AIMC Topic: Sinusitis

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Photocatalytic microrobots for treating bacterial infections deep within sinuses.

Science robotics
Microrobotic techniques are promising for treating biofilm infections located deep within the human body. However, the presence of highly viscous pus presents a formidable biological barrier, severely restricting targeted and minimally invasive treat...

Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps.

Scientific reports
Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomodulation plays a pivotal role. However, immuno-transcriptomic characteristics and its clinical relevance remains largely known. We analyzed transcript...

The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.

Biomedical engineering online
BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-...

Multi-omics integration and machine learning identify and validate neutrophil extracellular trap-associated gene signatures in chronic rhinosinusitis with nasal polyps.

Clinical immunology (Orlando, Fla.)
This study aimed to explore the molecular characteristics of neutrophil extracellular traps (NETs) in chronic rhinosinusitis with nasal polyps (CRSwNP). Differentially expressed gene analysis, weighted gene co-expression network analysis, and machine...

Using Machine Learning Models to Diagnose Chronic Rhinosinusitis: Analysis of Pre-Treatment Patient-Generated Health Data to Predict Cardinal Symptoms and Sinonasal Inflammation.

American journal of rhinology & allergy
BackgroundThe diagnosis of chronic rhinosinusitis (CRS) relies upon patient-reported symptoms and objective nasal endoscopy and/or computed tomography (CT) findings. Many patients, at the time of evaluation by an otolaryngologist or rhinologist, lack...

Factors associated with the development of severe asthma: A nationwide study (FINASTHMA).

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology
BACKGROUND: Severe asthma presents a major challenge to health care and negatively affects the quality of life of patients. Understanding the factors predicting the development of severe asthma is limited.

Deep Learning-Derived Quantitative Scores for Chronic Rhinosinusitis Assessment: Correlation With Quality of Life Outcomes.

American journal of rhinology & allergy
BackgroundComputed tomography (CT) plays a crucial role in assessing chronic rhinosinusitis, but lacks objective quantifiable indicators.ObjectiveThis study aimed to use deep learning for automated sinus segmentation to generate distinct quantitative...

Evaluation of the Usability of ChatGPT-4 and Google Gemini in Patient Education About Rhinosinusitis.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
INTRODUCTION: Artificial intelligence (AI) based chat robots are increasingly used by users for patient education about common diseases in the health field, as in every field. This study aims to evaluate and compare patient education materials on rhi...

Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).

A preliminary review of the utility of artificial intelligence to detect eosinophilic chronic rhinosinusitis.

International forum of allergy & rhinology
While typically diagnosed with biopsy, ECRS may be predicted preoperatively with the use of AI. Various AI models have been used, with pooled sensitivity of 0.857 and specificity of 0.850. We found no statistically significant difference between the ...