Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study
Abstract Background Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CB...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-025-05831-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850201069591724032 |
|---|---|
| author | Yan Jiang Canyang Jiang Bin Shi You Wu Shuli Xing Hao Liang Jianping Huang Xiaohong Huang Li Huang Lisong Lin |
| author_facet | Yan Jiang Canyang Jiang Bin Shi You Wu Shuli Xing Hao Liang Jianping Huang Xiaohong Huang Li Huang Lisong Lin |
| author_sort | Yan Jiang |
| collection | DOAJ |
| description | Abstract Background Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion. Methods A total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR). Results Each landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm. Conclusions Our proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising. |
| format | Article |
| id | doaj-art-e728dd891ad04e64871e18e9868ec0ac |
| institution | OA Journals |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-e728dd891ad04e64871e18e9868ec0ac2025-08-20T02:12:07ZengBMCBMC Oral Health1472-68312025-04-0125111610.1186/s12903-025-05831-8Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological studyYan Jiang0Canyang Jiang1Bin Shi2You Wu3Shuli Xing4Hao Liang5Jianping Huang6Xiaohong Huang7Li Huang8Lisong Lin9Department of Stomatology, The First Affiliated Hospital of Fujian Medical University, Tai-Jiang DistrictDepartment of Stomatology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical UniversityDepartment of Stomatology, The First Affiliated Hospital of Fujian Medical University, Tai-Jiang DistrictSchool of Stomatology, Fujian Medical UniversityCollege of Computer Science and Mathematics, Fujian University of TechnologyCollege of Computer Science and Mathematics, Fujian University of TechnologyDepartment of Stomatology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical UniversityDepartment of Stomatology, The First Affiliated Hospital of Fujian Medical University, Tai-Jiang DistrictDepartment of Stomatology, The First Affiliated Hospital of Fujian Medical University, Tai-Jiang DistrictDepartment of Stomatology, The First Affiliated Hospital of Fujian Medical University, Tai-Jiang DistrictAbstract Background Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion. Methods A total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR). Results Each landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm. Conclusions Our proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.https://doi.org/10.1186/s12903-025-05831-83D landmark identificationAccuracyCBCTDeep learning |
| spellingShingle | Yan Jiang Canyang Jiang Bin Shi You Wu Shuli Xing Hao Liang Jianping Huang Xiaohong Huang Li Huang Lisong Lin Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study BMC Oral Health 3D landmark identification Accuracy CBCT Deep learning |
| title | Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study |
| title_full | Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study |
| title_fullStr | Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study |
| title_full_unstemmed | Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study |
| title_short | Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study |
| title_sort | automatic identification of hard and soft tissue landmarks in cone beam computed tomography via deep learning with diversity datasets a methodological study |
| topic | 3D landmark identification Accuracy CBCT Deep learning |
| url | https://doi.org/10.1186/s12903-025-05831-8 |
| work_keys_str_mv | AT yanjiang automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT canyangjiang automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT binshi automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT youwu automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT shulixing automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT haoliang automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT jianpinghuang automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT xiaohonghuang automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT lihuang automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy AT lisonglin automaticidentificationofhardandsofttissuelandmarksinconebeamcomputedtomographyviadeeplearningwithdiversitydatasetsamethodologicalstudy |