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 …

Background An important challenge of big data is using complex information networks to
provide useful clinical information. Recently, machine learning, and particularly deep …

Systematic review of deep learning models in ultrasound tongue imaging for the detection
of speech disorders Page 1 P osted on 28 Apr 2020 — CC-BY 4.0 — h ttps://doi.org/10.36227/techrxiv.22699291.v1 …

Objective To update the literature and provide a systematic review of image‐based artificial
intelligence (AI) applications in otolaryngology, highlight its advances, and propose future …

Objectives: To develop a multiclass-classifier deep learning model and website for
distinguishing tympanic membrane (TM) pathologies based on otoscopic images. Methods …

Objective Diagnosis of otosclerosis on temporal bone CT images is often difficult because
the imaging findings are frequently subtle. Our aim was to assess the utility of deep learning …

Objective To develop a deep convolutional neural network (DCNN) that can automatically
detect laryngeal cancer (LCA) in laryngoscopic images. Methods A DCNN-based diagnostic …

Objectives/Hypothesis There may be an interobserver variation in the diagnosis of laryngeal
disease based on laryngoscopic images according to clinical experience. Therefore, this …

Objective To provide a state of the art review of artificial intelligence (AI), including its
subfields of machine learning and natural language processing, as it applies to …

This systematic literature review aims to study the role and impact of artificial intelligence (AI)
in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse …