Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study
The maxillary sinus is the most prominent in humans. Maxillary Sinus Volume (MSV) has grown in popularity as a tool to predict the success of various dental procedures and oral surgeries and determine a person's gender in cases such as forensic investigations when only partial skulls are availa...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023
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| Subjects: | |
| Online Access: | http://eprints.sunway.edu.my/2281/ http://eprints.sunway.edu.my/2281/1/71.pdf |
| _version_ | 1848802246703185920 |
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| author | Hamd, Zuhal Y. Aljuaid, Hanan Alorainy, Amal I. Osman, Eyas G. Abuzaid, Mohamed Elshami, Wiam Elhussein, Nagwan Gareeballah, Awadia Pathan, Refat Khan Naseer, K.A. Khandaker, Mayeen Uddin * Ahmed, Wegdan |
| author_facet | Hamd, Zuhal Y. Aljuaid, Hanan Alorainy, Amal I. Osman, Eyas G. Abuzaid, Mohamed Elshami, Wiam Elhussein, Nagwan Gareeballah, Awadia Pathan, Refat Khan Naseer, K.A. Khandaker, Mayeen Uddin * Ahmed, Wegdan |
| author_sort | Hamd, Zuhal Y. |
| building | SU Institutional Repository |
| collection | Online Access |
| description | The maxillary sinus is the most prominent in humans. Maxillary Sinus Volume (MSV) has grown in popularity as a tool to predict the success of various dental procedures and oral surgeries and determine a person's gender in cases such as forensic investigations when only partial skulls are available. Because it is an irregularly shaped cavity that may be difficult to measure manually, robust imaging techniques such as cone-beam computed tomography (CBCT) used in conjunction with machine learning (ML) algorithms may offer quick and vigorous ways to make accurate predictions using sinus data. In this retrospective study, we used data from 150 patients with normal maxillary sinuses to train and evaluate a Python ML algorithm for its ability to predict MSV from basic patient demographics (age, gender) and sinus length measurements in three directions (anteroposterior, mediolateral, and superoinferior). The model found sinus length measurements had significantly higher predictive values than either age or gender and could predict MSVs from these length measurements with almost linear accuracy indicated by R-squared values ranging from 0.97 to 0.98% for the right and left sinuses. |
| first_indexed | 2025-11-14T21:20:18Z |
| format | Article |
| id | sunway-2281 |
| institution | Sunway University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:20:18Z |
| publishDate | 2023 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | sunway-22812023-06-17T13:18:19Z http://eprints.sunway.edu.my/2281/ Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study Hamd, Zuhal Y. Aljuaid, Hanan Alorainy, Amal I. Osman, Eyas G. Abuzaid, Mohamed Elshami, Wiam Elhussein, Nagwan Gareeballah, Awadia Pathan, Refat Khan Naseer, K.A. Khandaker, Mayeen Uddin * Ahmed, Wegdan Q Science (General) QP Physiology RF Otorhinolaryngology The maxillary sinus is the most prominent in humans. Maxillary Sinus Volume (MSV) has grown in popularity as a tool to predict the success of various dental procedures and oral surgeries and determine a person's gender in cases such as forensic investigations when only partial skulls are available. Because it is an irregularly shaped cavity that may be difficult to measure manually, robust imaging techniques such as cone-beam computed tomography (CBCT) used in conjunction with machine learning (ML) algorithms may offer quick and vigorous ways to make accurate predictions using sinus data. In this retrospective study, we used data from 150 patients with normal maxillary sinuses to train and evaluate a Python ML algorithm for its ability to predict MSV from basic patient demographics (age, gender) and sinus length measurements in three directions (anteroposterior, mediolateral, and superoinferior). The model found sinus length measurements had significantly higher predictive values than either age or gender and could predict MSVs from these length measurements with almost linear accuracy indicated by R-squared values ranging from 0.97 to 0.98% for the right and left sinuses. Elsevier 2023-06 Article PeerReviewed text en cc_by_nc_nd_4 http://eprints.sunway.edu.my/2281/1/71.pdf Hamd, Zuhal Y. and Aljuaid, Hanan and Alorainy, Amal I. and Osman, Eyas G. and Abuzaid, Mohamed and Elshami, Wiam and Elhussein, Nagwan and Gareeballah, Awadia and Pathan, Refat Khan and Naseer, K.A. and Khandaker, Mayeen Uddin * and Ahmed, Wegdan (2023) Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study. Journal of Radiation Research and Applied Sciences, 16 (2). ISSN 1687-8507 https://doi.org/10.1016/j.jrras.2023.100570 10.1016/j.jrras.2023.100570 |
| spellingShingle | Q Science (General) QP Physiology RF Otorhinolaryngology Hamd, Zuhal Y. Aljuaid, Hanan Alorainy, Amal I. Osman, Eyas G. Abuzaid, Mohamed Elshami, Wiam Elhussein, Nagwan Gareeballah, Awadia Pathan, Refat Khan Naseer, K.A. Khandaker, Mayeen Uddin * Ahmed, Wegdan Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| title | Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| title_full | Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| title_fullStr | Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| title_full_unstemmed | Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| title_short | Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| title_sort | machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study |
| topic | Q Science (General) QP Physiology RF Otorhinolaryngology |
| url | http://eprints.sunway.edu.my/2281/ http://eprints.sunway.edu.my/2281/ http://eprints.sunway.edu.my/2281/ http://eprints.sunway.edu.my/2281/1/71.pdf |