Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study

Hamd Zuhal Y.; Aljuaid Hanan; Alorainy Amal. IOsman Eyas G.Abuzaid MohamedElshami WiamElhussein NagwanGareeballah AwadiaPathan Refat KhanNaseer K. A.Khandaker Mayeen UddinAhmed Wegdan

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Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study

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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 to-mography (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 pre-dictive 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.

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《journal of radiation research and applied sciences》 |2023年第2期|共7页
作者
Hamd Zuhal Y.; Aljuaid Hanan; Alorainy Amal. IOsman Eyas G.Abuzaid MohamedElshami WiamElhussein NagwanGareeballah AwadiaPathan Refat KhanNaseer K. A.Khandaker Mayeen UddinAhmed Wegdan;
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作者单位

Princess Nourah Bint Abdulrahman Univ PNU;

Shaqra Univ;

Univ SharjahUniv HailTaibah UnivSunway UnivFarook Coll AutonomousSun Way UnivDaffodil Int UnivAlzaiem Alazhari UnivNatl Univ;

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正文语种英语
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Sexual dimorphism; Maxillary sinus; Linear regression modelling; Machine learning; COMPUTED-TOMOGRAPHY; GENDER;

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Machine learning as new approach for predicting of maxillary sinus volume, a sexual dimorphic study

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