AIMC Topic: Rhinitis

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Medical data science in rhinology: Background and implications for clinicians.

American journal of otolaryngology
BACKGROUND: An important challenge of big data is using complex information networks to provide useful clinical information. Recently, machine learning, and particularly deep learning, has enabled rapid advances in clinical practice. The application ...

Prospective evaluation of clarithromycin in recurrent chronic rhinosinusitis with nasal polyps.

Brazilian journal of otorhinolaryngology
INTRODUCTION: The antiinflammatory effects of macrolides, especially clarithromycin, have been described in patients with chronic rhinosinusitis without polyps and also other chronic inflammatory airway diseases. There is no consensus in the literatu...

Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis.

The Annals of otology, rhinology, and laryngology
OBJECTIVES: This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional appr...

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...

A machine learning approach to predicting postoperative recurrence in pediatric chronic rhinosinusitis: identification of key metabolic biomarkers.

American journal of otolaryngology
BACKGROUND: Pediatric chronic rhinosinusitis (CRS) is a common chronic inflammatory disease with a high recurrence rate after surgery. This study aimed to construct and validate a machine learning-based predictive model to predict the risk of postope...

Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis.

European review for medical and pharmacological sciences
OBJECTIVE: Chronic rhinosinusitis (CRS) has traditionally been classified phenotypically according to the presence (CRSwNP) or absence (CRSsNP) of nasal polyps. However, the phenotypic dichotomy does not represent the complexity of the disease. Curre...

[Antifungal immunity in patients with chronic rhinosinusitis with nasal polyps].

Vestnik otorinolaringologii
OBJECTIVE: To evaluate the characteristics of antifungal immunity in patients with bilateral chronic rhinosinusitis with nasal polyps.

Clinical Validation and Extension of an Automated, Deep Learning-Based Algorithm for Quantitative Sinus CT Analysis.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Sinus CT is critically important for the diagnosis of chronic rhinosinusitis. While CT is sensitive for detecting mucosal disease, automated methods for objective quantification of sinus opacification are lacking. We describe ...