Journal of dentistry · 2024
Feasibility of using two generative AI models for teeth reconstruction.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Saleh O, Spies B C, Brandenburg L S, Metzger M C, Lüchtenborg J, Blatz M B, Burkhardt F
Аннотация
OBJECTIVES: This feasibility study investigates the application of artificial intelligence (AI) models, specifically transformer-based (TM) and diffusion-based (DM) models, for the reconstruction of single and multiple missing teeth.
METHODS: A dataset of 129 digitized models was utilized to create both TM and DM models. Single and multiple missing teeth were artificially generated. Reconstruction accuracy was assessed against ground truth data using Root Mean Square (RMS) and mean absolute error (MAE) across various artificially generated teeth. Paired t-tests were used for analyzing differences between the two models (p < 0.05).
RESULTS: Both TM and DM models demonstrated similar accuracy in the reconstruction of single and multiple missing teeth. The greatest disparity occurred in the reconstruction of all remaining teeth, with the exception of 33 and 43 for both models (RMS TM: 0.37; DM: 0.43). TM exhibited the highest precision in reconstructing tooth 34 (RMS: 0.21), whereas DM demonstrated superior accuracy in reconstructing tooth 21 (RMS: 0.19). Despite there was no significant difference between the models.
CONCLUSIONS: AI-based TM and DM models demonstrate promising results in reconstructing missing teeth, with superior accuracy in single-tooth compared to multiple-tooth edentulous spaces. Despite the need for additional refining and larger datasets, including antagonistic teeth, these models have the potential to streamline and improve the dental restoration processes, potentially leading to cost savings and enhanced clinical outcomes.
SIGNIFICANCE: This study demonstrates the feasibility and potential of transformer- and diffusion-based AI models to accurately reconstruct missing teeth, offering a novel approach that could streamline and enhance the precision of implant planning.
METHODS: A dataset of 129 digitized models was utilized to create both TM and DM models. Single and multiple missing teeth were artificially generated. Reconstruction accuracy was assessed against ground truth data using Root Mean Square (RMS) and mean absolute error (MAE) across various artificially generated teeth. Paired t-tests were used for analyzing differences between the two models (p < 0.05).
RESULTS: Both TM and DM models demonstrated similar accuracy in the reconstruction of single and multiple missing teeth. The greatest disparity occurred in the reconstruction of all remaining teeth, with the exception of 33 and 43 for both models (RMS TM: 0.37; DM: 0.43). TM exhibited the highest precision in reconstructing tooth 34 (RMS: 0.21), whereas DM demonstrated superior accuracy in reconstructing tooth 21 (RMS: 0.19). Despite there was no significant difference between the models.
CONCLUSIONS: AI-based TM and DM models demonstrate promising results in reconstructing missing teeth, with superior accuracy in single-tooth compared to multiple-tooth edentulous spaces. Despite the need for additional refining and larger datasets, including antagonistic teeth, these models have the potential to streamline and improve the dental restoration processes, potentially leading to cost savings and enhanced clinical outcomes.
SIGNIFICANCE: This study demonstrates the feasibility and potential of transformer- and diffusion-based AI models to accurately reconstruct missing teeth, offering a novel approach that could streamline and enhance the precision of implant planning.