A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification
Abstract Accurate identification and localization of cephalometric landmarks are crucial for diagnosing and quantifying anatomical abnormalities in orthodontics. Traditional manual annotation of these landmarks on lateral cephalograms (LCRs) is time-consuming and subject to inter- and intra-expert v...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05542-3 |
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| author | Muhammad Anwaar Khalid Kanwal Zulfiqar Ulfat Bashir Areeba Shaheen Rida Iqbal Zarnab Rizwan Ghina Rizwan Muhammad Moazam Fraz |
| author_facet | Muhammad Anwaar Khalid Kanwal Zulfiqar Ulfat Bashir Areeba Shaheen Rida Iqbal Zarnab Rizwan Ghina Rizwan Muhammad Moazam Fraz |
| author_sort | Muhammad Anwaar Khalid |
| collection | DOAJ |
| description | Abstract Accurate identification and localization of cephalometric landmarks are crucial for diagnosing and quantifying anatomical abnormalities in orthodontics. Traditional manual annotation of these landmarks on lateral cephalograms (LCRs) is time-consuming and subject to inter- and intra-expert variability. Attempts to develop automated landmark detection systems have persistently been made; however, they are inadequate for orthodontic applications due to the unavailability of a diverse dataset. In this work, we introduce a state-of-the-art cephalometric dataset designed to advance AI-driven quantitative morphometric analysis. Our dataset comprises 1,000 LCRs acquired from seven different imaging devices with varying resolutions, making it the most diverse and comprehensive collection to date. Each radiograph is meticulously annotated by clinical experts with 29 cephalometric landmarks, including the most extensive set of dental and soft tissue markers ever included in a public dataset. Additionally, we provide cervical vertebral maturation (CVM) stage annotations, marking the first standard resource for CVM classification. We anticipate that this dataset will serve as a benchmark for developing robust, automated landmark detection frameworks, with applications extending beyond orthodontics. |
| format | Article |
| id | doaj-art-602cb85080674bb88d10d211576c4e9d |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-602cb85080674bb88d10d211576c4e9d2025-08-20T03:42:23ZengNature PortfolioScientific Data2052-44632025-07-0112111310.1038/s41597-025-05542-3A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage ClassificationMuhammad Anwaar Khalid0Kanwal Zulfiqar1Ulfat Bashir2Areeba Shaheen3Rida Iqbal4Zarnab Rizwan5Ghina Rizwan6Muhammad Moazam Fraz7Peter L. Reichertz Institute for Medical InformaticsRiphah International UniversityRiphah International UniversityRiphah International UniversityRiphah International UniversityRiphah International UniversityRiphah International UniversityNational University of Sciences and Technology (NUST)Abstract Accurate identification and localization of cephalometric landmarks are crucial for diagnosing and quantifying anatomical abnormalities in orthodontics. Traditional manual annotation of these landmarks on lateral cephalograms (LCRs) is time-consuming and subject to inter- and intra-expert variability. Attempts to develop automated landmark detection systems have persistently been made; however, they are inadequate for orthodontic applications due to the unavailability of a diverse dataset. In this work, we introduce a state-of-the-art cephalometric dataset designed to advance AI-driven quantitative morphometric analysis. Our dataset comprises 1,000 LCRs acquired from seven different imaging devices with varying resolutions, making it the most diverse and comprehensive collection to date. Each radiograph is meticulously annotated by clinical experts with 29 cephalometric landmarks, including the most extensive set of dental and soft tissue markers ever included in a public dataset. Additionally, we provide cervical vertebral maturation (CVM) stage annotations, marking the first standard resource for CVM classification. We anticipate that this dataset will serve as a benchmark for developing robust, automated landmark detection frameworks, with applications extending beyond orthodontics.https://doi.org/10.1038/s41597-025-05542-3 |
| spellingShingle | Muhammad Anwaar Khalid Kanwal Zulfiqar Ulfat Bashir Areeba Shaheen Rida Iqbal Zarnab Rizwan Ghina Rizwan Muhammad Moazam Fraz A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification Scientific Data |
| title | A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification |
| title_full | A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification |
| title_fullStr | A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification |
| title_full_unstemmed | A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification |
| title_short | A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification |
| title_sort | benchmark dataset for automatic cephalometric landmark detection and cvm stage classification |
| url | https://doi.org/10.1038/s41597-025-05542-3 |
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