Literature DB >> 1299632

Application of computer-aided image interpretation to the diagnosis of periapical bone lesions.

A Mol1, P F van der Stelt.   

Abstract

An image analysis system was developed for the computer-aided diagnosis of periapical bone lesions in dental radiographs. The system was designed to (1) identify the periapical region, (2) determine the presence of a periapical lesion and (3) estimate the size of the lesion in cases when a lesion had been found. To initiate the procedure, an observer indicates an arbitrary point on the root in a digitized radiograph. From this initial point, the location of the radiographic projection of the apex of the root is automatically computed. Next, the trabecular bone pattern is detected through texture analysis. A local absence of the trabecular bone pattern in the periapical region is marked as a periapical bone lesion. When a lesion has been identified, its size is estimated based on local edge properties. Observer interaction is only allowed to adjust the result of the apex localization procedure if the apex has not correctly been localized. In an experiment with randomly selected radiographs of 111 mandibular roots, the performance of the system was tested against the consensual diagnosis of four expert observers. The sensitivity of the system to identify a lesion was 83.3%, the specificity 75.6% and the diagnostic accuracy 80.2%. The correlation between the size of the lesions as estimated by the system and by the observers was 0.67 (P < 0.01). When the procedure was repeated, the percentage of correctly reproduced lesion sizes by the system was 92.8%. The determination of the presence of a lesion was reproducible in 98.2% of all the cases.

Entities:  

Mesh:

Year:  1992        PMID: 1299632     DOI: 10.1259/dmfr.21.4.1299632

Source DB:  PubMed          Journal:  Dentomaxillofac Radiol        ISSN: 0250-832X            Impact factor:   2.419


  7 in total

Review 1.  Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology.

Authors:  Kuo Feng Hung; Qi Yong H Ai; Yiu Yan Leung; Andy Wai Kan Yeung
Journal:  Clin Oral Investig       Date:  2022-04-19       Impact factor: 3.606

2.  The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.

Authors:  Kuofeng Hung; Carla Montalvao; Ray Tanaka; Taisuke Kawai; Michael M Bornstein
Journal:  Dentomaxillofac Radiol       Date:  2019-08-14       Impact factor: 2.419

3.  Current applications and development of artificial intelligence for digital dental radiography.

Authors:  Ramadhan Hardani Putra; Chiaki Doi; Nobuhiro Yoda; Eha Renwi Astuti; Keiichi Sasaki
Journal:  Dentomaxillofac Radiol       Date:  2021-07-08       Impact factor: 2.419

4.  Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey.

Authors:  Ruben Pauwels; Yumi Chokyu Del Rey
Journal:  Dentomaxillofac Radiol       Date:  2021-01-12       Impact factor: 3.525

Review 5.  Artificial Intelligence in Dentistry: Past, Present, and Future.

Authors:  Paridhi Agrawal; Pradnya Nikhade
Journal:  Cureus       Date:  2022-07-28

6.  Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?

Authors:  Maira Moran; Marcelo Faria; Gilson Giraldi; Luciana Bastos; Aura Conci
Journal:  Sensors (Basel)       Date:  2021-03-12       Impact factor: 3.576

7.  Artificial Intelligence Application in Assessment of Panoramic Radiographs.

Authors:  Łukasz Zadrożny; Piotr Regulski; Katarzyna Brus-Sawczuk; Marta Czajkowska; Laszlo Parkanyi; Scott Ganz; Eitan Mijiritsky
Journal:  Diagnostics (Basel)       Date:  2022-01-17
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.