Systematic review of the software used for virtual surgical planning in craniomaxillofacial surgery over the last decade

Tel, Alessandro; Arboit, Lorenzo; Martino, M. De; Isola, M.; Sembronio, Salvatore; Robiony, Massimo

doi:10.1016/j.ijom.2022.11.011

“…Pellerin et al 35 reported that the volume of liver lesions measured by CBCT and CT in rabbit experiments was almost the same size [3.8 ± 1.6 cm 3 (range, 1.3–7.3 cm 3 ) on CBCT and 3.9 ± 1.6 cm 3 (range, 1.8–7.5 cm 3 ) on MDCT]. Trindade et al 36 also reported that there was no difference in the volume of periapical lesions measured by CBCT and CT. Additionally, the Mimics program used in this study is reported to be quite accurate compared to other software 37 . Nevertheless, a strength of this study lies in the detailed analysis of volumetric changes in the TMJ space, divided into compartments, in patients who underwent TOVRO.…”

Section: Discussionmentioning

confidence: 85%

Volumetric changes in temporomandibular joint space following trans-oral vertical ramus osteotomy in patients with mandibular prognathism: a one-year follow-up study

Kim,

Yong,

Kim

et al. 2024

Sci Rep

2

0

View full text Add to dashboard Cite

This study measured and analyzed chronological changes in temporomandibular joint space volume by compartment following transoral vertical ramus osteotomy (TOVRO) using reconstructed 3-dimensional (3D) images of patients with mandibular prognathism. It included 70 joints of 35 patients who underwent TOVRO between January 2018 and December 2021. Computed tomography (CT) or cone-beam CT (CBCT) was performed before surgery (T0) and at 3 days (T1), 6 months (T2), and 12 months postoperatively (T3). These scans were then analyzed using 3D software. The volumes of the overall (Vjs), anterior (Vajs), posterior (Vpjs), medial (Vmjs), and lateral (Vljs) joint spaces were calculated at each time point. A linear mixed model and repeated-measures covariance pattern with unstructured covariance were used to evaluate significant changes in joint space volume over time. Vjs significantly increased to 134.54 ± 34.28 mm3 at T3 compared to T0 (p < 0.001). Vpjas and Vljs increased by 130.72 ± 10.07 mm3 and 109.98 ± 7.52 mm3 at T3 compared to T0, respectively (p < 0.001). However, no significant difference was observed between T0 and T2 in Vajs and Vmjs (p = 0.9999). The observed volume increases in Vpjs and Vljs appeared to contribute to the overall Vjs increase.

show abstract

“…Due to its high precision and consistency, the developed automated landmarking method has the potential to be applied in various fields. Possible applications include objective follow-up and analysis of soft-tissue facial deformities, growth evaluation, facial asymmetry assessment, and integration in virtual planning software for 3D backward planning 23 , 24 . Considering that the proposed DiffusionNet-based approach only uses spatial features, it could be applied on 3D meshes of facial soft-tissue that are derived from imaging modalities lacking texture, such as CT, CBCT, or MRI.…”

Section: Discussionmentioning

confidence: 99%

Fully automated landmarking and facial segmentation on 3D photographs

Berends,

Bielevelt,

Schreurs

et al. 2024

Sci Rep

2

0

View full text Add to dashboard Cite

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

show abstract

“…Due to its high precision and consistency, the developed automated landmarking method has the potential to be applied in various elds. Possible applications include objective follow-up and analysis of softtissue facial deformities, growth evaluation, facial asymmetry assessment, and integration in virtual planning software for 3D backward planning 20,21 . Considering that the proposed DiffusionNet-based approach only uses spatial features, it could be applied on 3D meshes of facial soft tissue that are derived from imaging modalities lacking texture, such as CT, CBCT, or MRI.…”

Section: Discussionmentioning

confidence: 99%

Fully automated landmarking and facial segmentation on 3D photographs

Berends,

Bielevelt,

Schreurs

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

show abstract

Systematic review of the software used for virtual surgical planning in craniomaxillofacial surgery over the last decade

Cited by 12 publications

References 56 publications

Volumetric changes in temporomandibular joint space following trans-oral vertical ramus osteotomy in patients with mandibular prognathism: a one-year follow-up study

Volumetric changes in temporomandibular joint space following trans-oral vertical ramus osteotomy in patients with mandibular prognathism: a one-year follow-up study

Fully automated landmarking and facial segmentation on 3D photographs

Fully automated landmarking and facial segmentation on 3D photographs

Contact Info

Product

Resources

About