Three-dimensional analysis of lip changes in response to simulated maxillary incisor advancement

[Cambios en la posición e inclinación del incisivo superior y del labio superior postratamiento ortodóntico: reportes de casos]

Objetivo

Determinar los cambios en la posición e inclinación del incisivo y el labio superiores después del tratamiento de ortodoncia en una serie de 3 casos clínicos.

Materiales y métodos

Los tres casos clínicos reportados corresponden a pacientes adultos que fueron tratados con ortodoncia fija y extracciones de premolares. Se realizaron mediciones de la posición de los incisivos superiores (UIP), la inclinación de los incisivos superiores (UII), la posición del labio superior (ULP) y la inclinación del labio superior (ULI) en radiografías cefalométricas previas y posteriores al tratamiento para evaluar los cambios.

Resultados

En el primer caso, se encontró una variación de -1 mm tanto para la UIP como para la ULP, así como una variación de la UII y la ULI, aunque en diferente magnitud. El caso 2 presentó una variación de 2 mm en el UII, con cambios mínimos en el labio superior (∆ ULP = 0 mm y ∆ ULI = -0,5 mm). En el caso 3 se obtuvo una variación de 2 mm tanto para IIS como para ILS.

Conclusiones

Los resultados obtenidos en este reporte de caso nos muestran una amplia variabilidad, por lo que es imposible predecir con precisión los cambios en los tejidos blandos como respuesta al movimiento dentario.

Importancia clínica

Predecir los cambios en el movimiento de los dientes en los tejidos blandos es fundamental durante la fase de planificación inicial del tratamiento de ortodoncia.

Validity and reliability of three-dimensional modeling of orthodontic dental casts using smartphone-based photogrammetric technology

BACKGROUND

The development of intraoral scanning technology has effectively enhanced the digital documentation of orthodontic dental casts. Albeit, the expense of this technology is the main limitation. The purpose of the present study was to assess the validity and reliability of virtual three-dimensional (3D) models of orthodontic dental casts, which were constructed using smartphone-based 3D photogrammetry.

METHODS

A smartphone was used to capture a set of two-dimensional images for 30 orthodontic dental casts. The captured images were processed to construct 3D virtual images using Agisoft and 3DF Zephyr software programs. To evaluate the accuracy of the virtual 3D models obtained by the two software programs, the virtual 3D models were compared with cone-beam computed tomography scans of the 30 dental casts. Colored maps were used to express the absolute distances between the points of each compared two surfaces; then, the means of the 100%, 95th, and 90th of the absolute distances were calculated. A Wilcoxon signed-rank test was applied to detect any significant differences.

RESULTS

The differences between the constructed 3D images and the cone-beam computed tomography scans were not statistically significant and were accepted clinically. The deviations were mostly in the interproximal areas and in the occlusal details (sharp cusps and deep pits and fissures).

CONCLUSIONS

This study found that smartphone-based stereophotogrammetry is an accurate and reliable method for 3D modeling of orthodontic dental casts, with errors less than the accepted clinically detectable error of 0.5 mm. Smartphone photogrammetry succeeded in presenting occlusal details, but it was difficult to accurately reproduce interproximal areas.

Evaluation of automated cephalometric analysis based on the latest deep learning method

OBJECTIVES

To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI).

MATERIALS AND METHODS

This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR).

RESULTS

SDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures.

CONCLUSIONS

This latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.