Publications
2026
da Costa Barreto, Luísa Schubach; das Neves, Bruno Moreira; Bianchi, Jonas; Oh, Heesoo; dos Santos Lopes Batista, Klaus Barretto; Miguel, Jose Augusto Mendes
A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software Journal Article
In: Journal of Dentistry, vol. 165, 2026.
Abstract | Links | BibTeX | Tags: 3d, anatomic landmarks, artificial intelligence, CBCT, orthodontics
@article{nokey,
title = {A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software},
author = {Luísa Schubach da Costa Barreto and Bruno Moreira das Neves and Jonas Bianchi and Heesoo Oh and Klaus Barretto dos Santos Lopes Batista and Jose Augusto Mendes Miguel},
url = {https://www.sciencedirect.com/science/article/pii/S0300571225007353?pes=vor&utm_source=scopus&getft_integrator=scopus},
doi = {https://doi.org/10.1016/j.jdent.2025.106292},
year = {2026},
date = {2026-02-01},
urldate = {2026-02-01},
journal = {Journal of Dentistry},
volume = {165},
abstract = {Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning.},
keywords = {3d, anatomic landmarks, artificial intelligence, CBCT, orthodontics},
pubstate = {published},
tppubtype = {article}
}
Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning.
2023
M, Gillot; F, Miranda; B, Baquero; A, Ruellas; M, Gurgel; N, Al Turkestani; et al,
Automatic landmark identification in cone‐beam computed tomography. Journal Article
In: Orthod Craniofac Res, vol. 26, iss. 4, pp. 560-567, 2023.
Abstract | Links | BibTeX | Tags: anatomic landmarks, fiducial markers, machine learning
@article{Bianchi2023c,
title = {Automatic landmark identification in cone‐beam computed tomography. },
author = {Gillot M and Miranda F and Baquero B and Ruellas A and Gurgel M and Al Turkestani N and et al},
url = {https://pubmed.ncbi.nlm.nih.gov/36811276/},
doi = {10.1111/ocr.12642},
year = {2023},
date = {2023-11-26},
journal = {Orthod Craniofac Res},
volume = {26},
issue = {4},
pages = {560-567},
abstract = {Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.
Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.
Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.
Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.},
keywords = {anatomic landmarks, fiducial markers, machine learning},
pubstate = {published},
tppubtype = {article}
}
Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.
Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.
Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.
Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.
Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.
Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.
Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.
da Costa Barreto, Luísa Schubach; das Neves, Bruno Moreira; Bianchi, Jonas; Oh, Heesoo; dos Santos Lopes Batista, Klaus Barretto; Miguel, Jose Augusto Mendes
A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software Journal Article
In: Journal of Dentistry, vol. 165, 2026.
@article{nokey,
title = {A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software},
author = {Luísa Schubach da Costa Barreto and Bruno Moreira das Neves and Jonas Bianchi and Heesoo Oh and Klaus Barretto dos Santos Lopes Batista and Jose Augusto Mendes Miguel},
url = {https://www.sciencedirect.com/science/article/pii/S0300571225007353?pes=vor&utm_source=scopus&getft_integrator=scopus},
doi = {https://doi.org/10.1016/j.jdent.2025.106292},
year = {2026},
date = {2026-02-01},
urldate = {2026-02-01},
journal = {Journal of Dentistry},
volume = {165},
abstract = {Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning.
M, Gillot; F, Miranda; B, Baquero; A, Ruellas; M, Gurgel; N, Al Turkestani; et al,
Automatic landmark identification in cone‐beam computed tomography. Journal Article
In: Orthod Craniofac Res, vol. 26, iss. 4, pp. 560-567, 2023.
@article{Bianchi2023c,
title = {Automatic landmark identification in cone‐beam computed tomography. },
author = {Gillot M and Miranda F and Baquero B and Ruellas A and Gurgel M and Al Turkestani N and et al},
url = {https://pubmed.ncbi.nlm.nih.gov/36811276/},
doi = {10.1111/ocr.12642},
year = {2023},
date = {2023-11-26},
journal = {Orthod Craniofac Res},
volume = {26},
issue = {4},
pages = {560-567},
abstract = {Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.
Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.
Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.
Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.
Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.
Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.
Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.
Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.
Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.
Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.
2026 |
da Costa Barreto, Luísa Schubach; das Neves, Bruno Moreira; Bianchi, Jonas; Oh, Heesoo; dos Santos Lopes Batista, Klaus Barretto; Miguel, Jose Augusto Mendes: A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software. In: Journal of Dentistry, vol. 165, 2026. (Type: Journal Article | Abstract | Links | BibTeX | Tags: 3d, anatomic landmarks, artificial intelligence, CBCT, orthodontics)@article{nokey,Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning. |
2023 |
M, Gillot; F, Miranda; B, Baquero; A, Ruellas; M, Gurgel; N, Al Turkestani; et al,: Automatic landmark identification in cone‐beam computed tomography. . In: Orthod Craniofac Res, vol. 26, iss. 4, pp. 560-567, 2023. (Type: Journal Article | Abstract | Links | BibTeX | Tags: anatomic landmarks, fiducial markers, machine learning)@article{Bianchi2023c,Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans. Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position. Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU. Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision. |