Publications
2025
Jung, Young-Eun; Suh, Heeyeon; Park, Joorok; Oh, Heesoo
In: The Angle Orthodontist, vol. 95, iss. 4, pp. 362-370, 2025.
Abstract | Links | BibTeX | Tags: Automated, CBCT, Cephalometric analysis, Landmark Identification
@article{Jung2025,
title = {Accuracy and reliability of automated landmark identification and cephalometric measurements on cone beam computed tomography using Invivo software},
author = {Young-Eun Jung and Heeyeon Suh and Joorok Park and Heesoo Oh },
url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/4/article-p362.xml},
doi = {10.2319/122324-1049.1},
year = {2025},
date = {2025-04-10},
urldate = {2025-04-10},
journal = {The Angle Orthodontist},
volume = {95},
issue = {4},
pages = {362-370},
abstract = {Objectives: To evaluate the accuracy and reliability of an automated landmark identification (ALI) system and the impact of ALI errors on cephalometric measurements on cone-beam computed tomography (CBCT) images. Materials and Methods: Thirty-one landmarks were identified on 76 CBCT images using Invivo7 software (Anatomage, San Jose, Calif). Ground truth was established by averaging landmark coordinates from two calibrated human examiners. The accuracy of the ALI system was assessed by the mean absolute error (MAE, mm) across coordinate axes, the mean error distance (mm), and the successful detection rate (SDR) for each landmark. Interexaminer reliability between the ALI and manual landmark location was evaluated. Eighteen cephalometric measurements were computed from 25 landmarks. Accuracy of measurements from the ALI system was assessed with the MAE and successful measurement rates (SMR). Results: The ALI system closely matched human examiners in landmark identification, with an average MAE of 0.94 +/- 0.99 mm. Across all three coordinate axes, 87% of the landmarks had <2 mm MAE. ALI average MAE for conventional linear and angular cephalometric measurements were 1.35 +/- 1.33 mm and 0.89 +/- 0.89 degrees, respectively. Only one measurement, Intercondylar Width, showed MAE >3 mm. Conclusions: The ALI system showed clinically acceptable accuracy and reliability for the majority of cephalometric landmarks and measurements. Clinicians are advised to critically evaluate ALI landmarks with substantial errors, to fully utilize the capabilities of commercial software effectively. (Angle Orthod. 2025;95:362–370.)},
keywords = {Automated, CBCT, Cephalometric analysis, Landmark Identification},
pubstate = {published},
tppubtype = {article}
}
Objectives: To evaluate the accuracy and reliability of an automated landmark identification (ALI) system and the impact of ALI errors on cephalometric measurements on cone-beam computed tomography (CBCT) images. Materials and Methods: Thirty-one landmarks were identified on 76 CBCT images using Invivo7 software (Anatomage, San Jose, Calif). Ground truth was established by averaging landmark coordinates from two calibrated human examiners. The accuracy of the ALI system was assessed by the mean absolute error (MAE, mm) across coordinate axes, the mean error distance (mm), and the successful detection rate (SDR) for each landmark. Interexaminer reliability between the ALI and manual landmark location was evaluated. Eighteen cephalometric measurements were computed from 25 landmarks. Accuracy of measurements from the ALI system was assessed with the MAE and successful measurement rates (SMR). Results: The ALI system closely matched human examiners in landmark identification, with an average MAE of 0.94 +/- 0.99 mm. Across all three coordinate axes, 87% of the landmarks had <2 mm MAE. ALI average MAE for conventional linear and angular cephalometric measurements were 1.35 +/- 1.33 mm and 0.89 +/- 0.89 degrees, respectively. Only one measurement, Intercondylar Width, showed MAE >3 mm. Conclusions: The ALI system showed clinically acceptable accuracy and reliability for the majority of cephalometric landmarks and measurements. Clinicians are advised to critically evaluate ALI landmarks with substantial errors, to fully utilize the capabilities of commercial software effectively. (Angle Orthod. 2025;95:362–370.)
2022
Ghowsi, A; Hatcher, D; Suh, H; Wiled, D; Castro, W; Krueger, J; Park, J; Oh, H
Automated landmark identification on one cone beam computed tomography: Accuracy and reliability Journal Article
In: Angle Orthodontist, vol. 92, pp. 642-654, 2022.
Abstract | Links | BibTeX | Tags: 3D landmark identification, AAOF, accuracy, Automated, CBCT, Landmark error, Reliability
@article{Oh2022b,
title = {Automated landmark identification on one cone beam computed tomography: Accuracy and reliability},
author = {A Ghowsi and D Hatcher and H Suh and D Wiled and W Castro and J Krueger and J Park and H Oh},
url = {https://pubmed.ncbi.nlm.nih.gov/35653226/},
doi = {10.2319/122121-928.1},
year = {2022},
date = {2022-06-02},
urldate = {2022-06-02},
journal = {Angle Orthodontist},
volume = {92},
pages = {642-654},
abstract = {Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.
Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.},
keywords = {3D landmark identification, AAOF, accuracy, Automated, CBCT, Landmark error, Reliability},
pubstate = {published},
tppubtype = {article}
}
Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.
Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.
Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.
Jung, Young-Eun; Suh, Heeyeon; Park, Joorok; Oh, Heesoo
Accuracy and reliability of automated landmark identification and cephalometric measurements on cone beam computed tomography using Invivo software Journal Article
In: The Angle Orthodontist, vol. 95, iss. 4, pp. 362-370, 2025.
@article{Jung2025,
title = {Accuracy and reliability of automated landmark identification and cephalometric measurements on cone beam computed tomography using Invivo software},
author = {Young-Eun Jung and Heeyeon Suh and Joorok Park and Heesoo Oh },
url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/4/article-p362.xml},
doi = {10.2319/122324-1049.1},
year = {2025},
date = {2025-04-10},
urldate = {2025-04-10},
journal = {The Angle Orthodontist},
volume = {95},
issue = {4},
pages = {362-370},
abstract = {Objectives: To evaluate the accuracy and reliability of an automated landmark identification (ALI) system and the impact of ALI errors on cephalometric measurements on cone-beam computed tomography (CBCT) images. Materials and Methods: Thirty-one landmarks were identified on 76 CBCT images using Invivo7 software (Anatomage, San Jose, Calif). Ground truth was established by averaging landmark coordinates from two calibrated human examiners. The accuracy of the ALI system was assessed by the mean absolute error (MAE, mm) across coordinate axes, the mean error distance (mm), and the successful detection rate (SDR) for each landmark. Interexaminer reliability between the ALI and manual landmark location was evaluated. Eighteen cephalometric measurements were computed from 25 landmarks. Accuracy of measurements from the ALI system was assessed with the MAE and successful measurement rates (SMR). Results: The ALI system closely matched human examiners in landmark identification, with an average MAE of 0.94 +/- 0.99 mm. Across all three coordinate axes, 87% of the landmarks had <2 mm MAE. ALI average MAE for conventional linear and angular cephalometric measurements were 1.35 +/- 1.33 mm and 0.89 +/- 0.89 degrees, respectively. Only one measurement, Intercondylar Width, showed MAE >3 mm. Conclusions: The ALI system showed clinically acceptable accuracy and reliability for the majority of cephalometric landmarks and measurements. Clinicians are advised to critically evaluate ALI landmarks with substantial errors, to fully utilize the capabilities of commercial software effectively. (Angle Orthod. 2025;95:362–370.)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: To evaluate the accuracy and reliability of an automated landmark identification (ALI) system and the impact of ALI errors on cephalometric measurements on cone-beam computed tomography (CBCT) images. Materials and Methods: Thirty-one landmarks were identified on 76 CBCT images using Invivo7 software (Anatomage, San Jose, Calif). Ground truth was established by averaging landmark coordinates from two calibrated human examiners. The accuracy of the ALI system was assessed by the mean absolute error (MAE, mm) across coordinate axes, the mean error distance (mm), and the successful detection rate (SDR) for each landmark. Interexaminer reliability between the ALI and manual landmark location was evaluated. Eighteen cephalometric measurements were computed from 25 landmarks. Accuracy of measurements from the ALI system was assessed with the MAE and successful measurement rates (SMR). Results: The ALI system closely matched human examiners in landmark identification, with an average MAE of 0.94 +/- 0.99 mm. Across all three coordinate axes, 87% of the landmarks had <2 mm MAE. ALI average MAE for conventional linear and angular cephalometric measurements were 1.35 +/- 1.33 mm and 0.89 +/- 0.89 degrees, respectively. Only one measurement, Intercondylar Width, showed MAE >3 mm. Conclusions: The ALI system showed clinically acceptable accuracy and reliability for the majority of cephalometric landmarks and measurements. Clinicians are advised to critically evaluate ALI landmarks with substantial errors, to fully utilize the capabilities of commercial software effectively. (Angle Orthod. 2025;95:362–370.)
Ghowsi, A; Hatcher, D; Suh, H; Wiled, D; Castro, W; Krueger, J; Park, J; Oh, H
Automated landmark identification on one cone beam computed tomography: Accuracy and reliability Journal Article
In: Angle Orthodontist, vol. 92, pp. 642-654, 2022.
@article{Oh2022b,
title = {Automated landmark identification on one cone beam computed tomography: Accuracy and reliability},
author = {A Ghowsi and D Hatcher and H Suh and D Wiled and W Castro and J Krueger and J Park and H Oh},
url = {https://pubmed.ncbi.nlm.nih.gov/35653226/},
doi = {10.2319/122121-928.1},
year = {2022},
date = {2022-06-02},
urldate = {2022-06-02},
journal = {Angle Orthodontist},
volume = {92},
pages = {642-654},
abstract = {Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.
Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.
Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.
Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.
2025 |
Jung, Young-Eun; Suh, Heeyeon; Park, Joorok; Oh, Heesoo: Accuracy and reliability of automated landmark identification and cephalometric measurements on cone beam computed tomography using Invivo software. In: The Angle Orthodontist, vol. 95, iss. 4, pp. 362-370, 2025. (Type: Journal Article | Abstract | Links | BibTeX | Tags: Automated, CBCT, Cephalometric analysis, Landmark Identification)@article{Jung2025,Objectives: To evaluate the accuracy and reliability of an automated landmark identification (ALI) system and the impact of ALI errors on cephalometric measurements on cone-beam computed tomography (CBCT) images. Materials and Methods: Thirty-one landmarks were identified on 76 CBCT images using Invivo7 software (Anatomage, San Jose, Calif). Ground truth was established by averaging landmark coordinates from two calibrated human examiners. The accuracy of the ALI system was assessed by the mean absolute error (MAE, mm) across coordinate axes, the mean error distance (mm), and the successful detection rate (SDR) for each landmark. Interexaminer reliability between the ALI and manual landmark location was evaluated. Eighteen cephalometric measurements were computed from 25 landmarks. Accuracy of measurements from the ALI system was assessed with the MAE and successful measurement rates (SMR). Results: The ALI system closely matched human examiners in landmark identification, with an average MAE of 0.94 +/- 0.99 mm. Across all three coordinate axes, 87% of the landmarks had <2 mm MAE. ALI average MAE for conventional linear and angular cephalometric measurements were 1.35 +/- 1.33 mm and 0.89 +/- 0.89 degrees, respectively. Only one measurement, Intercondylar Width, showed MAE >3 mm. Conclusions: The ALI system showed clinically acceptable accuracy and reliability for the majority of cephalometric landmarks and measurements. Clinicians are advised to critically evaluate ALI landmarks with substantial errors, to fully utilize the capabilities of commercial software effectively. (Angle Orthod. 2025;95:362–370.) |
2022 |
Ghowsi, A; Hatcher, D; Suh, H; Wiled, D; Castro, W; Krueger, J; Park, J; Oh, H: Automated landmark identification on one cone beam computed tomography: Accuracy and reliability. In: Angle Orthodontist, vol. 92, pp. 642-654, 2022. (Type: Journal Article | Abstract | Links | BibTeX | Tags: 3D landmark identification, AAOF, accuracy, Automated, CBCT, Landmark error, Reliability)@article{Oh2022b,Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges. Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated. Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range. Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs. |