Literature DB >> 11125191

Computer-automated caries detection in digital bitewings: consistency of a program and its influence on observer agreement.

A Wenzel1.   

Abstract

The aim of this study was to evaluate a decision-support, caries detection program and its influence on observer agreement in caries diagnosis. 130 patients were examined by digital bitewing radiography (RVG XL sensor, Trophy Radiologie Inc.). Fifty-four approximal surfaces (27 in premolars and 27 in molars) were selected by the author: 24 surfaces (9 in molars and 15 in premolars) scored as sound, 16 surfaces (9 in molars and 7 in premolars) scored as carious in enamel, and 14 surfaces (9 in molars and 5 in premolars) scored as carious in dentine. The Logicon Caries Detector (LCD) program (Logicon Inc., USA) was assessed by repeating the automated analysis ten times for each surface. The two most varying outcomes for lesion probability (Lp(min) and Lp(max)) were saved. Five observers scored the 54 surfaces independently as sound, caries in enamel or caries in dentine before and after the use of LCD. In more than one third of all surfaces the program indicated different lesion probability, from sound at Lp(min) to the presence of a carious lesion at Lp(max). The 5 observers changed their caries score after the use of LCD in a total of 31 surfaces (only 2 of these were in the same surface). Mean kappa value for inter-observer agreement for caries scores before the use of LCD was 0.47 (range 0. 39-0.61) and after LCD 0.48 (range 0.37-0.69). It was concluded that the automated caries detection program was not very consistent and provided different opinions on the caries status in a surface. Inter-observer agreement in caries diagnosis did not improve using the program.

Entities:  

Mesh:

Year:  2001        PMID: 11125191     DOI: 10.1159/000047425

Source DB:  PubMed          Journal:  Caries Res        ISSN: 0008-6568            Impact factor:   4.056


  6 in total

1.  A novel classification system for assessment of approximal caries lesion progression in bitewing radiographs.

Authors:  Anna Senneby; Margareta Elfvin; Christina Stebring-Franzon; Madeleine Rohlin
Journal:  Dentomaxillofac Radiol       Date:  2016-04-04       Impact factor: 2.419

2.  Effect of computer assistance on observer performance of approximal caries diagnosis using intraoral digital radiography.

Authors:  Kazuyuki Araki; Yukiko Matsuda; Kenji Seki; Tomohiro Okano
Journal:  Clin Oral Investig       Date:  2009-06-26       Impact factor: 3.573

Review 3.  Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems.

Authors:  Ann Wenzel
Journal:  Dentomaxillofac Radiol       Date:  2021-03-04       Impact factor: 3.525

4.  Assessment of diagnostic accuracy of a direct digital radiographic-CMOS image with four types of filtered images for the detection of occlusal caries.

Authors:  Rohit Kumar Sahu; Jagadish P Rajguru; Naina Pattnaiak; Debajyoti Bardhan; Bikash Nayak
Journal:  J Family Med Prim Care       Date:  2020-01-28

5.  Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs.

Authors:  Michael G Endres; Florian Hillen; Marios Salloumis; Ahmad R Sedaghat; Stefan M Niehues; Olivia Quatela; Henning Hanken; Ralf Smeets; Benedicta Beck-Broichsitter; Carsten Rendenbach; Karim Lakhani; Max Heiland; Robert A Gaudin
Journal:  Diagnostics (Basel)       Date:  2020-06-24

6.  Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth.

Authors:  Solmaz Valizadeh; Mostafa Goodini; Sara Ehsani; Hadis Mohseni; Fateme Azimi; Hooman Bakhshandeh
Journal:  Iran J Radiol       Date:  2015-08-05       Impact factor: 0.212

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.