In vitro evaluation of the accuracy of three electronic apex locators.

J Endod

Department of Dental Pathology and Therapeutics, School of Dentistry, University of Granada, Granada, Spain.

Published: April 2004

The accuracy of three electronic apex locators (EALs) (Justy II, Root ZX, and Neosono Ultima EZ) is evaluated, together with the concordance of the measurements obtained by two different operators. Twenty single-root human teeth were used, sectioning the crown to gain access to the root canal. A first operator (A) determined the reference (or control) length (corresponding to the actual length) for each tooth, after which all teeth were measured individually and independently by the other two operators (B and C). The results obtained with each EAL and by each operator were in turn compared with the corresponding control length. The statistical analysis of the results showed EAL reliability in detecting the apex to vary from 80% to 85% and 85% to 90% (depending on the operator) for the Justy II and Neosono systems, respectively, whereas reliability was found to be 85% for the Root ZX device. These results, combined with a high interobserver concordance, suggest electronic root canal measurement to be an objective and acceptably reproducible technique.

Download full-text PDF

Source
http://dx.doi.org/10.1097/00004770-200404000-00012DOI Listing

Publication Analysis

Top Keywords

accuracy three
8
three electronic
8
electronic apex
8
apex locators
8
root canal
8
control length
8
vitro evaluation
4
evaluation accuracy
4
locators accuracy
4
locators eals
4

Similar Publications

Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space of real-world scenarios. Artificial intelligence (AI) has revolutionized retrosynthesis prediction in recent decades, significantly increasing the accuracy and diversity of predictions for target compounds.

View Article and Find Full Text PDF

Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.

Trop Anim Health Prod

January 2025

Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.

Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.

View Article and Find Full Text PDF

Atlantoaxial dislocation (AAD) is a serious condition in which the first two cervical vertebrae lose their anatomical position and stability. This may lead to neurological complications, including death. The treatment of AAD remains controversial, and posterior instrumentation with pedicle screw placement is one of the commonly used methods.

View Article and Find Full Text PDF

Background And Aim: Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.

View Article and Find Full Text PDF

Objective: To assess and compare the diagnostic accuracy of radiologist, MR findings, and radiomics-clinical models in the diagnosis of placental implantation disorders.

Methods: Retrospective collection of MR images from patients suspected of having placenta accreta spectrum (PAS) was conducted across three institutions: Institution I (n = 505), Institution II (n = 67), and Institution III (n = 58). Data from Institution I were utilized to form a training set, while data from Institutions II and III served as an external test set.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!