Endocranial Morphology in Metopism
Comparative investigations on homogenous cranial series have demonstrated that metopism is linked to a specific configuration of the cranial vault; however, there are no comparative data concerning the endocranial morphology in this condition. This study aimed to compare the endocranial space in met...
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MDPI AG
2025-07-01
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| author | Silviya Nikolova Diana Toneva Gennady Agre |
| author_facet | Silviya Nikolova Diana Toneva Gennady Agre |
| author_sort | Silviya Nikolova |
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| description | Comparative investigations on homogenous cranial series have demonstrated that metopism is linked to a specific configuration of the cranial vault; however, there are no comparative data concerning the endocranial morphology in this condition. This study aimed to compare the endocranial space in metopic and control crania using morphometric analysis and machine learning algorithms. For this purpose, a series of 230 (184 control and 46 metopic) dry crania of contemporary adult Bulgarian males were scanned using an industrial µCT system. The 3D coordinates of 47 landmarks were collected on the endocranial surface. All possible measurements between the landmarks were calculated as Euclidean distances. The resultant 1081 measurements represented the initial dataset, which was reduced to smaller datasets applying different criteria. The derived datasets were used for learning a set of classification models by machine learning algorithms. The morphometric analysis showed that in the metopic crania some segments of the anterior and middle cranial fossae were significantly longer, and the landmark endobregma was significantly closer to the anterior and middle sections of the cranial base. The most accurate model, with a classification accuracy of 85%, was the Naive Bayes one learned on a dataset of 69 attributes assembled after an attribute selection procedure. |
| format | Article |
| id | doaj-art-5a2124fbce6d4c4e9f463e823c879846 |
| institution | DOAJ |
| issn | 2079-7737 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Biology |
| spelling | doaj-art-5a2124fbce6d4c4e9f463e823c8798462025-08-20T02:45:53ZengMDPI AGBiology2079-77372025-07-0114783510.3390/biology14070835Endocranial Morphology in MetopismSilviya Nikolova0Diana Toneva1Gennady Agre2Department of Anthropology and Anatomy, Institute of Experimental Morphology Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, 1113 Sofia, BulgariaDepartment of Anthropology and Anatomy, Institute of Experimental Morphology Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, 1113 Sofia, BulgariaDepartment of Linguistic Modelling and Knowledge Processing, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, BulgariaComparative investigations on homogenous cranial series have demonstrated that metopism is linked to a specific configuration of the cranial vault; however, there are no comparative data concerning the endocranial morphology in this condition. This study aimed to compare the endocranial space in metopic and control crania using morphometric analysis and machine learning algorithms. For this purpose, a series of 230 (184 control and 46 metopic) dry crania of contemporary adult Bulgarian males were scanned using an industrial µCT system. The 3D coordinates of 47 landmarks were collected on the endocranial surface. All possible measurements between the landmarks were calculated as Euclidean distances. The resultant 1081 measurements represented the initial dataset, which was reduced to smaller datasets applying different criteria. The derived datasets were used for learning a set of classification models by machine learning algorithms. The morphometric analysis showed that in the metopic crania some segments of the anterior and middle cranial fossae were significantly longer, and the landmark endobregma was significantly closer to the anterior and middle sections of the cranial base. The most accurate model, with a classification accuracy of 85%, was the Naive Bayes one learned on a dataset of 69 attributes assembled after an attribute selection procedure.https://www.mdpi.com/2079-7737/14/7/835digital morphometryendocranial surfacemachine learningmetopic sutureµCT imaging |
| spellingShingle | Silviya Nikolova Diana Toneva Gennady Agre Endocranial Morphology in Metopism Biology digital morphometry endocranial surface machine learning metopic suture µCT imaging |
| title | Endocranial Morphology in Metopism |
| title_full | Endocranial Morphology in Metopism |
| title_fullStr | Endocranial Morphology in Metopism |
| title_full_unstemmed | Endocranial Morphology in Metopism |
| title_short | Endocranial Morphology in Metopism |
| title_sort | endocranial morphology in metopism |
| topic | digital morphometry endocranial surface machine learning metopic suture µCT imaging |
| url | https://www.mdpi.com/2079-7737/14/7/835 |
| work_keys_str_mv | AT silviyanikolova endocranialmorphologyinmetopism AT dianatoneva endocranialmorphologyinmetopism AT gennadyagre endocranialmorphologyinmetopism |