Publications
2025
Bianchi, Jonas; Wilka, Lorena; Vallejo, Gabriel Bravo; Miranda, Felicia; Massaro, Camila; Cevidanes, Lucia; Oh, Heesoo
In: Seminars in Orthodontics, pp. 1-9, 2025, ISSN: 1073-8746.
Abstract | Links | BibTeX | Tags: 3D facial imaging, artificial intelligence, facial landmarks, facial scanner, soft tissue morphology
@article{nokey,
title = {A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics},
author = {Jonas Bianchi and Lorena Wilka and Gabriel Bravo Vallejo and Felicia Miranda and Camila Massaro and Lucia Cevidanes and Heesoo Oh},
url = {https://www.sciencedirect.com/science/article/pii/S1073874625001409?pes=vor&utm_source=scopus&getft_integrator=scopus},
doi = {https://doi.org/10.1053/j.sodo.2025.10.014},
issn = {1073-8746},
year = {2025},
date = {2025-10-29},
journal = {Seminars in Orthodontics},
pages = {1-9},
abstract = {Objectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus 3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the
University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for
generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.},
keywords = {3D facial imaging, artificial intelligence, facial landmarks, facial scanner, soft tissue morphology},
pubstate = {published},
tppubtype = {article}
}
Objectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus 3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the
University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for
generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.
University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for
generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.
Suh, Yoon Weon; Park, Yong Jong; Yue, Olivia; Yatabe, Marilia Sayako; Oh, Heesoo; Cevidanes, Lucia; Hansen, Meghan; Dipietro, Sophia; Bianchi, Jonas
Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis Journal Article
In: Orthodontics and Craniofacial Research, iss. 29, pp. 83-92, 2025.
Abstract | Links | BibTeX | Tags: 3D analysis, cleft lip and palate, Nasoalveolar, soft tissue morphology
@article{nokey,
title = {Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis},
author = {Yoon Weon Suh and Yong Jong Park and Olivia Yue and Marilia Sayako Yatabe and Heesoo Oh and Lucia Cevidanes and Meghan Hansen and Sophia Dipietro and Jonas Bianchi},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ocr.70051?getft_integrator=scopus&utm_source=scopus&utm_source=scopus},
doi = {https://doi.org/10.1111/ocr.70051},
year = {2025},
date = {2025-10-28},
journal = {Orthodontics and Craniofacial Research},
issue = {29},
pages = {83-92},
abstract = {Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions. },
keywords = {3D analysis, cleft lip and palate, Nasoalveolar, soft tissue morphology},
pubstate = {published},
tppubtype = {article}
}
Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions.
Bianchi, Jonas; Wilka, Lorena; Vallejo, Gabriel Bravo; Miranda, Felicia; Massaro, Camila; Cevidanes, Lucia; Oh, Heesoo
A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics Journal Article
In: Seminars in Orthodontics, pp. 1-9, 2025, ISSN: 1073-8746.
@article{nokey,
title = {A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics},
author = {Jonas Bianchi and Lorena Wilka and Gabriel Bravo Vallejo and Felicia Miranda and Camila Massaro and Lucia Cevidanes and Heesoo Oh},
url = {https://www.sciencedirect.com/science/article/pii/S1073874625001409?pes=vor&utm_source=scopus&getft_integrator=scopus},
doi = {https://doi.org/10.1053/j.sodo.2025.10.014},
issn = {1073-8746},
year = {2025},
date = {2025-10-29},
journal = {Seminars in Orthodontics},
pages = {1-9},
abstract = {Objectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus 3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the
University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for
generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus 3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the
University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for
generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.
University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for
generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.
Suh, Yoon Weon; Park, Yong Jong; Yue, Olivia; Yatabe, Marilia Sayako; Oh, Heesoo; Cevidanes, Lucia; Hansen, Meghan; Dipietro, Sophia; Bianchi, Jonas
Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis Journal Article
In: Orthodontics and Craniofacial Research, iss. 29, pp. 83-92, 2025.
@article{nokey,
title = {Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis},
author = {Yoon Weon Suh and Yong Jong Park and Olivia Yue and Marilia Sayako Yatabe and Heesoo Oh and Lucia Cevidanes and Meghan Hansen and Sophia Dipietro and Jonas Bianchi},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ocr.70051?getft_integrator=scopus&utm_source=scopus&utm_source=scopus},
doi = {https://doi.org/10.1111/ocr.70051},
year = {2025},
date = {2025-10-28},
journal = {Orthodontics and Craniofacial Research},
issue = {29},
pages = {83-92},
abstract = {Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions.
2025 |
Bianchi, Jonas; Wilka, Lorena; Vallejo, Gabriel Bravo; Miranda, Felicia; Massaro, Camila; Cevidanes, Lucia; Oh, Heesoo: A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics. In: Seminars in Orthodontics, pp. 1-9, 2025, ISSN: 1073-8746. (Type: Journal Article | Abstract | Links | BibTeX | Tags: 3D facial imaging, artificial intelligence, facial landmarks, facial scanner, soft tissue morphology)@article{nokey,Objectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus 3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed. |
Suh, Yoon Weon; Park, Yong Jong; Yue, Olivia; Yatabe, Marilia Sayako; Oh, Heesoo; Cevidanes, Lucia; Hansen, Meghan; Dipietro, Sophia; Bianchi, Jonas: Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis. In: Orthodontics and Craniofacial Research, iss. 29, pp. 83-92, 2025. (Type: Journal Article | Abstract | Links | BibTeX | Tags: 3D analysis, cleft lip and palate, Nasoalveolar, soft tissue morphology)@article{nokey,Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions. |