AI Medical Compendium Journal:
International forum of allergy & rhinology

Showing 1 to 10 of 17 articles

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.

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 ...

Real-time augmentation of diagnostic nasal endoscopy video using AI-enabled edge computing.

International forum of allergy & rhinology
AI-enabled augmentation of nasal endoscopy video images is feasible in the clinical setting. Edge computing hardware can interface with existing nasal endoscopy equipment. Real-time AI performance can achieve an acceptable balance of accuracy and eff...

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...

Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis.

International forum of allergy & rhinology
Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP-SC...

Gender-based linguistic differences in letters of recommendation for rhinology fellowship over time: A dual-institutional follow-up study using natural language processing and deep learning.

International forum of allergy & rhinology
This follow-up dual-institutional and longitudinal study further evaluated for underlying gender biases in LORs for rhinology fellowship. Explicit and implicit linguistic gender bias was found, heavily favoring male applicants.

Assessing the quality of artificial intelligence-generated patient counseling for rhinosinusitis.

International forum of allergy & rhinology
GPT-4 generated moderate quality information in response to questions regarding sinusitis and surgery. GPT-4 generated significantly higher quality responses to questions regarding treatment of sinusitis. Future studies exploring quality of GPT respo...

Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.

International forum of allergy & rhinology
A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.

Endoscopic surgical field clarity index: An artificial intelligence-based measure of transnasal endoscopic surgical field quality.

International forum of allergy & rhinology
Clear visualization during transnasal endoscopic surgery (TNES) is crucial for safe, efficient surgery. The endoscopic surgical field clarity index (ESFCI) is an artificial intelligence-enabled measure of surgical field quality. The ESFCI allows rese...