Deep learning based quantitative cervical vertebral maturation analysis
Abstract Objectives This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variation...
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BMC
2025-03-01
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| Series: | Head & Face Medicine |
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| Online Access: | https://doi.org/10.1186/s13005-025-00498-6 |
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| author | Fulin Jiang Abbas Ahmed Abdulqader Yan Yan Fangyuan Cheng Tao Xiang Jinghong Yu Juan Li Yong Qiu Xin Chen |
| author_facet | Fulin Jiang Abbas Ahmed Abdulqader Yan Yan Fangyuan Cheng Tao Xiang Jinghong Yu Juan Li Yong Qiu Xin Chen |
| author_sort | Fulin Jiang |
| collection | DOAJ |
| description | Abstract Objectives This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. Therefore, we designed an advanced two-stage convolutional neural network customized for improved accuracy in cervical vertebrae analysis. Methods This study analyzed 2100 cephalometric images. The data distribution to an 8:1:1 for training, validation, and testing. The CVnet system was designed as a two-step method with a comprehensive evaluation of various regions of interest (ROI) sizes to locate 19 cervical vertebral landmarks and classify precision maturation stages. The accuracy of landmark localization was assessed by success detection rate and student t-test. The QCVM diagnostic accuracy test was conducted to evaluate the assistant performances of our system for six junior orthodontists. Results Upon precise calibration with optimal ROI size, the landmark localization registered an average error of 0.66 ± 0.46 mm and a success detection rate of 98.10% within 2 mm. Additionally, the identification accuracy of QCVM stages was 69.52%, resulting in an enhancement of 10.95% in the staging accuracy of junior orthodontists in the diagnostic test. Conclusions This study presented a two-stage neural network that successfully automated the identification of cervical vertebral landmarks and the staging of QCVM. By streamlining the workflow and enhancing the accuracy of skeletal maturation estimation, this method offered valuable clinical support, particularly for practitioners with limited experience or access to advanced diagnostic resources, facilitating more consistent and reliable treatment planning. |
| format | Article |
| id | doaj-art-ed8a0657e89e4edcb683298bd3a0b16a |
| institution | Kabale University |
| issn | 1746-160X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Head & Face Medicine |
| spelling | doaj-art-ed8a0657e89e4edcb683298bd3a0b16a2025-08-20T03:41:14ZengBMCHead & Face Medicine1746-160X2025-03-0121111210.1186/s13005-025-00498-6Deep learning based quantitative cervical vertebral maturation analysisFulin Jiang0Abbas Ahmed Abdulqader1Yan Yan2Fangyuan Cheng3Tao Xiang4Jinghong Yu5Juan Li6Yong Qiu7Xin Chen8College of Computer Science, Chongqing University, Chongqing University Three Gorges HospitalState Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan UniversityState Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan UniversityChengdu Boltzmann Intelligence Technology Co., LtdCollege of Computer Science, Chongqing University, Chongqing University Three Gorges HospitalCollege of Computer Science, Chongqing University, Chongqing University Three Gorges HospitalState Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan UniversityCollege of Computer Science, Chongqing University, Chongqing University Three Gorges HospitalCollege of Computer Science, Chongqing University, Chongqing University Three Gorges HospitalAbstract Objectives This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. Therefore, we designed an advanced two-stage convolutional neural network customized for improved accuracy in cervical vertebrae analysis. Methods This study analyzed 2100 cephalometric images. The data distribution to an 8:1:1 for training, validation, and testing. The CVnet system was designed as a two-step method with a comprehensive evaluation of various regions of interest (ROI) sizes to locate 19 cervical vertebral landmarks and classify precision maturation stages. The accuracy of landmark localization was assessed by success detection rate and student t-test. The QCVM diagnostic accuracy test was conducted to evaluate the assistant performances of our system for six junior orthodontists. Results Upon precise calibration with optimal ROI size, the landmark localization registered an average error of 0.66 ± 0.46 mm and a success detection rate of 98.10% within 2 mm. Additionally, the identification accuracy of QCVM stages was 69.52%, resulting in an enhancement of 10.95% in the staging accuracy of junior orthodontists in the diagnostic test. Conclusions This study presented a two-stage neural network that successfully automated the identification of cervical vertebral landmarks and the staging of QCVM. By streamlining the workflow and enhancing the accuracy of skeletal maturation estimation, this method offered valuable clinical support, particularly for practitioners with limited experience or access to advanced diagnostic resources, facilitating more consistent and reliable treatment planning.https://doi.org/10.1186/s13005-025-00498-6Quantitative cervical vertebral maturation (QCVM)Lateral cephalogramAutomated landmark locationArtificial intelligenceOrthodontics |
| spellingShingle | Fulin Jiang Abbas Ahmed Abdulqader Yan Yan Fangyuan Cheng Tao Xiang Jinghong Yu Juan Li Yong Qiu Xin Chen Deep learning based quantitative cervical vertebral maturation analysis Head & Face Medicine Quantitative cervical vertebral maturation (QCVM) Lateral cephalogram Automated landmark location Artificial intelligence Orthodontics |
| title | Deep learning based quantitative cervical vertebral maturation analysis |
| title_full | Deep learning based quantitative cervical vertebral maturation analysis |
| title_fullStr | Deep learning based quantitative cervical vertebral maturation analysis |
| title_full_unstemmed | Deep learning based quantitative cervical vertebral maturation analysis |
| title_short | Deep learning based quantitative cervical vertebral maturation analysis |
| title_sort | deep learning based quantitative cervical vertebral maturation analysis |
| topic | Quantitative cervical vertebral maturation (QCVM) Lateral cephalogram Automated landmark location Artificial intelligence Orthodontics |
| url | https://doi.org/10.1186/s13005-025-00498-6 |
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