Reliability of Artificial Intelligence in Lateral Cephalometric Analysis

Hesham , Nouran; Ashmawy , Mostafa; Samir , Sahar

Article Details

Reliability of Artificial Intelligence in Lateral Cephalometric Analysis

Authors:

Hesham , Nouran | Ashmawy , Mostafa | Samir , Sahar

Publisher:

Ain Shams University : Faculty of Dentistry

Main Topics:

Medicine

Journal:

Ain Shams Dental Journal 2024 Vol.33 Issue 1, pp.61-71

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

264463

Frequency:

Quarterly

pISSN:

11107642

Descriptors:

Keywords:

Abstract:

 Aim: The purpose of this study was to assess the reliability of lateral cephalometric analysis performed by an artificial intelligence-dependent software program. Materials and Methods: One Hundred and Eighty digital cephalometric radiographs acquired by Vatech PaX-i X-ray machine, were used in the study. The anatomical landmarks of both Steiner and McNamara analyses were manually traced using a thirdparty software AudaxCeph Empower, version 6.6.12.4731 (Audax d.o.o., Ljubljana, Slovenia), the tracing was performed by two radiologists with more than 5 years of experience in digital cephalometry to determine the inter-reliability, then it was repeated with an interval of two weeks to determine the intra-reliability. The landmarks were retraced automatically through the fully automatic option on the same software program using convolutional neural network. Results: Regarding McNamara analysis, the results of this study showed excellent reliability of the artificial intelligence measurements compared to the manual measurements, with an interclass correlation coefficient >0.9. Regarding Steiner analysis, our results showed excellent reliability of the artificial intelligence measurements compared to the manual measurements (0.75<ICC<1 excluding Positive 1/SN degree, Negative 1i/NB mm, Pg/NB mm, and S-L point mm, which show moderate reliability with 0.4<ICC<0.74). Two measurements showed poor reliability (Holdaway ratio and S-E point mm). Conclusions: The results of this study showed that the AudaxCeph automated software program has excellent reliability regarding McNamara and Steiner analyses. While in Steiner analysis, manual confirmation should be made with some dental landmarks

Article Details

Reliability of Artificial Intelligence in Lateral Cephalometric Analysis

Authors:

Hesham , Nouran | Ashmawy , Mostafa | Samir , Sahar

Publisher:

Ain Shams University : Faculty of Dentistry

Main Topics:

Medicine

Journal:

Ain Shams Dental Journal 2024 Vol.33 Issue 1, pp.61-71

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

264463

Frequency:

Quarterly

pISSN:

11107642

Descriptors:

DENTISTRY

ORTHODONTICS

X-RAYS

ARTIFICIAL INTELLIGENCE

MACHINE LEARNING

RELIABILITY

Artificial intelligence

Cephalometry

Deep learning

Issues

Article Details

Authors: Hesham , Nouran | Ashmawy , Mostafa | Samir , Sahar

Publisher: Ain Shams University : Faculty of Dentistry

Main Topics: Medicine

Journal: Ain Shams Dental Journal 2024 Vol.33 Issue 1, pp.61-71

Document Type: Article

Publication Date: 3/2024

Document Language: English

Document ID (Org No.): 264463

Frequency: Quarterly

pISSN: 11107642

Descriptors:

Issues

Authors:

Hesham , Nouran | Ashmawy , Mostafa | Samir , Sahar

Publisher:

Ain Shams University : Faculty of Dentistry

Main Topics:

Medicine

Journal:

Ain Shams Dental Journal 2024 Vol.33 Issue 1, pp.61-71

Document Type:

Article

Publication Date:

3/2024

Document Language:

English

Document ID (Org No.):

264463

Frequency:

Quarterly

pISSN:

11107642