A Comparison of Automated Tracing Using Artificial Intelligence (AI) Software and Manual Digital Tracing Software.
Abstract
Background: Various fields of dentistry have been significantly influenced by Artificial
Intelligence. One of the most prominent uses of artificial intelligence in orthodontics is
automated cephalometric analysis. There have been many different automated cephalometric
software developed recently, and they claim to be as effective as digital cephalometric analysis.
Aim: To assess whether or not there is a statistically significant difference in time taken to
establish the cephalometric analysis using three methods: Dolphin software, AI-generated
cephalometric landmarks on WebCeph, and manually-modified cephalometric landmarks on
WebCeph, also to assess whether or not there are statistically significant differences in the
cephalometric analysis measurements between the same three methods.
Methods: Thirty lateral cephalometric radiographs of patients were consecutively selected, and
cephalometric analyses were done with three methods: digital tracing using Dolphin,
automated tracing using WebCeph, and automated tracing using WebCeph but with landmark
modification. Twenty-one measurements were obtained. The duration of each method was
measured in seconds, and the results were tallied. Values were registered in a spreadsheet.
Statistical analysis One-way ANOVA and The Kruskal–Wallis test were performed. The
intraclass correlation coefficient (ICC) was utilised to determine the level of agreement
between the measurements obtained from all three groups.
Results: There is a statistically significant difference in the tracing time between the three
groups (p-value = 0.0001). On the other hand, no statistically significant difference was found
between the groups when comparing lateral cephalometric tracing measurement values (P>
0.05). Moreover, a high level of agreement is evident between the measurements from each
group.
Conclusions: Compared to Dolphin tracing, WebCeph cephalometric values are relatively
accurate. It is economical, practical, and effective for routine orthodontic practices.
Description
Keywords
Artificial Intelligence, Orthodontics, Cephlometric analysis, Dolphin, WebCeph