A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis
Abstract Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-06-01
|
| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-022-01008-4 |
| _version_ | 1818205333396389888 |
|---|---|
| author | Juncheng Guo Yuyan Wu Lizhi Chen Shangbin Long Daqi Chen Haibing Ouyang Chunliang Zhang Yadong Tang Wenlong Wang |
| author_facet | Juncheng Guo Yuyan Wu Lizhi Chen Shangbin Long Daqi Chen Haibing Ouyang Chunliang Zhang Yadong Tang Wenlong Wang |
| author_sort | Juncheng Guo |
| collection | DOAJ |
| description | Abstract Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What’s more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted. |
| first_indexed | 2024-12-12T03:55:28Z |
| format | Article |
| id | doaj.art-c82db0d348974ee4aca4feb2b5f3878a |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-12T03:55:28Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-c82db0d348974ee4aca4feb2b5f3878a2022-12-22T00:39:16ZengBMCBioMedical Engineering OnLine1475-925X2022-06-0121112210.1186/s12938-022-01008-4A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysisJuncheng Guo0Yuyan Wu1Lizhi Chen2Shangbin Long3Daqi Chen4Haibing Ouyang5Chunliang Zhang6Yadong Tang7Wenlong Wang8School of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Mechanical and Electrical Engineering, Guangzhou UniversitySchool of Biomedical and Pharmaceutical Sciences, Guangdong University of TechnologySchool of Mechanical and Electrical Engineering, Guangzhou UniversityAbstract Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What’s more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.https://doi.org/10.1186/s12938-022-01008-4Review of oral diagnosisImage processingArtificial intelligenceSurvey of crack detection |
| spellingShingle | Juncheng Guo Yuyan Wu Lizhi Chen Shangbin Long Daqi Chen Haibing Ouyang Chunliang Zhang Yadong Tang Wenlong Wang A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis BioMedical Engineering OnLine Review of oral diagnosis Image processing Artificial intelligence Survey of crack detection |
| title | A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis |
| title_full | A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis |
| title_fullStr | A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis |
| title_full_unstemmed | A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis |
| title_short | A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis |
| title_sort | perspective on the diagnosis of cracked tooth imaging modalities evolve to ai based analysis |
| topic | Review of oral diagnosis Image processing Artificial intelligence Survey of crack detection |
| url | https://doi.org/10.1186/s12938-022-01008-4 |
| work_keys_str_mv | AT junchengguo aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT yuyanwu aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT lizhichen aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT shangbinlong aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT daqichen aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT haibingouyang aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT chunliangzhang aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT yadongtang aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT wenlongwang aperspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT junchengguo perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT yuyanwu perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT lizhichen perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT shangbinlong perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT daqichen perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT haibingouyang perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT chunliangzhang perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT yadongtang perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis AT wenlongwang perspectiveonthediagnosisofcrackedtoothimagingmodalitiesevolvetoaibasedanalysis |