AIMC Topic: Maxillary Sinus

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Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring.

Tomography (Ann Arbor, Mich.)
BACKGROUND: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strong...

Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation.

PloS one
BACKGROUND: This study aims to develop artificial intelligence (AI) system to automatically classify patients with maxillary sinus fungal ball (MFB), chronic rhinosinusitis (CRS), and healthy controls (HCs).

A miniature robotic steerable endoscope for maxillary sinus surgery called PliENT.

Scientific reports
In endoscopic maxillary sinus surgery, the maxillary sinus is accessed through the nasal cavity which constitutes a narrow and tortuous pathway. However, surgeons still use rigid endoscopes and rigid, straight or pre-bent instruments for this procedu...

Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network.

Clinical oral investigations
OBJECTIVES: To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam compu...

Adaptive Localizing Region-Based Level Set for Segmentation of Maxillary Sinus Based on Convolutional Neural Networks.

Computational intelligence and neuroscience
In this paper, we propose a novel method, an adaptive localizing region-based level set using convolutional neural network, for improving performance of maxillary sinus segmentation. The healthy sinus without lesion inside is easy for conventional al...

Development and validation of a two-segment continuum robot for maxillary sinus surgery.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Existing rigid instruments have difficulties in backward inspection and operation. Moreover, the pathway to the maxillary sinus is curved and narrow, resulting in complex and repetitive manual operations. There is a necessity to develop a...

Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net.

International journal of computer assisted radiology and surgery
PURPOSE: The analysis of the maxillary sinus (MS) can provide an assessment for many clinical diagnoses, so accurate CT image segmentation of the MS is essential. However, common segmentation methods are mainly done by experienced doctors manually, a...

Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs.

Dento maxillo facial radiology
OBJECTIVE: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification ...

Performance of artificial intelligence in evaluating maxillary sinus mucosal alterations in imaging examinations: systematic review.

Dento maxillo facial radiology
OBJECTIVES: This systematic review aimed to assess the performance of artificial intelligence (AI) in the evaluation of maxillary sinus mucosal alterations in imaging examinations compared to human analysis.

Accuracy of a Cascade Network for Semi-Supervised Maxillary Sinus Detection and Sinus Cyst Classification.

Clinical implant dentistry and related research
OBJECTIVE: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipelin...