected.

Comparative Analysis of Silicone Mouth Swabs with Varying Hardness Levels for Optimal Plaque Removal in Elderly Oral Care.

Purpose: Silicone mouth swabs have emerged as a promising alternative to gauze, sponge brushes, and soft-bristled toothbrushes, offering a balance between gentle cleaning and effectiveness. The flexibility and softness of silicone make it a suitable material for safely cleaning the sensitive oral tissues of elderly patients. This study aims to determine the optimal hardness level of silicone that maximises cleaning effectiveness while minimising the risk of trauma to oral tissues.

Micro-computed tomography evaluation of minimally invasive root canal preparation in 3D-printed C-shaped canal.

This study aimed to evaluate and compare the shaping abilities and minimum dentin thickness of minimally invasive rotary instruments via micro-computed tomography. Twelve 3D-printed C-shaped canal models from a mandibular molar were divided into two groups, and root canals were prepared with either XP-endo Rise (XR) or TruNatomy (TN) systems. Pre- and post-preparation evaluations included canal volume, prepared area and minimum dentin thickness.

RISKOCA: A Smartphone-Based Digital Platform for Oral Cancer Self-Examination.

We pioneered a smartphone-based digital platform for oral cancer self-examination, namely RISKOCA. It enabled anyone to self-submit their own oral images to evaluate the potential risk of oral lesions. Integrative artificial intelligence (AI) could immediately report if the image might have a type of oral cancer as well as the precise locations of the lesions.

Precise Identification of Oral Cancer Lesions Using Artificial Intelligence.

Dentists, especially those who are not oral lesion specialists and live in rural areas, need an artificial intelligence (AI) system for accurately assisting them in screening for oral cancer that may appear in smartphone images. Not many literatures present a viable model that addresses the needs, especially in the context of oral lesion segmentation in smartphone images. This study demonstrates the use of a deep learning-based AI for simultaneously identifying types of oral cancer lesions as well as precisely outlining the boundary of the lesions in the images for the first time.

Enhancing Periodontal Treatment Through the Integration of Deep Learning-Based Detection with Bayesian Network Models.

This study incorporated deep learning for periodontal disease detection into a Bayesian network (BN) clinical decision support model for comprehensive periodontal care. BN structure and probabilities were based on clinical data and Faster R-CNN-detected radiographic images. Receiver operating characteristic curve analysis confirmed the model's high accuracy in treatment plan recommendations.

The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining.

Objective:  A 5-year survival rate is a predictor for the assessment of oral cancer prognosis. The purpose of this study is to analyze oral cancer data to discover and rank the prognostic factors associated with oral cancer 5-year survival using the association rule mining (ARM) technique.

Materials And Methods:  This study is a retrospective analysis of 897 oral cancer patients from a regional cancer center between 2011 and 2017.

Survival estimation of oral cancer using fuzzy deep learning.

Background: Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer.

Methods: Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019.

Deep learning in oral cancer- a systematic review.

Background: Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer.

Methods: This systematic review was conducted following the PRISMA guidelines.

Development of an Artificial Intelligence Teaching Assistant System for Undergraduate Nursing Students: A Field Testing Study.

Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this study was to develop, test usability of, and explore undergraduate nursing students' perceptions toward the Artificial Intelligence-Teaching Assistant System.

Deep Learning for Midfacial Fracture Detection in CT Images.

This study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object detection models were created using faster R-CNN and RetinaNet from 2,000 CT images. The best detection model, faster R-CNN, yielded an average precision of 0.

Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning.

Temporomandibular joint (TMJ) disorders have been misinterpreted by various normal TMJ features leading to treatment failure. This study assessed deep learning algorithms, DenseNet-121 and InceptionV3, for multi-class classification of TMJ normal variations and disorders in 1,710 panoramic radiographs. The overall accuracy of DenseNet-121 and InceptionV3 were 0.

The effect of 3D stereopsis and hand-tool alignment on learning effectiveness and skill transfer of a VR-based simulator for dental training.

Recent years have seen the proliferation of VR-based dental simulators using a wide variety of different VR configurations with varying degrees of realism. Important aspects distinguishing VR hardware configurations are 3D stereoscopic rendering and visual alignment of the user's hands with the virtual tools. New dental simulators are often evaluated without analysing the impact of these simulation aspects.

Maxillofacial fracture detection and classification in computed tomography images using convolutional neural network-based models.

The purpose of this study was to evaluate the performance of convolutional neural network-based models for the detection and classification of maxillofacial fractures in computed tomography (CT) maxillofacial bone window images. A total of 3407 CT images, 2407 of which contained maxillofacial fractures, were retrospectively obtained from the regional trauma center from 2016 to 2020. Multiclass image classification models were created by using DenseNet-169 and ResNet-152.

Clinical Decision Support System for Geriatric Dental Treatment Using a Bayesian Network and a Convolutional Neural Network.

Objectives: The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeutic plans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only the patient's oral health status obtained from an oral examination, but also other related factors such as underlying diseases, socioeconomic characteristics, and functional dependency.

Methods: A Bayesian network (BN) was used as a framework to construct a model of contributing factors and their causal relationships based on clinical knowledge and data.

The Effect of Morpholine on Composite-to-Composite Repair Strength Contaminated with Saliva.

The aim of this study was to specifically explore the effects of morpholine on chemical surface treatments of aged resin composites contaminated with saliva to new resin composite repair strength. One hundred and thirty five resin composite specimens were fabricated and thermocycled to replicate an aged resin composite. These aged resin composites were randomly separated into nine groups (n = 15) depending on the various surface contaminants and surface treatment techniques.

Forecasting COVID-19 cases using time series modeling and association rule mining.

Background: The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management.

Methods: The 3685 COVID-19 patients admitted at Thailand's first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM).

Results: The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R = 0.

Utilizing educational technology in enhancing undergraduate nursing students' engagement and motivation: A scoping review.

Background: Modern educational technology (Edtech) combines technological tools with educational theories. Over the years, Edtech has been adopted in nursing education to address student needs and expectations, institutional resources, community stakeholder expectations, and healthcare trends. However, regardless of the technologies used, keeping students engaged in learning is still challenging.

AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer.

Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classification and detection of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) in oral photographic images. A dataset comprising 980 oral photographic images was divided into 365 images of OSCC, 315 images of OPMDs and 300 images of non-pathological images.

Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs.

The aim of this study was to develop automated models for the identification and detection of mandibular fractures in panoramic radiographs using convolutional neural network (CNN) algorithms. A total of 1710 panoramic radiograph images from the years 2016 to 2020, including 855 images containing mandibular fractures, were obtained retrospectively from the regional trauma centre. CNN-based classification models, DenseNet-169 and ResNet-50, were fabricated to identify fractures in the radiographic images.

Analysis of Characteristics and Clinical Outcomes for Crisis Management during the Four Waves of the COVID-19 Pandemic.

This study aims to analyze the patient characteristics and factors related to clinical outcomes in the crisis management of the COVID-19 pandemic in a field hospital. We conducted retrospective analysis of patient clinical data from March 2020 to August 2021 at the first university-based field hospital in Thailand. Multivariable logistic regression models were used to evaluate the factors associated with the field hospital discharge destination.

Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images.

Oral potentially malignant disorders (OPMDs) are a group of conditions that can transform into oral cancer. The purpose of this study was to evaluate convolutional neural network (CNN) algorithms to classify and detect OPMDs in oral photographs. In this study, 600 oral photograph images were collected retrospectively and grouped into 300 images of OPMDs and 300 images of normal oral mucosa.

Automatic classification and detection of oral cancer in photographic images using deep learning algorithms.

Background: Oral cancer is a deadly disease among the most common malignant tumors worldwide, and it has become an increasingly important public health problem in developing and low-to-middle income countries. This study aims to use the convolutional neural network (CNN) deep learning algorithms to develop an automated classification and detection model for oral cancer screening.

Methods: The study included 700 clinical oral photographs, collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa.

Formative feedback generation in a VR-based dental surgical skill training simulator.

Fine motor skill is indispensable for a dentist. As in many other medical fields of study, the traditional surgical master-apprentice model is widely adopted in dental education. Recently, virtual reality (VR) simulators have been employed as supplementary components to the traditional skill-training curriculum, and numerous dental VR systems have been developed academically and commercially.