AIMC Topic: Turbinates

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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...
Humans Retrospective Studies Tomography, X-Ray Computed Male Deep Learning Surgery, Computer-Assisted Paranasal Sinuses Maxillary Sinus Turbinates Nasal Septum
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Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.

International forum of allergy & rhinology Jun 10, 2024
A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.
Humans Neural Networks, Computer Algorithms Endoscopy Anatomic Landmarks Nose Turbinates
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An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans.

The Journal of laryngology and otology Apr 1, 2020
OBJECTIVE: Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algori...
Humans Neural Networks, Computer Artificial Intelligence Algorithms Retrospective Studies Tomography, X-Ray Computed Female Male Australia Paranasal Sinuses Observer Variation Nose Diseases Turbinates
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