AIMC Topic: Paranasal Sinuses

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Endonasal endoscopic transsphenoidal approach robot prototype: A cadaveric trial.

Asian journal of surgery
BACKGROUND: The Endonasal Endoscopic Transsphenoidal Surgery (EETS) is a minimally invasive procedure to approach and remove pituitary tumors and other sellar lesions. The process causes less pain, faster recovery, and provides further minimal invasi...

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans.

The Journal of laryngology and otology
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...

Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks.

International forum of allergy & rhinology
BACKGROUND: Convolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real-world applications including handw...

Effectiveness of Artificial Intelligence in detecting sinonasal pathology using clinical imaging modalities: a systematic review.

Rhinology
BACKGROUND: Sinonasal pathology can be complex and requires a systematic and meticulous approach. Artificial Intelligence (AI) has the potential to improve diagnostic accuracy and efficiency in sinonasal imaging, but its clinical applicability remain...

Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images.

Journal of X-ray science and technology
In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient's condition is crucial in modern medicine since it determines whether or not the patie...

Prediction of age and sex from paranasal sinus images using a deep learning network.

Medicine
This study was conducted to develop a convolutional neural network (CNN)-based model to predict the sex and age of patients by identifying unique unknown features from paranasal sinus (PNS) X-ray images.We employed a retrospective study design and us...