Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

Purpose: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging. Materials and Methods: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as “DCNN,” “deep learning,” “convolutional neural network,” “machine learning,” “predictive modeling,” and “data mining” were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy. Results: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivityof 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91. Conclusion AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

PURPOSE: To determine the prevalence of concha bullosa (CB) and nasal septal deviation (NSD) and their impact on maxillary sinus volume (MSV). MATERIALS AND METHODS: Cone-beam computed tomographic (CBCT) images of 106 Emirati people were used in this study. The direction and angle of septal deviation were calculated. The presence of CB, which could be unilateral, contralateral, or bilateral in relation to the direction of NSD, was also recorded. MSV was measured using reconstructed Digital Imaging and Communication in Medicine images on Dolphin 3D imaging software version 11.8 premium (Dolphin Imaging, Chatsworth, CA, USA). P values<0.05 were considered to indicate statistical significance. RESULTS: CB was detected in 37.7% of the sample; 20.7% of the sample showed single unilateral CB and 16.6% had single bilateral CB. NSD was seen in 74.5% of the sample. In the participants with CB, 45.5% showed mild deviation, 34.4% showed moderate deviation, and only 12.5% showed severe septal deviation. CB, but not NSD, was associated with significantly higher MSV on the affected side (P=0.001). CONCLUSION: Although NSD was observed in more than two-thirds of the sample and CB was present in more than one-third of the sample, only CB had a significant impact on MSV.
Cone-Beam Computed Tomography ; Dolphins ; Maxillary Sinus ; Nasal Septum ; Prevalence ; Turbinates