Reliability and accuracy of Artificial intelligence-based software for cephalometric diagnosis. A diagnostic study

Abstract

BACKGROUND: Artificial intelligence (AI) is revolutionizing cephalometric diagnosis in orthodontics, streamlining the patient assessments. This study aimed to assess the reliability, accuracy, and time consumption of artificial intelligence (AI)-based software compared to a conventional digital cephalometric analysis method on 2D lateral cephalogram. METHODS: 408 lateral cephalometries were analysed using three methods: manual landmark localization, automatic localization, and semi-automatic localization with AI-based software. On each lateral cephalogram, 15 variables were selected, including skeletal, dental, and soft tissue measurements. The difference between the two AI-based software options (automatic and semi-automatic) was compared with the conventional digital technique. The time required to produce a complete cephalometric tracing was evaluated for each method using Student's t-test. RESULTS: Statistically significant differences in the accuracy of landmark positioning were detected among the three different techniques (p < 0,01). However, it is noteworthy that almost all of these differences were not clinically significant. There was a small difference in accuracy between the semi-automatic AI-based option and conventional digital techniques. Regarding the time used for each technique, the automatic version was the fastest, followed by the semi-automatic option and the conventional digital technique. (p < 0,000). CONCLUSIONS: The study showed a statistical difference in accuracy between the conventional digital technique and two AI-based software alternatives, but these differences were not clinically significant except for specific measurements. The semi-automatic option was more accurate than the automatic one and faster than conventional tracing. Further research is needed to confirm AI's accuracy in cephalometric tracing.

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