Abstract Machine learning models have been increasingly considered to model head and
neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of …

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the
widespread application of head and neck MRI in clinical practice serves to assess various …

Objectives Advances in artificial intelligence (AI) technology have increased the feasibility of
classifying voice disorders using voice recordings as a screening tool. This work develops …

Objective Advances in deep learning and artificial intelligence (AI) have led to the
emergence of large language models (LLM) like ChatGPT from OpenAI. The study aimed to …

Background A considerable volume of possible applications of artificial intelligence (AI) in
the field of rhinology exists, and research in the area is rapidly evolving. Objective This …

Purpose Paranasal anomalies are commonly discovered during routine radiological
screenings and can present with a wide range of morphological features. This diversity can …

Objective To provide a comprehensive overview on the applications of artificial intelligence
(AI) in rhinology, highlight its limitations, and propose strategies for its integration into …

Using deep learning techniques, anomalies in the paranasal sinus system can be detected
automatically in MRI images and can be further analyzed and classified based on their …

Objectives Our research aims to construct machine learning prediction models to identify
patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up …

The nasal cavity is characterized by protrusions and involutions, which can pose substantial
challenges to the interpretation of the endoscopic examination. 1 The inferior turbinate (IT) …