Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss
Abstract Background Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice. Methods Our...
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| Language: | English |
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BMC
2025-03-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-05677-0 |
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| author | Sang Won Lee Kateryna Huz Kayla Gorelick Jackie Li Thomas Bina Satoko Matsumura Noah Yin Nicholas Zhang Yvonne Naa Ardua Anang Sanam Sachadava Helena I. Servin-DeMarrais Donald J. McMahon Helen H. Lu Michael T. Yin Sunil Wadhwa |
| author_facet | Sang Won Lee Kateryna Huz Kayla Gorelick Jackie Li Thomas Bina Satoko Matsumura Noah Yin Nicholas Zhang Yvonne Naa Ardua Anang Sanam Sachadava Helena I. Servin-DeMarrais Donald J. McMahon Helen H. Lu Michael T. Yin Sunil Wadhwa |
| author_sort | Sang Won Lee |
| collection | DOAJ |
| description | Abstract Background Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice. Methods Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application. Results In total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82–87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting. Conclusion Overall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy. |
| format | Article |
| id | doaj-art-ebc374dc48604227902eab49772c1df3 |
| institution | OA Journals |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-ebc374dc48604227902eab49772c1df32025-08-20T02:16:49ZengBMCBMC Oral Health1472-68312025-03-012511910.1186/s12903-025-05677-0Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone lossSang Won Lee0Kateryna Huz1Kayla Gorelick2Jackie Li3Thomas Bina4Satoko Matsumura5Noah Yin6Nicholas Zhang7Yvonne Naa Ardua Anang8Sanam Sachadava9Helena I. Servin-DeMarrais10Donald J. McMahon11Helen H. Lu12Michael T. Yin13Sunil Wadhwa14Department of Biomedical Engineering, Columbia UniversityDivision of Orthodontics, Columbia University College of Dental MedicineDivision of Orthodontics, Columbia University College of Dental MedicineDepartment of Biomedical Engineering, Columbia UniversityDepartment of Biomedical Engineering, Columbia UniversityDivision of Oral & Maxillofacial Radiology, Columbia University College of Dental MedicineDepartment of Biomedical Engineering, Columbia UniversityDepartment of Biomedical Engineering, Columbia UniversityDepartment of Biomedical Engineering, Columbia UniversityDivision of Orthodontics, Columbia University College of Dental MedicineDepartment of Biomedical Engineering, Columbia UniversityDivision of Oral & Maxillofacial Radiology, Columbia University College of Dental MedicineDepartment of Biomedical Engineering, Columbia UniversityVagelos College of Physicians and Surgeons, Division of Infectious Diseases, Columbia UniversityDivision of Orthodontics, Columbia University College of Dental MedicineAbstract Background Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice. Methods Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application. Results In total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82–87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting. Conclusion Overall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy.https://doi.org/10.1186/s12903-025-05677-0Alveolar crestal heightPeriodontal diseaseArtificial intelligenceDeep learningAcceptabilityDental professionals |
| spellingShingle | Sang Won Lee Kateryna Huz Kayla Gorelick Jackie Li Thomas Bina Satoko Matsumura Noah Yin Nicholas Zhang Yvonne Naa Ardua Anang Sanam Sachadava Helena I. Servin-DeMarrais Donald J. McMahon Helen H. Lu Michael T. Yin Sunil Wadhwa Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss BMC Oral Health Alveolar crestal height Periodontal disease Artificial intelligence Deep learning Acceptability Dental professionals |
| title | Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss |
| title_full | Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss |
| title_fullStr | Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss |
| title_full_unstemmed | Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss |
| title_short | Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss |
| title_sort | evaluation by dental professionals of an artificial intelligence based application to measure alveolar bone loss |
| topic | Alveolar crestal height Periodontal disease Artificial intelligence Deep learning Acceptability Dental professionals |
| url | https://doi.org/10.1186/s12903-025-05677-0 |
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