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 M.; Elshami, Wiam; Elhussein, Nagwan; Gareeballah, Awadia; Pathan, Refat Khan; Naseer, K.A.; Khandaker, Mayeen Uddin; Ahmed, Wegdan

doi:10.1016/j.jrras.2023.100570

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Using ML algorithms to analyze volumetric and liner data obtained from CBCT radiographs of various structures of the maxillofacial region is a new technique to estimate the sex. Hamad et al [45] reported that the sex could be predicted with high accuracy, reaching up to 98%, by studying the maxillary sinus morphometry with the aid of ML models. This result suggests that using ML models and CBCT radiographs is a promising approach to determining the sex.…”

Section: Discussionmentioning

confidence: 99%

The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms—A Retrospective Study

Baban

Mohammad

2023

Diagnostics

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In forensics, predicting the sex is a crucial step in identification. Many studies have aimed to find an accurate and fast technique to estimate the sex. This study was conducted to determine the accuracy of volumetric and linear measurements of three-dimensional (3D) images of the mandible obtained from cone beam computed tomography (CBCT) radiographs, using different machine-learning (ML) models for sex identification. The CBCTs of 104 males and 104 females were included in this study. The radiographs were converted to 3D images, and the volume, surface area, and ten linear measurements of the mandible were obtained. The data were evaluated using statistical analysis and five different ML algorithms. All results were considered statistically significant at p < 0.05, and the precision, recall, f1-score, training accuracy, and testing accuracy were used to evaluate the performance of the ML models. All the studied parameters showed statistically significant differences between sexes p < 0.05. The right coronoid-to-gonion linear distance had the highest discriminative power of all the parameters. Meanwhile, Gaussian Naive Bayes (GNB) showed the best performance among all the ML models. The results of this study revealed promising outcomes; the sex can be easily determined, with high accuracy (90%).

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Section: Discussionmentioning

confidence: 99%

The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms—A Retrospective Study

Baban

Mohammad

2023

Diagnostics

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“…Few authors have employed conventional machine learning techniques in the diagnosis of sinus-related conditions. Hamd et al [32] conducted a retrospective study focusing on predicting Maxillary Sinus Volume (MSV) using a machine learning (ML) algorithm based on data from 150 patients with normal maxillary sinuses. The study aimed to assess the predictability of the MSV using patient demographics (age, gender) and sinus length measurements in three directions.…”

Section: Conventional Techniquesmentioning

confidence: 99%

Transfer Learning-Based Classification of Maxillary Sinus Using Generative Adversarial Networks

Alhumaid,

Fayoumi

2024

Applied Sciences

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Paranasal sinus pathologies, particularly those affecting the maxillary sinuses, pose significant challenges in diagnosis and treatment due to the complex anatomical structures and diverse disease manifestations. The aim of this study is to investigate the use of deep learning techniques, particularly generative adversarial networks (GANs), in combination with convolutional neural networks (CNNs), for the classification of sinus pathologies in medical imaging data. The dataset is composed of images obtained through computed tomography (CT) scans, covering cases classified into “Moderate”, “Severe”, and “Normal” classes. The lightweight GAN is applied to augment a dataset by creating synthetic images, which are then used to train and test the ResNet-50 and ResNeXt-50 models. The model performance is optimized using random search to perform hyperparameter tuning, and the evaluation is conducted extensively for various metrics like accuracy, precision, recall, and the F1-score. The results demonstrate the effectiveness of the proposed approach in accurately classifying sinus pathologies, with the ResNeXt-50 model achieving superior performance with accuracy: 91.154, precision: 0.917, recall: 0.912, and F1-score: 0.913 compared to ResNet-50. This study highlights the potential of GAN-based data augmentation and deep learning techniques in enhancing the diagnosis of maxillary sinus diseases.

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Maxillary Sinus Disease Detection and Analysis Approaches in Deep Learning: Survey

Vimala,

Preethi

2024

2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS)

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

Cited by 3 publications

References 22 publications

The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms—A Retrospective Study

The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms—A Retrospective Study

Transfer Learning-Based Classification of Maxillary Sinus Using Generative Adversarial Networks

Maxillary Sinus Disease Detection and Analysis Approaches in Deep Learning: Survey

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