AIMC Topic: Maxillary Sinus

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Development of a machine learning-based predictive model for maxillary sinus cysts and exploration of clustering patterns.

Head & face medicine Mar 12, 2025
BACKGROUND AND OBJECTIVE: There are still many controversies about the factors influencing maxillary sinus cysts and their clinical management. This study aims to construct a prediction model of maxillary sinus cyst and explore its clustering pattern...
Humans Machine Learning Young Adult Adult Male Female Risk Factors Aged Maxillary Sinus Paranasal Sinus Diseases Cone-Beam Computed Tomography Cluster Analysis Cysts Middle Aged Adolescent
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Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.

Scientific reports Mar 10, 2025
Accessory ostium [AO] is one of the important anatomical variations in the maxillary sinus. AO is often associated with sinus pathology. Radiographic imaging plays a very important role in the detection of AO. Deep learning models have been used in m...
Maxillary Sinus Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans
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Automatic Segmentation of Bone Graft in Maxillary Sinus via Distance Constrained Network Guided by Prior Anatomical Knowledge.

IEEE journal of biomedical and health informatics Mar 6, 2025
Maxillary Sinus Lifting is a crucial surgical procedure for addressing insufficient alveolar bone mass andsevere resorption in dental implant therapy. To accurately analyze the geometry changesof the bone graft (BG) in the maxillary sinus (MS), it is...
Deep Learning Image Processing, Computer-Assisted Artifacts Humans Maxillary Sinus Maxilla Alveolar Ridge Augmentation Datasets as Topic Postoperative Period Preoperative Period
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A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.

Clinical oral investigations Jan 25, 2025
OBJECTIVES: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Maxillary Sinus Cone-Beam Computed Tomography Imaging, Three-Dimensional Neural Networks, Computer Algorithms Female Reproducibility of Results Adult Male Humans
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Application of task-autonomous dental robot for lateral window preparation in the maxillary sinus.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery Dec 6, 2024
The study aimed to investigate the feasibility and accuracy of task-autonomous dental robots for lateral window preparation in the maxillary sinus, as well as to propose 3 indexes for assessing the accuracy of lateral window preparation. Twenty 1:1 r...
Feasibility Studies Maxillary Sinus Robotics Robotic Surgical Procedures Humans Cone-Beam Computed Tomography
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The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery Sep 4, 2024
PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.
Humans Tomography, X-Ray Computed Female Adult Sensitivity and Specificity Male Chronic Disease ROC Curve Middle Aged Maxillary Sinus Rhinosinusitis Sinusitis Deep Learning Mycoses Rhinitis
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Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery Jul 31, 2024
BACKGROUND: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this stud...
Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted Deep Learning Maxillary Sinus
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A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Jun 23, 2024
OBJECTIVE: Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation metho...
Surgery, Computer-Assisted Tomography, X-Ray Computed Humans Maxillary Sinus Paranasal Sinuses Male Turbinates Retrospective Studies Deep Learning Nasal Septum
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Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review.

Clinical implant dentistry and related research Jun 11, 2024
Cone-beam computed tomography (CBCT) imaging of the maxillary sinus is indispensable for implantologists, offering three-dimensional anatomical visualization, morphological variation detection, and abnormality identification, all critical for diagnos...
Cone-Beam Computed Tomography Imaging, Three-Dimensional Maxillary Sinus Humans Artificial Intelligence
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Self-supervised learning for classifying paranasal anomalies in the maxillary sinus.

International journal of computer assisted radiology and surgery Jun 8, 2024
PURPOSE: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse an...
Imaging, Three-Dimensional Humans Neural Networks, Computer Supervised Machine Learning Tomography, X-Ray Computed Maxillary Sinus
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