AIMC Topic: Nasal Polyps

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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...

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...

Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.

International forum of allergy & rhinology
BACKGROUND: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.

Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing b...

Quantitative characterization of eosinophilia in nasal polyps with AI-based single cell classification.

International forum of allergy & rhinology
Eosinophilic granulocytes have characteristic morphological features. This makes them prime candidates for utilization of a single cell binary classification network. Single cell binary classification networks can reliably help quantify eosinophils i...

Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVE: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.

Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based i...

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

BMC medical informatics and decision making
BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis...

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

BMC medical imaging
Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment...

Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps.

BMC medical imaging
BACKGROUND: As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to c...