Purpose: The aim of this study was to compare the accuracy of AI-based AudaxCeph software, Dolphin software, and the manual technique for identifying orthodontic landmarks and tracing lateral cephalograms. Materials and Methods: In this cross-sectional study, 23 anatomical landmarks were identified on 60 randomlyselected lateral cephalograms, and 5 dental indices, 4 skeletal indices, and 1 soft tissue index were measured. Each cephalogram was traced using 4 different methods: manually, with the Dolphin software, with the AudaxCeph software automatically, and with the AudaxCeph software in semi-automatic mode. The intra-class correlation coefficient (ICC) and Bland-Altman plots were used to evaluate the agreement between methods. Inter-observer and intra-observer agreements, calculated using the ICC, confirmed the accuracy, reliability, and reproducibility of the results: . Results: There was strong agreement among the AudexCeph (semi-automated or automated) AudaxCeph, Dolphin, and manual methods in measuring orthodontic indices, with ICC values consistently above 0.90. Bland-Altman plots confirmed satisfactory agreement between both versions of AudaxCeph (semi-automated and automated) with the manual method, with mean differences close to 0 and about 95% of data points within the limits of agreement.However, the semi-automated AudaxCeph showed greater agreement and precision than the automated version, as indicated by narrower limits of agreement. The ICC values for inter-observer and intra-observer agreements exceeded 0.98 and 0.99, respectively. Conclusion The semi-automated AudaxCeph software offers a robust and cost-effective solution for cephalometric analysis. Its high accuracy and affordability make it a compelling alternative to Dolphin software and the manual method.

Purpose: The aim of this study was to compare the accuracy of AI-based AudaxCeph software, Dolphin software, and the manual technique for identifying orthodontic landmarks and tracing lateral cephalograms. Materials and Methods: In this cross-sectional study, 23 anatomical landmarks were identified on 60 randomlyselected lateral cephalograms, and 5 dental indices, 4 skeletal indices, and 1 soft tissue index were measured. Each cephalogram was traced using 4 different methods: manually, with the Dolphin software, with the AudaxCeph software automatically, and with the AudaxCeph software in semi-automatic mode. The intra-class correlation coefficient (ICC) and Bland-Altman plots were used to evaluate the agreement between methods. Inter-observer and intra-observer agreements, calculated using the ICC, confirmed the accuracy, reliability, and reproducibility of the results: . Results: There was strong agreement among the AudexCeph (semi-automated or automated) AudaxCeph, Dolphin, and manual methods in measuring orthodontic indices, with ICC values consistently above 0.90. Bland-Altman plots confirmed satisfactory agreement between both versions of AudaxCeph (semi-automated and automated) with the manual method, with mean differences close to 0 and about 95% of data points within the limits of agreement.However, the semi-automated AudaxCeph showed greater agreement and precision than the automated version, as indicated by narrower limits of agreement. The ICC values for inter-observer and intra-observer agreements exceeded 0.98 and 0.99, respectively. Conclusion The semi-automated AudaxCeph software offers a robust and cost-effective solution for cephalometric analysis. Its high accuracy and affordability make it a compelling alternative to Dolphin software and the manual method.

Purpose: The aim of this study was to compare the accuracy of AI-based AudaxCeph software, Dolphin software, and the manual technique for identifying orthodontic landmarks and tracing lateral cephalograms. Materials and Methods: In this cross-sectional study, 23 anatomical landmarks were identified on 60 randomlyselected lateral cephalograms, and 5 dental indices, 4 skeletal indices, and 1 soft tissue index were measured. Each cephalogram was traced using 4 different methods: manually, with the Dolphin software, with the AudaxCeph software automatically, and with the AudaxCeph software in semi-automatic mode. The intra-class correlation coefficient (ICC) and Bland-Altman plots were used to evaluate the agreement between methods. Inter-observer and intra-observer agreements, calculated using the ICC, confirmed the accuracy, reliability, and reproducibility of the results: . Results: There was strong agreement among the AudexCeph (semi-automated or automated) AudaxCeph, Dolphin, and manual methods in measuring orthodontic indices, with ICC values consistently above 0.90. Bland-Altman plots confirmed satisfactory agreement between both versions of AudaxCeph (semi-automated and automated) with the manual method, with mean differences close to 0 and about 95% of data points within the limits of agreement.However, the semi-automated AudaxCeph showed greater agreement and precision than the automated version, as indicated by narrower limits of agreement. The ICC values for inter-observer and intra-observer agreements exceeded 0.98 and 0.99, respectively. Conclusion The semi-automated AudaxCeph software offers a robust and cost-effective solution for cephalometric analysis. Its high accuracy and affordability make it a compelling alternative to Dolphin software and the manual method.